From 4d020ba4391b1db29e578b8460e38f34a7c83424 Mon Sep 17 00:00:00 2001
From: Andreas Troxler <andreas_troxler@gmx.ch>
Date: Sat, 7 Oct 2023 11:20:44 +0200
Subject: [PATCH] update library versions, add notebook 3

---
 ...Actuarial_Applications_of_NLP_Part_1.ipynb | 74949 ++++++----------
 ...Actuarial_Applications_of_NLP_Part_2.ipynb | 74325 +++++----------
 ...Actuarial_Applications_of_NLP_Part_3.ipynb |  1896 +
 3 files changed, 50600 insertions(+), 100570 deletions(-)
 create mode 100644 12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_3.ipynb

diff --git a/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_1.ipynb b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_1.ipynb
index ed3ec21..bb7f761 100644
--- a/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_1.ipynb	
+++ b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_1.ipynb	
@@ -1,49132 +1,26841 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OnFQmduQWNvE"
-   },
-   "source": [
-    "# Actuarial Applications of Natural Language Processing Using Transformers\n",
-    "### A Case Study for Processing Text Features in an Actuarial Context\n",
-    "### Part I – Introduction and Case Studies on Car Accident Descriptions\n",
-    "\n",
-    "By Andreas Troxler, June 2022\n",
-    "\n",
-    "An abundant amount of information is available to insurance companies in the form of text.\n",
-    "However, language data is unstructured, sometimes multilingual,\n",
-    "and single words or phrases taken out of context can be highly ambiguous.\n",
-    "By the help of transformer models, text data can be converted into structured data and then\n",
-    "used as input to predictive models.\n",
-    "\n",
-    "In this Part I of tutorial, you will discover the use of transformer models for text classification.\n",
-    "Throughout this tutorial, the [HuggingFace](https://huggingface.co/docs/transformers/index)\n",
-    "Transformers library will be used.\n",
-    "\n",
-    "This notebook serves as a companion to the tutorial\n",
-    "[\"Actuarial Applications of Natural Language Processing Using Transformers”](https://github.com/JSchelldorfer/ActuarialDataScience/tree/master/12%20-%20NLP%20Using%20Transformers).\n",
-    "The tutorial explains the underlying concepts, and this notebook illustrates the implementation.\n",
-    "This tutorial, the dataset and the notebooks are available on [github](https://github.com/JSchelldorfer/ActuarialDataScience/tree/master/12%20-%20NLP%20Using%20Transformers).\n",
-    "\n",
-    "After competing this tutorial, you will know:\n",
-    "* How to use a transformer model to convert multi-lingual text features into embeddings - simply put, into a vector of real numbers.\n",
-    "* How to use this structured data to perform a text classification task.\n",
-    "* How to improve model performance by fine-tuning the NLP model with your own data.\n",
-    "* How to perform error analysis and interpret model predictions.\n",
-    "* How to deal with long input sequences.\n",
-    "\n",
-    "Let’s get started.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TDCiMy_fWNvK"
-   },
-   "source": [
-    "## Notebook Overview\n",
-    "\n",
-    "This notebook is divided into into seven parts; they are:\n",
-    "\n",
-    "1. [Introduction](#intro)\n",
-    "\n",
-    "   1.1 [Prerequisites](#prerequisites)\n",
-    "\n",
-    "   [1.2 Exploring the data](#dataexploration)<br><br>\n",
-    "\n",
-    "2. [A brief introduction to the HuggingFace ecosystem](#huggingface)\n",
-    "\n",
-    "   2.1 [Loading the data into a DataSet](#dataset)\n",
-    "\n",
-    "   2.2 [Tokenization – splitting the raw text](#tokenize)\n",
-    "\n",
-    "   2.3 [The transformer model](#transformer)<br><br>\n",
-    "\n",
-    "3. [Using transformers to extract features for classification or regression tasks](#feature_extraction)\n",
-    "\n",
-    "   3.1 [Extracting the encoded text ...](#extract_encoding)\n",
-    "\n",
-    "   3.2  [... and using it in a classification model](#classification)\n",
-    "\n",
-    "   3.3 [Case study: use accident descriptions to predict the number of vehicles involved](#case_study_nvehicles)\n",
-    "\n",
-    "   3.4 [Cross-lingual transfer](#cross_lingual_transfer)\n",
-    "\n",
-    "   3.5 [Multi-lingual training](#multi_lingual_training)<br><br>\n",
-    "\n",
-    "4. [Fine-tuning – improving the model](#finetuning)\n",
-    "\n",
-    "   4.1. [Domain-specific finetuning](#domain_finetuning)\n",
-    "\n",
-    "   4.2. [Task-specific finetuning](#task_finetuning)<br><br>\n",
-    "\n",
-    "5. [Understand predictions errors and interpret predictions](#understand)\n",
-    "\n",
-    "   5.1. [Case study: use accident descriptions to identify bodily injury](#case_study_injuries)\n",
-    "\n",
-    "   5.2. [Investigate false positives and false negatives](#investigate)\n",
-    "\n",
-    "   5.3. [Use Captum and `transformers-interpret` to interpret predictions](#interpret)<br><br>\n",
-    "\n",
-    "6. [Using extractive question answering to process longer texts](#qna)<br><br>\n",
-    "\n",
-    "7. [Conclusion](#conclusion)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dnU_Y0gcWNvM"
-   },
-   "source": [
-    "<a id='intro'></a>\n",
-    "<a name='intro'></a>\n",
-    "## 1.&nbsp;Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "bTFN7kK4WNvN"
-   },
-   "source": [
-    "<a id='prerequisites'></a>\n",
-    "<a name='prerequisites'></a>\n",
-    "### 1.1. Prerequisites"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Z2xX3ICs0AAo"
-   },
-   "source": [
-    "#### Computing Power\n",
-    "This notebook is computationally intensive. We recommend using a platform with GPU support.\n",
-    "\n",
-    "We have run this notebook on Google Colab and on an Amazon EC2 p2.xlarge instance (an older generation of GPU-based instances).\n",
-    "\n",
-    "Please note that the results may not be reproducible across platforms and versions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qc3FaMEFDWwh"
-   },
-   "source": [
-    "#### Local files\n",
-    "Make sure the following files are available in the directory of the notebook:\n",
-    "* `tutorial_utils.py` - a collection of utility functions used throughout this notebook, explained in Section [3.2](#classification)\n",
-    "* `NHTSA_NMVCCS_extract.parquet.gzip` - the data\n",
-    "\n",
-    "This notebook will create the following subdirectories:\n",
-    "* `datasets` - pre-processed datasets\n",
-    "* `models` - trained Transformer models\n",
-    "* `results` - figures and Excel files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uLmmA-fQWNvN"
-   },
-   "source": [
-    "#### Getting started with Python and Jupyter Notebook\n",
-    "\n",
-    "For this tutorial, we assume that you are already familiar with Python and Jupyter Notebook.\n",
-    "\n",
-    "In this section, Jupyter Notebook and Python settings are initialized.\n",
-    "For code in Python, the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/)\n",
-    "(\"PEP = Python Enhancement Proposal\") is enforced with minor variations to improve readability.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 17
-    },
-    "id": "E7grvuz8WNvQ",
-    "outputId": "cfe1efde-b6c7-4df9-f744-0b611fae3399",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/html": [
-       "<style>.container { width:100% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OnFQmduQWNvE"
+      },
+      "source": [
+        "# Actuarial Applications of Natural Language Processing Using Transformers\n",
+        "### A Case Study for Processing Text Features in an Actuarial Context\n",
+        "### Part I – Introduction and Case Studies on Car Accident Descriptions\n",
+        "\n",
+        "By Andreas Troxler, June 2022\n",
+        "\n",
+        "An abundant amount of information is available to insurance companies in the form of text.\n",
+        "However, language data is unstructured, sometimes multilingual,\n",
+        "and single words or phrases taken out of context can be highly ambiguous.\n",
+        "By the help of transformer models, text data can be converted into structured data and then\n",
+        "used as input to predictive models.\n",
+        "\n",
+        "In this Part I of tutorial, you will discover the use of transformer models for text classification.\n",
+        "Throughout this tutorial, the [HuggingFace](https://huggingface.co/docs/transformers/index)\n",
+        "Transformers library will be used.\n",
+        "\n",
+        "This notebook serves as a companion to the tutorial\n",
+        "[\"Actuarial Applications of Natural Language Processing Using Transformers”](https://github.com/JSchelldorfer/ActuarialDataScience/tree/master/12%20-%20NLP%20Using%20Transformers).\n",
+        "The tutorial explains the underlying concepts, and this notebook illustrates the implementation.\n",
+        "This tutorial, the dataset and the notebooks are available on [github](https://github.com/JSchelldorfer/ActuarialDataScience/tree/master/12%20-%20NLP%20Using%20Transformers).\n",
+        "\n",
+        "After competing this tutorial, you will know:\n",
+        "* How to use a transformer model to convert multi-lingual text features into embeddings - simply put, into a vector of real numbers.\n",
+        "* How to use this structured data to perform a text classification task.\n",
+        "* How to improve model performance by fine-tuning the NLP model with your own data.\n",
+        "* How to perform error analysis and interpret model predictions.\n",
+        "* How to deal with long input sequences.\n",
+        "\n",
+        "Let’s get started.\n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Notebook settings\n",
-    "\n",
-    "# clear the namespace variables\n",
-    "from IPython import get_ipython\n",
-    "get_ipython().run_line_magic(\"reset\", \"-sf\")\n",
-    "\n",
-    "# formatting: cell width\n",
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FGxBx9MVWNvS"
-   },
-   "source": [
-    "#### Importing Required Libraries\n",
-    "\n",
-    "The following libraries are required:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "PFZRb_nWZnG5",
-    "outputId": "2c9bcb1d-0ebb-4413-b06e-584bbef0a709"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "HDmc_CXBZmQz",
-    "outputId": "a028a40c-4a64-4aab-eeda-64661d82d935"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "fap9tsxwa5QR",
-    "outputId": "e742c528-71ea-444d-f7a1-0a193623221c"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers_interpret"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "PzHnOM3JZq7G",
-    "outputId": "e119834a-fd1f-4c5a-9e0d-60b5e65dfc6f"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install plotly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "cFmU2CO5Zxe-",
-    "outputId": "5e26145d-0c87-450f-e257-cda799e9f895"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install kaleido"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TDCiMy_fWNvK"
+      },
+      "source": [
+        "## Notebook Overview\n",
+        "\n",
+        "This notebook is divided into into seven parts; they are:\n",
+        "\n",
+        "1. [Introduction](#intro)\n",
+        "\n",
+        "   1.1 [Prerequisites](#prerequisites)\n",
+        "\n",
+        "   [1.2 Exploring the data](#dataexploration)<br><br>\n",
+        "\n",
+        "2. [A brief introduction to the HuggingFace ecosystem](#huggingface)\n",
+        "\n",
+        "   2.1 [Loading the data into a DataSet](#dataset)\n",
+        "\n",
+        "   2.2 [Tokenization – splitting the raw text](#tokenize)\n",
+        "\n",
+        "   2.3 [The transformer model](#transformer)<br><br>\n",
+        "\n",
+        "3. [Using transformers to extract features for classification or regression tasks](#feature_extraction)\n",
+        "\n",
+        "   3.1 [Extracting the encoded text ...](#extract_encoding)\n",
+        "\n",
+        "   3.2  [... and using it in a classification model](#classification)\n",
+        "\n",
+        "   3.3 [Case study: use accident descriptions to predict the number of vehicles involved](#case_study_nvehicles)\n",
+        "\n",
+        "   3.4 [Cross-lingual transfer](#cross_lingual_transfer)\n",
+        "\n",
+        "   3.5 [Multi-lingual training](#multi_lingual_training)<br><br>\n",
+        "\n",
+        "4. [Fine-tuning – improving the model](#finetuning)\n",
+        "\n",
+        "   4.1. [Domain-specific finetuning](#domain_finetuning)\n",
+        "\n",
+        "   4.2. [Task-specific finetuning](#task_finetuning)<br><br>\n",
+        "\n",
+        "5. [Understand predictions errors and interpret predictions](#understand)\n",
+        "\n",
+        "   5.1. [Case study: use accident descriptions to identify bodily injury](#case_study_injuries)\n",
+        "\n",
+        "   5.2. [Investigate false positives and false negatives](#investigate)\n",
+        "\n",
+        "   5.3. [Use Captum and `transformers-interpret` to interpret predictions](#interpret)<br><br>\n",
+        "\n",
+        "6. [Using extractive question answering to process longer texts](#qna)<br><br>\n",
+        "\n",
+        "7. [Conclusion](#conclusion)\n"
+      ]
     },
-    "id": "JgoxGJH-bW84",
-    "outputId": "778bad51-692b-491d-8376-13f5fe16477e"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install pyyaml==5.4.1  ## https://github.com/yaml/pyyaml/issues/576"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dnU_Y0gcWNvM"
+      },
+      "source": [
+        "<a id='intro'></a>\n",
+        "<a name='intro'></a>\n",
+        "## 1.&nbsp;Introduction"
+      ]
     },
-    "id": "_z92uLSKWNvS",
-    "outputId": "83969d4b-83c3-4640-bbe6-a2604472a8de",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import Dataset, DatasetDict, load_from_disk\n",
-    "from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, trainer_utils, AutoModelForMaskedLM,\\\n",
-    "    DataCollatorForLanguageModeling, AutoModelForSequenceClassification, pipeline\n",
-    "from transformers_interpret import SequenceClassificationExplainer\n",
-    "import torch\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy.special import softmax\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.dummy import DummyClassifier\n",
-    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score\n",
-    "import plotly.express as px\n",
-    "from wordcloud import WordCloud\n",
-    "\n",
-    "from tutorial_utils import extract_sequence_encoding, get_xy, dummy_classifier, logistic_regression_classifier, evaluate_classifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ut5VN51xWNvT"
-   },
-   "source": [
-    "In addition, we require `openpyxl` to enable export from Pandas to Excel."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "u8MaU-VkWNvU"
-   },
-   "source": [
-    "<a id='dataexploration'></a>\n",
-    "<a name='dataexploration'></a>\n",
-    "### 1.2. Exploring the Data\n",
-    "\n",
-    "The data used throughout this tutorial is derived from data of a vehicle crash causation study performed\n",
-    "in the United States from 2005 to 2007.\n",
-    "The dataset has almost 7'000 records, each relating to one accident.\n",
-    "For each case, a verbal description of the accident is available in English,\n",
-    "which summarizes road and weather conditions,\n",
-    "vehicles, drivers and passengers involved, preconditions, injury severities, etc.\n",
-    "The same information is also encoded in tabular form,\n",
-    "so that we can apply supervised learning techniques to train the NLP models and\n",
-    "compare the information extracted from the verbal descriptions with the encoded data.\n",
-    "\n",
-    "The original data consists of multiple tables. For this tutorial, we have aggregated it into a single dataset\n",
-    "and added German translations of the English accident descriptions.\n",
-    "The translations were generated using the new\n",
-    "[DeepL python API](https://pypi.org/project/deepl/).\n",
-    "\n",
-    "To explore the data, let's load it into a Pandas DataFrame and examine its shape, columns and data types:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bTFN7kK4WNvN"
+      },
+      "source": [
+        "<a id='prerequisites'></a>\n",
+        "<a name='prerequisites'></a>\n",
+        "### 1.1. Prerequisites"
+      ]
     },
-    "id": "RJDyLey0WNvU",
-    "outputId": "886a4e98-eba3-4c8c-bf9f-26212dcea1e5",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "shape of DataFrame: (6949, 16)\n",
-      "('level_0', dtype('int64'))\n",
-      "('index', dtype('int64'))\n",
-      "('SCASEID', dtype('int64'))\n",
-      "('SUMMARY_EN', dtype('O'))\n",
-      "('SUMMARY_GE', dtype('O'))\n",
-      "('INJSEVA', dtype('int64'))\n",
-      "('NUMTOTV', dtype('int64'))\n",
-      "('WEATHER1', dtype('int64'))\n",
-      "('WEATHER2', dtype('int64'))\n",
-      "('WEATHER3', dtype('int64'))\n",
-      "('WEATHER4', dtype('int64'))\n",
-      "('WEATHER5', dtype('int64'))\n",
-      "('WEATHER6', dtype('int64'))\n",
-      "('WEATHER7', dtype('int64'))\n",
-      "('WEATHER8', dtype('int64'))\n",
-      "('INJSEVB', dtype('int64'))\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.read_parquet(\"NHTSA_NMVCCS_extract.parquet.gzip\")\n",
-    "print(f\"shape of DataFrame: {df.shape}\")\n",
-    "print(*list(zip(df.columns, df.dtypes)), sep=\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "k2Ee4mjtWNvW"
-   },
-   "source": [
-    "The column `SCASEID` is a unique case identifier.\n",
-    "\n",
-    "The columns `SUMMARY_EN` and `SUMMARY_GE` are strings representing the verbal descriptions of the accident\n",
-    "in English and German, respectively.\n",
-    "\n",
-    "`NUMTOTV` is the number of vehicles involved in the case. Let's have a look at the distribution of this feature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 542
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z2xX3ICs0AAo"
+      },
+      "source": [
+        "#### Computing Power\n",
+        "This notebook is computationally intensive. We recommend using a platform with GPU support.\n",
+        "\n",
+        "We have run this notebook on Google Colab and on an Amazon EC2 p2.xlarge instance (an older generation of GPU-based instances).\n",
+        "\n",
+        "Please note that the results may not be reproducible across platforms and versions."
+      ]
     },
-    "id": "wx8Bp2n-WNvW",
-    "outputId": "d74f1f13-7540-444d-f656-e255f7c7231f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "        <script type=\"text/javascript\">\n",
-       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
-       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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-       " */\n",
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t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],196:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],203:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],204:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],205:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],206:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var 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this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new 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e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",m),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",m),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=(new Error).stack||\"\",this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var m=s.columns,g=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/m,w=y/m;s.passes=[];for(var T=0;T<m;T++)for(var k=0;k<m;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:m},y:{buffer:s.buffer,offset:T,count:g,stride:m}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":346,\"object-assign\":247}],337:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":346,\"object-assign\":247}],338:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":346,\"object-assign\":247}],341:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":346,\"object-assign\":247}],344:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":346,\"object-assign\":247}],345:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var 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r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),m=t(\"../titles\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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r=n.select(this);c.ensureSingle(r,\"rect\",A.cbbg),c.ensureSingle(r,\"g\",A.cbfills),c.ensureSingle(r,\"g\",A.cblines),c.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),c.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),c.ensureSingle(r,\"rect\",A.cboutline);var v=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,v=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,I=e.ypad,O=e.x,z=o?e.y:1-e.y,D=r._fullLayout,R=D._size,F=e._fillcolor,B=e._line,N=e.title,j=N.side,U=e._zrange||n.extent((\"function\"==typeof F?F:B.color).domain()),V=\"function\"==typeof B.color?B.color:function(){return B.color},H=\"function\"==typeof F?F:function(){return F},q=e._levels,G=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,c=a.size,u=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*c,!(c>0?n>=l:n<=l));i++)n>u&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,c=h.size,i=0;i<1e5&&(n=h.start+i*c,!(c>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);a.size<0&&(o.reverse(),s.reverse());return{line:o,fill:s}}(0,e,U),Y=G.fill,W=G.line,X=Math.round(v*(\"fraction\"===_?o?R.w:R.h:1)),Z=X/(o?R.w:R.h),J=Math.round(l*(\"fraction\"===h?o?R.h:R.w:1)),K=J/(o?R.h:R.w),Q=Math.round(o?O*R.w+P:z*R.h+I),$={center:.5,right:1}[L]||0,tt={top:1,middle:.5}[C]||0,et=o?O-$*Z:z-tt*Z,rt=o?z-tt*K:O-$*K,nt=Math.round(o?R.h*(1-rt):R.w*rt);e._lenFrac=K,e._thickFrac=Z,e._uFrac=et,e._vFrac=rt;var it=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),mt=0,gt=0;function vt(n,i){var a={propContainer:it,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:D._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),m.draw(r,n,f(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&at||!o&&!at)&&(\"top\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt-K)+3+.75*pt),\"bottom\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt)-3-.25*pt),\"right\"===j&&(e=I+R.t+R.h*z+3+.75*pt,t=P+R.l+R.w*rt),vt(it._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!at||o&&at){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+it._id+\"title-math-group\").node(),m=15.6;if(f.node()&&(m=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),gt=a.width,(mt=a.height)>m&&(h[1]-=(mt-m)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),gt=a.width,mt=a.height),o){if(mt){if(mt+=5,\"top\"===j)it.domain[1]-=mt/R.h,h[1]*=-1;else{it.domain[0]+=mt/R.h;var v=g.lineCount(f);h[1]+=(1-v)*m}l.attr(\"transform\",u(h[0],h[1])),it.setScale()}}else gt&&(\"right\"===j&&(it.domain[0]+=(gt+pt/2)/R.w),l.attr(\"transform\",u(h[0],h[1])),it.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?u(0,Math.round(R.h*(1-it.domain[1]))):u(Math.round(R.w*it.domain[0]),0)),dt.attr(\"transform\",o?u(0,Math.round(-R.t)):u(Math.round(-R.l),0));var y=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(Y);y.enter().append(\"rect\").classed(A.cbfill,!0).style(\"stroke\",\"none\"),y.exit().remove();var x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,m=!o&&\"right\"===j,g=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,g=n.height)),m&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,g+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=g;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+g-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=m?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?mt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+mt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-g))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var Y={},W=T[L],Z=k[L],K=T[C],et=k[C],rt=b-X;o?(\"pixels\"===h?(Y.y=z,Y.t=J*K,Y.b=J*et):(Y.t=Y.b=0,Y.yt=z+l*K,Y.yb=z-l*et),\"pixels\"===_?(Y.x=O,Y.l=b*W,Y.r=b*Z):(Y.l=rt*W,Y.r=rt*Z,Y.xl=O-v*W,Y.xr=O+v*Z)):(\"pixels\"===h?(Y.x=O,Y.l=J*W,Y.r=J*Z):(Y.l=Y.r=0,Y.xl=O+l*W,Y.xr=O-l*Z),\"pixels\"===_?(Y.y=1-z,Y.t=b*K,Y.b=b*et):(Y.t=rt*K,Y.b=rt*et,Y.yt=z-v*K,Y.yb=z+v*et)),a.autoMargin(r,e._id,Y)}],r)}(r,e,t);v&&v.then&&(t._promises||[]).push(v),t._context.edits.colorbarPosition&&function(t,e,r){var n,i,a,s=\"v\"===e.orientation,c=r._fullLayout._size;l.init({element:t.node(),gd:r,prepFn:function(){n=t.attr(\"transform\"),h(t)},moveFn:function(r,o){t.attr(\"transform\",n+u(r,o)),i=l.align((s?e._uFrac:e._vFrac)+r/c.w,s?e._thickFrac:e._lenFrac,0,1,e.xanchor),a=l.align((s?e._vFrac:1-e._uFrac)-o/c.h,s?e._lenFrac:e._thickFrac,0,1,e.yanchor);var f=l.getCursor(i,a,e.xanchor,e.yanchor);h(t,f)},doneFn:function(){if(h(t),void 0!==i&&void 0!==a){var n={};n[e._propPrefix+\"x\"]=i,n[e._propPrefix+\"y\"]=a,void 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var m=d[0];i(m.x0)&&i(m.y0)&&(h=ht(m),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=mt;var gt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};gt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),m=t.trace;if(g.traceIs(m,\"gl3d\")){var v=i._fullLayout[m.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(g,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),m=d.LINE_SPACING,g=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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rectangle\")},attr:\"dragmode\",val:\"drawrect\",icon:o.drawrect,click:f},u.drawcircle={name:\"drawcircle\",title:function(t){return c(t,\"Draw circle\")},attr:\"dragmode\",val:\"drawcircle\",icon:o.drawcircle,click:f},u.eraseshape={name:\"eraseshape\",title:function(t){return c(t,\"Erase active shape\")},icon:o.eraseshape,click:s},u.zoomIn2d={name:\"zoomIn2d\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:f},u.zoomOut2d={name:\"zoomOut2d\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:f},u.autoScale2d={name:\"autoScale2d\",_cat:\"autoscale\",title:function(t){return c(t,\"Autoscale\")},attr:\"zoom\",val:\"auto\",icon:o.autoscale,click:f},u.resetScale2d={name:\"resetScale2d\",_cat:\"resetscale\",title:function(t){return c(t,\"Reset axes\")},attr:\"zoom\",val:\"reset\",icon:o.home,click:f},u.hoverClosestCartesian={name:\"hoverClosestCartesian\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:f},u.hoverCompareCartesian={name:\"hoverCompareCartesian\",_cat:\"hoverCompare\",title:function(t){return c(t,\"Compare data on hover\")},attr:\"hovermode\",val:function(t){return t._fullLayout._isHoriz?\"y\":\"x\"},icon:o.tooltip_compare,gravity:\"ne\",click:f},u.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(t){return c(t,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:o.zoombox,click:h},u.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(t){return c(t,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:o.pan,click:h},u.orbitRotation={name:\"orbitRotation\",title:function(t){return c(t,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:o[\"3d_rotate\"],click:h},u.tableRotation={name:\"tableRotation\",title:function(t){return c(t,\"Turntable rotation\")},attr:\"scene.dragmode\",val:\"turntable\",icon:o[\"z-axis\"],click:h},u.resetCameraDefault3d={name:\"resetCameraDefault3d\",_cat:\"resetCameraDefault\",title:function(t){return c(t,\"Reset camera to default\")},attr:\"resetDefault\",icon:o.home,click:p},u.resetCameraLastSave3d={name:\"resetCameraLastSave3d\",_cat:\"resetCameraLastSave\",title:function(t){return c(t,\"Reset camera to last save\")},attr:\"resetLastSave\",icon:o.movie,click:p},u.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);n.call(\"_guiRelayout\",t,r)}},u.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:m},u.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(t){return c(t,\"Reset\")},attr:\"reset\",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestGl2d={name:\"hoverClosestGl2d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:v},u.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return c(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},u.toggleHover={name:\"toggleHover\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call(\"_guiRelayout\",t,r)}},u.resetViews={name:\"resetViews\",title:function(t){return c(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:\"zoomOutMapbox\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:y}},{\"../../fonts/ploticon\":482,\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../plots/plots\":619,\"../../registry\":638,\"../shapes/draw\":450}],427:[function(t,e,r){\"use strict\";var n=t(\"./buttons\"),i=Object.keys(n),a=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],o=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),e.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},{\"./buttons\":426}],428:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES,u=t(\"../../lib\").extendDeep;e.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=u([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(m||b||y||x||_)+v+g+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):g?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];m&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!m&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e})(r)&&z.push(\"select2d\",\"lasso2d\");var D=[],R=function(t){-1===D.indexOf(t)&&-1!==I.indexOf(t)&&D.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];\"string\"==typeof N?(N=N.toLowerCase(),-1!==c.indexOf(N)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&z.push(N):\"togglespikelines\"===N?R(\"toggleSpikelines\"):\"togglehover\"===N?R(\"toggleHover\"):\"hovercompare\"===N?R(\"hoverCompareCartesian\"):\"hoverclosest\"===N?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\")):\"v1hovermode\"===N&&(R(\"toggleHover\"),R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\"))):F.push(N)}p=F}return L(z),L(P.concat(O)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},{\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../../traces/scatter/subtypes\":952,\"../fx/helpers\":402,\"./buttons\":426,\"./constants\":427,\"./modebar\":431}],431:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"fast-isnumeric\"),a=t(\"../../lib\"),o=t(\"../../fonts/ploticon\"),s=t(\"../../version\").version,l=new DOMParser;function c(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var u=c.prototype;u.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover \":\"\";a.deleteRelatedStyleRule(i),a.addRelatedStyleRule(i,s+\"#\"+i+\" .modebar-group\",\"background-color: \"+o.bgcolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn .icon path\",\"fill: \"+o.color),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn:hover .icon path\",\"fill: \"+o.activecolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn.active .icon path\",\"fill: \"+o.activecolor);var l=!this.hasButtons(e),c=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(l||c||u)&&(this.removeAllButtons(),this.updateButtons(e),r.watermark||r.displaylogo)){var f=this.getLogo();r.watermark&&(f.className=f.className+\" watermark\"),\"v\"===n.modebar.orientation?this.element.insertBefore(f,this.element.childNodes[0]):this.element.appendChild(f),this.hasLogo=!0}this.updateActiveButton()},u.updateButtons=function(t){var e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},u.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},u.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute(\"data-toggle\",t.toggle||!1),t.toggle&&n.select(r).classed(\"active\",!0);var s=t.icon;return\"function\"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute(\"data-gravity\",t.gravity||\"n\"),r},u.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n=\"http://www.w3.org/2000/svg\";if(t.path){(e=document.createElementNS(n,\"svg\")).setAttribute(\"viewBox\",[0,0,t.width,r].join(\" \")),e.setAttribute(\"class\",\"icon\");var a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]);return e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},u.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",c===i)}}))},u.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var 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t.match(n.segmentRE).forEach((function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}})),r},r.getDataToPixel=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){return e._length*(n?1-t:t)+e._offset};else{var s=r.shapePositionToRange(e);a=function(t){return e._offset+e.r2p(s(t,!0))},\"date\"===e.type&&(a=r.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},r.getPixelToData=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){var r=(t-e._offset)/e._length;return n?1-r:r};else{var s=r.rangeToShapePosition(e);a=function(t){return s(e.p2r(t-e._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},r.makeOptionsAndPlotinfo=function(t,e){var 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/pad_attributes\"),a=t(\"../../lib/extend\").extendDeepAll,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/animation_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=t(\"./constants\"),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plot_api/plot_template\":543,\"../../plots/animation_attributes\":548,\"../../plots/font_attributes\":585,\"../../plots/pad_attributes\":618,\"./constants\":460}],460:[function(t,e,r){\"use 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return 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t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),o.on(\"touchend\",null),o.on(\"touchmove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}T(e,r,t,c,!0),t._dragging=!0,o.on(\"mousemove\",u),o.on(\"touchmove\",u),o.on(\"mouseup\",h),o.on(\"touchend\",h)}t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function M(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var 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n=t(\"@plotly/d3\"),i=t(\"d3-time-format\").utcFormat,a=t(\"d3-format\").format,o=t(\"fast-isnumeric\"),s=t(\"../constants/numerical\"),l=s.FP_SAFE,c=-l,u=s.BADNUM,f=e.exports={};f.adjustFormat=function(t){return!t||/^\\d[.]\\df/.test(t)||/[.]\\d%/.test(t)?t:\"0.f\"===t?\"~f\":/^\\d%/.test(t)?\"~%\":/^\\ds/.test(t)?\"~s\":!/^[~,.0$]/.test(t)&&/[&fps]/.test(t)?\"~\"+t:t};var h={};f.warnBadFormat=function(t){var e=String(t);h[e]||(h[e]=1,f.warn('encountered bad format: \"'+e+'\"'))},f.noFormat=function(t){return String(t)},f.numberFormat=function(t){var e;try{e=a(f.adjustFormat(t))}catch(e){return f.warnBadFormat(t),f.noFormat}return e},f.nestedProperty=t(\"./nested_property\"),f.keyedContainer=t(\"./keyed_container\"),f.relativeAttr=t(\"./relative_attr\"),f.isPlainObject=t(\"./is_plain_object\"),f.toLogRange=t(\"./to_log_range\"),f.relinkPrivateKeys=t(\"./relink_private\");var 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b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener(\"resize\",t._responsiveChartHandler)):o.clearResponsive(t);var T=o.extendFlat({},m._size),k=0;function A(){if(h.clearAutoMarginIds(t),w.drawMarginPushers(t),p.allowAutoMargin(t),m._has(\"pie\"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];\"pie\"===n.type&&n.automargin&&h.allowAutoMargin(t,\"pie.\"+n.uid+\".automargin\")}return h.doAutoMargin(t),h.previousPromises(t)}function S(){t._transitioning||(w.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),u.getComponentMethod(\"rangeslider\",\"calcAutorange\")(t))}var E=[h.previousPromises,function(){if(s)return r.addFrames(t,s)},function e(){for(var 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u=e._clips.selectAll(\".axesclip\").data(c,(function(t){return t.x._id+t.y._id}));u.enter().append(\"clipPath\").classed(\"axesclip\",!0).attr(\"id\",(function(t){return\"clip\"+e._uid+t.x._id+t.y._id})).append(\"rect\"),u.exit().remove(),u.each((function(t){n.select(this).select(\"rect\").attr({x:t.x._offset||0,y:t.y._offset||0,width:t.x._length||1,height:t.y._length||1})}))}},H.draw=function(t,e,r){var n=t._fullLayout;\"redraw\"===e&&n._paper.selectAll(\"g.subplot\").each((function(t){var e=t[0],r=n._plots[e];if(r){var i=r.xaxis,a=r.yaxis;r.xaxislayer.selectAll(\".\"+i._id+\"tick\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"tick2\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick2\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"divider\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"divider\").remove(),r.minorGridlayer&&r.minorGridlayer.selectAll(\"path\").remove(),r.gridlayer&&r.gridlayer.selectAll(\"path\").remove(),r.zerolinelayer&&r.zerolinelayer.selectAll(\"path\").remove(),n._infolayer.select(\".g-\"+i._id+\"title\").remove(),n._infolayer.select(\".g-\"+a._id+\"title\").remove()}}));var i=e&&\"redraw\"!==e?e:H.listIds(t);return s.syncOrAsync(i.map((function(e){return function(){if(e){var n=H.getFromId(t,e),i=H.drawOne(t,n,r);return n._r=n.range.slice(),n._rl=s.simpleMap(n._r,n.r2l),i}}})))},H.drawOne=function(t,e,r){var n,i,l;r=r||{},e.setScale();var c=t._fullLayout,p=e._id,d=p.charAt(0),m=H.counterLetter(p),g=c._plots[e._mainSubplot];if(g){var v=g[d+\"axislayer\"],y=e._mainLinePosition,x=e._mainMirrorPosition,b=e._vals=H.calcTicks(e),_=[e.mirror,y,x].join(\"_\");for(n=0;n<b.length;n++)b[n].axInfo=_;e._selections={},e._tickAngles&&(e._prevTickAngles=e._tickAngles),e._tickAngles={},e._depth=null;var w={};if(e.visible){var T,k,A=H.makeTransTickFn(e),M=H.makeTransTickLabelFn(e),S=\"inside\"===e.ticks,E=\"outside\"===e.ticks;if(\"boundaries\"===e.tickson){var L=function(t,e){var r,n=[],i=function(t,e){var r=t.xbnd[e];null!==r&&n.push(s.extendFlat({},t,{x:r}))};if(e.length){for(r=0;r<e.length;r++)i(e[r],0);i(e[r-1],1)}return n}(0,b);k=H.clipEnds(e,L),T=S?k:L}else k=H.clipEnds(e,b),T=S&&\"period\"!==e.ticklabelmode?k:b;var C,P=e._gridVals=k,I=function(t,e){var r,n,i=[],a=e.length&&e[e.length-1].x<e[0].x,o=function(t,e){var r=t.xbnd[e];null!==r&&i.push(s.extendFlat({},t,{x:r}))};if(t.showdividers&&e.length){for(r=0;r<e.length;r++){var l=e[r];l.text2!==n&&o(l,a?1:0),n=l.text2}o(e[r-1],a?0:1)}return i}(e,b);if(!c._hasOnlyLargeSploms){var O=e._subplotsWith,z={};for(n=0;n<O.length;n++){i=O[n];var D=(l=c._plots[i])[m+\"axis\"],R=D._mainAxis._id;if(!z[R]){z[R]=1;var F=\"x\"===d?\"M0,\"+D._offset+\"v\"+D._length:\"M\"+D._offset+\",0h\"+D._length;H.drawGrid(t,e,{vals:P,counterAxis:D,layer:l.gridlayer.select(\".\"+p),minorLayer:l.minorGridlayer.select(\".\"+p),path:F,transFn:A}),H.drawZeroLine(t,e,{counterAxis:D,layer:l.zerolinelayer,path:F,transFn:A})}}}var B=H.getTickSigns(e),N=H.getTickSigns(e,\"minor\");if(e.ticks||e.minor&&e.minor.ticks){var j,q,G,Y,W=H.makeTickPath(e,y,B[2]),X=H.makeTickPath(e,y,N[2],{minor:!0});if(e._anchorAxis&&e.mirror&&!0!==e.mirror?(j=H.makeTickPath(e,x,B[3]),q=H.makeTickPath(e,x,N[3],{minor:!0}),G=W+j,Y=X+q):(j=\"\",q=\"\",G=W,Y=X),e.showdividers&&E&&\"boundaries\"===e.tickson){var Z={};for(n=0;n<I.length;n++)Z[I[n].x]=1;C=function(t){return Z[t.x]?j:G}}else C=function(t){return t.minor?Y:G}}if(H.drawTicks(t,e,{vals:T,layer:v,path:C,transFn:A}),\"allticks\"===e.mirror){var J=Object.keys(e._linepositions||{});for(n=0;n<J.length;n++){i=J[n],l=c._plots[i];var K=e._linepositions[i]||[],Q=K[0],$=K[1],tt=K[2],et=H.makeTickPath(e,Q,tt?B[0]:N[0],{minor:tt})+H.makeTickPath(e,$,tt?B[1]:N[1],{minor:tt});H.drawTicks(t,e,{vals:T,layer:l[d+\"axislayer\"],path:et,transFn:A})}}var rt=[];if(rt.push((function(){return H.drawLabels(t,e,{vals:b,layer:v,plotinfo:l,transFn:M,labelFns:H.makeLabelFns(e,y)})})),\"multicategory\"===e.type){var nt={x:2,y:10}[d];rt.push((function(){var r={x:\"height\",y:\"width\"}[d],n=at()[r]+nt+(e._tickAngles[p+\"tick\"]?e.tickfont.size*U:0);return H.drawLabels(t,e,{vals:vt(e,b),layer:v,cls:p+\"tick2\",repositionOnUpdate:!0,secondary:!0,transFn:A,labelFns:H.makeLabelFns(e,y+n*B[4])})})),rt.push((function(){return e._depth=B[4]*(at(\"tick2\")[e.side]-y),function(t,e,r){var n=e._id+\"divider\",i=r.vals,a=r.layer.selectAll(\"path.\"+n).data(i,xt);a.exit().remove(),a.enter().insert(\"path\",\":first-child\").classed(n,1).classed(\"crisp\",1).call(f.stroke,e.dividercolor).style(\"stroke-width\",h.crispRound(t,e.dividerwidth,1)+\"px\"),a.attr(\"transform\",r.transFn).attr(\"d\",r.path)}(t,e,{vals:I,layer:v,path:H.makeTickPath(e,y,B[4],{len:e._depth}),transFn:A})}))}else e.title.hasOwnProperty(\"standoff\")&&rt.push((function(){e._depth=B[4]*(at()[e.side]-y)}));var it=o.getComponentMethod(\"rangeslider\",\"isVisible\")(e);return rt.push((function(){var r,n,i,s,l=e.side.charAt(0),u=V[e.side].charAt(0),f=H.getPxPosition(t,e),h=E?e.ticklen:0;if((e.automargin||it)&&(\"multicategory\"===e.type?r=at(\"tick2\"):(r=at(),\"x\"===d&&\"b\"===l&&(e._depth=Math.max(r.width>0?r.bottom-f:0,h)))),e.automargin){n={x:0,y:0,r:0,l:0,t:0,b:0};var p=[0,1];if(\"x\"===d){if(\"b\"===l?n[l]=e._depth:(n[l]=e._depth=Math.max(r.width>0?f-r.top:0,h),p.reverse()),r.width>0){var g=r.right-(e._offset+e._length);g>0&&(n.xr=1,n.r=g);var v=e._offset-r.left;v>0&&(n.xl=0,n.l=v)}}else if(\"l\"===l?n[l]=e._depth=Math.max(r.height>0?f-r.left:0,h):(n[l]=e._depth=Math.max(r.height>0?r.right-f:0,h),p.reverse()),r.height>0){var y=r.bottom-(e._offset+e._length);y>0&&(n.yb=0,n.b=y);var x=e._offset-r.top;x>0&&(n.yt=1,n.t=x)}n[m]=\"free\"===e.anchor?e.position:e._anchorAxis.domain[p[0]],e.title.text!==c._dfltTitle[d]&&(n[l]+=bt(e)+(e.title.standoff||0)),e.mirror&&\"free\"!==e.anchor&&((i={x:0,y:0,r:0,l:0,t:0,b:0})[u]=e.linewidth,e.mirror&&!0!==e.mirror&&(i[u]+=h),!0===e.mirror||\"ticks\"===e.mirror?i[m]=e._anchorAxis.domain[p[1]]:\"all\"!==e.mirror&&\"allticks\"!==e.mirror||(i[m]=[e._counterDomainMin,e._counterDomainMax][p[1]]))}it&&(s=o.getComponentMethod(\"rangeslider\",\"autoMarginOpts\")(t,e)),a.autoMargin(t,Tt(e),n),a.autoMargin(t,kt(e),i),a.autoMargin(t,At(e),s)})),r.skipTitle||it&&\"bottom\"===e.side||rt.push((function(){return function(t,e){var r,n=t._fullLayout,i=e._id,a=i.charAt(0),o=e.title.font.size;if(e.title.hasOwnProperty(\"standoff\"))r=e._depth+e.title.standoff+bt(e);else{var s=Pt(e);if(\"multicategory\"===e.type)r=e._depth;else{var l=1.5*o;s&&(l=.5*o,\"outside\"===e.ticks&&(l+=e.ticklen)),r=10+l+(e.linewidth?e.linewidth-1:0)}s||(r+=\"x\"===a?\"top\"===e.side?o*(e.showticklabels?1:0):o*(e.showticklabels?1.5:.5):\"right\"===e.side?o*(e.showticklabels?1:.5):o*(e.showticklabels?.5:0))}var c,f,p,d,m=H.getPxPosition(t,e);\"x\"===a?(f=e._offset+e._length/2,p=\"top\"===e.side?m-r:m+r):(p=e._offset+e._length/2,f=\"right\"===e.side?m+r:m-r,c={rotate:\"-90\",offset:0});if(\"multicategory\"!==e.type){var g=e._selections[e._id+\"tick\"];if(d={selection:g,side:e.side},g&&g.node()&&g.node().parentNode){var v=h.getTranslate(g.node().parentNode);d.offsetLeft=v.x,d.offsetTop=v.y}e.title.hasOwnProperty(\"standoff\")&&(d.pad=0)}return u.draw(t,i+\"title\",{propContainer:e,propName:e._name+\".title.text\",placeholder:n._dfltTitle[a],avoid:d,transform:c,attributes:{x:f,y:p,\"text-anchor\":\"middle\"}})}(t,e)})),s.syncOrAsync(rt)}}function at(t){var r=p+(t||\"tick\");return w[r]||(w[r]=function(t,e){var r,n,i,a;t._selections[e].size()?(r=1/0,n=-1/0,i=1/0,a=-1/0,t._selections[e].each((function(){var t=wt(this),e=h.bBox(t.node().parentNode);r=Math.min(r,e.top),n=Math.max(n,e.bottom),i=Math.min(i,e.left),a=Math.max(a,e.right)}))):(r=0,n=0,i=0,a=0);return{top:r,bottom:n,left:i,right:a,height:n-r,width:a-i}}(e,r)),w[r]}},H.getTickSigns=function(t,e){var r=t._id.charAt(0),n={x:\"top\",y:\"right\"}[r],i=t.side===n?1:-1,a=[-1,1,i,-i];return\"inside\"!==(e?(t.minor||{}).ticks:t.ticks)==(\"x\"===r)&&(a=a.map((function(t){return-t}))),t.side&&a.push({l:-1,t:-1,r:1,b:1}[t.side.charAt(0)]),a},H.makeTransTickFn=function(t){return\"x\"===t._id.charAt(0)?function(e){return l(t._offset+t.l2p(e.x),0)}:function(e){return l(0,t._offset+t.l2p(e.x))}},H.makeTransTickLabelFn=function(t){var e=function(t){var e=t.ticklabelposition||\"\",r=function(t){return-1!==e.indexOf(t)},n=r(\"top\"),i=r(\"left\"),a=r(\"right\"),o=r(\"bottom\"),s=r(\"inside\"),l=o||i||n||a;if(!l&&!s)return[0,0];var c=t.side,u=l?(t.tickwidth||0)/2:0,f=3,h=t.tickfont?t.tickfont.size:12;(o||n)&&(u+=h*j,f+=(t.linewidth||0)/2);(i||a)&&(u+=(t.linewidth||0)/2,f+=3);s&&\"top\"===c&&(f-=h*(1-j));(i||n)&&(u=-u);\"bottom\"!==c&&\"right\"!==c||(f=-f);return[l?u:0,s?f:0]}(t),r=e[0],n=e[1];return\"x\"===t._id.charAt(0)?function(e){return l(r+t._offset+t.l2p(yt(e)),n)}:function(e){return l(n,r+t._offset+t.l2p(yt(e)))}},H.makeTickPath=function(t,e,r,n){n||(n={});var i=n.minor;if(i&&!t.minor)return\"\";var a=void 0!==n.len?n.len:i?t.minor.ticklen:t.ticklen,o=t._id.charAt(0),s=(t.linewidth||1)/2;return\"x\"===o?\"M0,\"+(e+s*r)+\"v\"+a*r:\"M\"+(e+s*r)+\",0h\"+a*r},H.makeLabelFns=function(t,e,r){var n=t.ticklabelposition||\"\",a=function(t){return-1!==n.indexOf(t)},o=a(\"top\"),l=a(\"left\"),c=a(\"right\"),u=a(\"bottom\")||l||o||c,f=a(\"inside\"),h=\"inside\"===n&&\"inside\"===t.ticks||!f&&\"outside\"===t.ticks&&\"boundaries\"!==t.tickson,p=0,d=0,m=h?t.ticklen:0;if(f?m*=-1:u&&(m=0),h&&(p+=m,r)){var g=s.deg2rad(r);p=m*Math.cos(g)+1,d=m*Math.sin(g)}t.showticklabels&&(h||t.showline)&&(p+=.2*t.tickfont.size);var v,y,x,b,_,w={labelStandoff:p+=(t.linewidth||1)/2*(f?-1:1),labelShift:d},T=0,k=t.side,A=t._id.charAt(0),M=t.tickangle;if(\"x\"===A)b=(_=!f&&\"bottom\"===k||f&&\"top\"===k)?1:-1,f&&(b*=-1),v=d*b,y=e+p*b,x=_?1:-.2,90===Math.abs(M)&&(f?x+=N:x=-90===M&&\"bottom\"===k?j:90===M&&\"top\"===k?N:.5,T=N/2*(M/90)),w.xFn=function(t){return t.dx+v+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*x},w.anchorFn=function(t,e){if(u){if(l)return\"end\";if(c)return\"start\"}return i(e)&&0!==e&&180!==e?e*b<0!==f?\"end\":\"start\":\"middle\"},w.heightFn=function(e,r,n){return r<-60||r>60?-.5*n:\"top\"===t.side!==f?-n:0};else if(\"y\"===A){if(b=(_=!f&&\"left\"===k||f&&\"right\"===k)?1:-1,f&&(b*=-1),v=p,y=d*b,x=0,f||90!==Math.abs(M)||(x=-90===M&&\"left\"===k||90===M&&\"right\"===k?j:.5),f){var S=i(M)?+M:0;if(0!==S){var E=s.deg2rad(S);T=Math.abs(Math.sin(E))*j*b,x=0}}w.xFn=function(t){return t.dx+e-(v+t.fontSize*x)*b+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*N},w.anchorFn=function(t,e){return i(e)&&90===Math.abs(e)?\"middle\":_?\"end\":\"start\"},w.heightFn=function(e,r,n){return\"right\"===t.side&&(r*=-1),r<-30?-n:r<30?-.5*n:0}}return w},H.drawTicks=function(t,e,r){r=r||{};var i=e._id+\"tick\",a=[].concat(e.minor&&e.minor.ticks?r.vals.filter((function(t){return t.minor&&!t.noTick})):[]).concat(e.ticks?r.vals.filter((function(t){return!t.minor&&!t.noTick})):[]),o=r.layer.selectAll(\"path.\"+i).data(a,xt);o.exit().remove(),o.enter().append(\"path\").classed(i,1).classed(\"ticks\",1).classed(\"crisp\",!1!==r.crisp).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.tickcolor:e.tickcolor)})).style(\"stroke-width\",(function(r){return h.crispRound(t,r.minor?e.minor.tickwidth:e.tickwidth,1)+\"px\"})).attr(\"d\",r.path).style(\"display\",null),It(e,[R]),o.attr(\"transform\",r.transFn)},H.drawGrid=function(t,e,r){r=r||{};var i=e._id+\"grid\",a=e.minor&&e.minor.showgrid,o=a?r.vals.filter((function(t){return t.minor})):[],s=e.showgrid?r.vals.filter((function(t){return!t.minor})):[],l=r.counterAxis;if(l&&H.shouldShowZeroLine(t,e,l))for(var c=\"array\"===e.tickmode,u=0;u<s.length;u++){var p=s[u].x;if(c?!p:Math.abs(p)<e.dtick/100){if(s=s.slice(0,u).concat(s.slice(u+1)),!c)break;u--}}e._gw=h.crispRound(t,e.gridwidth,1);for(var d=a?h.crispRound(t,e.minor.gridwidth,1):0,m=r.layer,g=r.minorLayer,v=1;v>=0;v--){var y=v?m:g;if(y){var x=y.selectAll(\"path.\"+i).data(v?s:o,xt);x.exit().remove(),x.enter().append(\"path\").classed(i,1).classed(\"crisp\",!1!==r.crisp),x.attr(\"transform\",r.transFn).attr(\"d\",r.path).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.gridcolor:e.gridcolor||\"#ddd\")})).style(\"stroke-dasharray\",(function(t){return h.dashStyle(t.minor?e.minor.griddash:e.griddash,t.minor?e.minor.gridwidth:e.gridwidth)})).style(\"stroke-width\",(function(t){return(t.minor?d:e._gw)+\"px\"})).style(\"display\",null),\"function\"==typeof r.path&&x.attr(\"d\",r.path)}}It(e,[z,D])},H.drawZeroLine=function(t,e,r){r=r||r;var n=e._id+\"zl\",i=H.shouldShowZeroLine(t,e,r.counterAxis),a=r.layer.selectAll(\"path.\"+n).data(i?[{x:0,id:e._id}]:[]);a.exit().remove(),a.enter().append(\"path\").classed(n,1).classed(\"zl\",1).classed(\"crisp\",!1!==r.crisp).each((function(){r.layer.selectAll(\"path\").sort((function(t,e){return Y(t.id,e.id)}))})),a.attr(\"transform\",r.transFn).attr(\"d\",r.path).call(f.stroke,e.zerolinecolor||f.defaultLine).style(\"stroke-width\",h.crispRound(t,e.zerolinewidth,e._gw||1)+\"px\").style(\"display\",null),It(e,[O])},H.drawLabels=function(t,e,r){r=r||{};var a=t._fullLayout,o=e._id,u=o.charAt(0),f=r.cls||o+\"tick\",p=r.vals.filter((function(t){return t.text})),d=r.labelFns,m=r.secondary?0:e.tickangle,g=(e._prevTickAngles||{})[f],v=r.layer.selectAll(\"g.\"+f).data(e.showticklabels?p:[],xt),y=[];function x(t,a){t.each((function(t){var o=n.select(this),s=o.select(\".text-math-group\"),u=d.anchorFn(t,a),f=r.transFn.call(o.node(),t)+(i(a)&&0!=+a?\" rotate(\"+a+\",\"+d.xFn(t)+\",\"+(d.yFn(t)-t.fontSize/2)+\")\":\"\"),p=c.lineCount(o),m=U*t.fontSize,g=d.heightFn(t,i(a)?+a:0,(p-1)*m);if(g&&(f+=l(0,g)),s.empty()){var v=o.select(\"text\");v.attr({transform:f,\"text-anchor\":u}),v.style(\"opacity\",1),e._adjustTickLabelsOverflow&&e._adjustTickLabelsOverflow()}else{var y=h.bBox(s.node()).width*{end:-.5,start:.5}[u];s.attr(\"transform\",f+l(y,0))}}))}v.enter().append(\"g\").classed(f,1).append(\"text\").attr(\"text-anchor\",\"middle\").each((function(e){var r=n.select(this),i=t._promises.length;r.call(c.positionText,d.xFn(e),d.yFn(e)).call(h.font,e.font,e.fontSize,e.fontColor).text(e.text).call(c.convertToTspans,t),t._promises[i]?y.push(t._promises.pop().then((function(){x(r,m)}))):x(r,m)})),It(e,[F]),v.exit().remove(),r.repositionOnUpdate&&v.each((function(t){n.select(this).select(\"text\").call(c.positionText,d.xFn(t),d.yFn(t))})),e._adjustTickLabelsOverflow=function(){var r=e.ticklabeloverflow;if(r&&\"allow\"!==r){var i=-1!==r.indexOf(\"hide\"),o=\"x\"===e._id.charAt(0),l=0,c=o?t._fullLayout.width:t._fullLayout.height;if(-1!==r.indexOf(\"domain\")){var u=s.simpleMap(e.range,e.r2l);l=e.l2p(u[0])+e._offset,c=e.l2p(u[1])+e._offset}var f=Math.min(l,c),p=Math.max(l,c),d=e.side,m=1/0,g=-1/0;for(var y in v.each((function(t){var 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o=\"tick\"===t.K&&\"text\"===t.L&&\"period\"===e.ticklabelmode,s=a._plots[e._mainSubplot];(t.K===O.K?s.zerolinelayer.selectAll(\".\"+e._id+\"zl\"):t.K===D.K?s.minorGridlayer.selectAll(\".\"+e._id):t.K===z.K?s.gridlayer.selectAll(\".\"+e._id):s[e._id.charAt(0)+\"axislayer\"]).each((function(){var a=n.select(this);t.L&&(a=a.selectAll(t.L)),a.each((function(a){var s=e.l2p(o?yt(a):a.x)+e._offset,l=n.select(this);s<e[\"_visibleLabelMax_\"+r._id]&&s>e[\"_visibleLabelMin_\"+r._id]?l.style(\"display\",\"none\"):\"tick\"!==t.K||i||l.style(\"display\",null)}))}))}))}))},x(v,g+1?g:m);var b=null;e._selections&&(e._selections[f]=v);var _=[function(){return y.length&&Promise.all(y)}];e.automargin&&a._redrawFromAutoMarginCount&&90===g?(b=90,_.push((function(){x(v,g)}))):_.push((function(){if(x(v,m),p.length&&\"x\"===u&&!i(m)&&(\"log\"!==e.type||\"D\"!==String(e.dtick).charAt(0))){b=0;var t,n=0,a=[];if(v.each((function(t){n=Math.max(n,t.fontSize);var 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n=t(\"fast-isnumeric\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../../plot_api/plot_template\"),s=t(\"../array_container_defaults\"),l=t(\"./layout_attributes\"),c=t(\"./tick_value_defaults\"),u=t(\"./tick_mark_defaults\"),f=t(\"./tick_label_defaults\"),h=t(\"./prefix_suffix_defaults\"),p=t(\"./category_order_defaults\"),d=t(\"./line_grid_defaults\"),m=t(\"./set_convert\"),g=t(\"./constants\").WEEKDAY_PATTERN,v=t(\"./constants\").HOUR_PATTERN;function y(t,e,r){function i(r,n){return a.coerce(t,e,l.rangebreaks,r,n)}if(i(\"enabled\")){var o=i(\"bounds\");if(o&&o.length>=2){var s,c,u=\"\";if(2===o.length)for(s=0;s<2;s++)if(c=b(o[s])){u=g;break}var f=i(\"pattern\",u);if(f===g)for(s=0;s<2;s++)(c=b(o[s]))&&(e.bounds[s]=o[s]=c-1);if(f)for(s=0;s<2;s++)switch(c=o[s],f){case g:if(!n(c))return void(e.enabled=!1);if((c=+c)!==Math.floor(c)||c<0||c>=7)return void(e.enabled=!1);e.bounds[s]=o[s]=c;break;case v:if(!n(c))return void(e.enabled=!1);if((c=+c)<0||c>24)return 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),yt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":y(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,y(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=Ot,y(a)&&It(),ot||(\"zoom\"===s?(Pt.moveFn=zt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(mt,kt,Q,$,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(mt,Q,$)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;y(r)||(yt(),Yt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b=\"ns\"===at?-e[1]*a._length/O._length:Zt(a,y,{n:\"top\",s:\"bottom\"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T=\"ew\"===it?-e[0]*o._length/I._length:Zt(o,x,{e:\"right\",w:\"left\"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||\"middle\"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(\".nsewdrag\").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt(\"x\"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt(\"y\"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit(\"plotly_relayouting\",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log(\"Did not find wheel motion attributes: \",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{\"../../components/color\":366,\"../../components/dragelement\":385,\"../../components/dragelement/helpers\":384,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/clear_gl_canvases\":487,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/subroutines\":544,\"../../registry\":638,\"../plots\":619,\"./axes\":554,\"./axis_ids\":558,\"./constants\":561,\"./scale_zoom\":574,\"./select\":575,\"@plotly/d3\":58,\"has-passive-events\":229,tinycolor2:312}],564:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/fx\"),a=t(\"../../components/dragelement\"),o=t(\"../../lib/setcursor\"),s=t(\"./dragbox\").makeDragBox,l=t(\"./constants\").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(\".drag\").remove();else if(e._has(\"cartesian\")||e._has(\"splom\")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split(\"y\"),i=r.split(\"y\");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,\"ns\",\"ew\");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,\"n\",\"w\"),s(t,n,o._offset+o._length,c._offset-l,l,l,\"n\",\"e\"),s(t,n,o._offset-l,c._offset+c._length,l,l,\"s\",\"w\"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,\"s\",\"e\"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;\"top\"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,\"\",\"ew\"),s(t,n,o._offset,f,.1*o._length,l,\"\",\"w\"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,\"\",\"e\")}if(r===c._mainSubplot){var h=c._mainLinePosition;\"right\"!==c.side&&(h-=l),s(t,n,h,c._offset+.1*c._length,l,.8*c._length,\"ns\",\"\"),s(t,n,h,c._offset+.9*c._length,l,.1*c._length,\"s\",\"\"),s(t,n,h,c._offset,l,.1*c._length,\"n\",\"\")}}}));var o=e._hoverlayer.node();o.onmousemove=function(r){r.target=t._fullLayout._lasthover,i.hover(t,r,e._hoversubplot)},o.onclick=function(e){e.target=t._fullLayout._lasthover,i.click(t,e)},o.onmousedown=function(e){t._fullLayout._lasthover.onmousedown(e)},r.updateFx(t)}},r.updateFx=function(t){var e=t._fullLayout,r=\"pan\"===e.dragmode?\"move\":\"crosshair\";o(e._draggers,r)}},{\"../../components/dragelement\":385,\"../../components/fx\":406,\"../../lib/setcursor\":524,\"./constants\":561,\"./dragbox\":563,\"@plotly/d3\":58}],565:[function(t,e,r){\"use strict\";e.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearSelect:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._drawing=!1}}},{}],566:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{\"../../lib\":503}],567:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"./axis_ids\");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has(\"cartesian\")||r._has(\"gl2d\"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var g=a.cleanId(m.xref,\"x\",!1),v=a.cleanId(m.yref,\"y\",!1),y=l.x.test(g),x=l.y.test(v);if(y||x){p||i.pushUnique(r._basePlotModules,s);var b=!1;y&&-1===u.indexOf(g)&&(u.push(g),b=!0),x&&-1===f.indexOf(v)&&(f.push(v),b=!0),b&&y&&x&&h.push(g+v)}}}}}},{\"../../lib\":503,\"../../registry\":638,\"./axis_ids\":558}],568:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../plots\"),s=t(\"../../components/drawing\"),l=t(\"../get_data\").getModuleCalcData,c=t(\"./axis_ids\"),u=t(\"./constants\"),f=t(\"../../constants/xmlns_namespaces\"),h=a.ensureSingle;function p(t,e,r){return a.ensureSingle(t,e,r,(function(t){t.datum(r)}))}function d(t,e,r,a,o){for(var c,f,h,p=u.traceLayerClasses,d=t._fullLayout,m=d._modules,g=[],v=[],y=0;y<m.length;y++){var x=(c=m[y]).name,b=i.modules[x].categories;if(b.svg){var _=c.layerName||x+\"layer\",w=c.plot;h=(f=l(r,w))[0],r=f[1],h.length&&g.push({i:p.indexOf(_),className:_,plotMethod:w,cdModule:h}),b.zoomScale&&v.push(\".\"+_)}}g.sort((function(t,e){return t.i-e.i}));var T=e.plot.selectAll(\"g.mlayer\").data(g,(function(t){return t.className}));if(T.enter().append(\"g\").attr(\"class\",(function(t){return t.className})).classed(\"mlayer\",!0).classed(\"rangeplot\",e.isRangePlot),T.exit().remove(),T.order(),T.each((function(r){var i=n.select(this),l=r.className;r.plotMethod(t,e,r.cdModule,i,a,o),-1===u.clipOnAxisFalseQuery.indexOf(\".\"+l)&&s.setClipUrl(i,e.layerClipId,t)})),d._has(\"scattergl\")&&(c=i.getModule(\"scattergl\"),h=l(r,c)[0],c.plot(t,e,h)),!t._context.staticPlot&&(e._hasClipOnAxisFalse&&(e.clipOnAxisFalseTraces=e.plot.selectAll(u.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),v.length)){var k=e.plot.selectAll(v.join(\",\")).selectAll(\".trace\");e.zoomScalePts=k.selectAll(\"path.point\"),e.zoomScaleTxt=k.selectAll(\".textpoint\")}}function m(t,e){var r=e.plotgroup,n=e.id,i=u.layerValue2layerClass[e.xaxis.layer],a=u.layerValue2layerClass[e.yaxis.layer],o=t._fullLayout._hasOnlyLargeSploms;if(e.mainplot){var s=e.mainplotinfo,l=s.plotgroup,f=n+\"-x\",d=n+\"-y\";e.minorGridlayer=s.minorGridlayer,e.gridlayer=s.gridlayer,e.zerolinelayer=s.zerolinelayer,h(s.overlinesBelow,\"path\",f),h(s.overlinesBelow,\"path\",d),h(s.overaxesBelow,\"g\",f),h(s.overaxesBelow,\"g\",d),e.plot=h(s.overplot,\"g\",n),h(s.overlinesAbove,\"path\",f),h(s.overlinesAbove,\"path\",d),h(s.overaxesAbove,\"g\",f),h(s.overaxesAbove,\"g\",d),e.xlines=l.select(\".overlines-\"+i).select(\".\"+f),e.ylines=l.select(\".overlines-\"+a).select(\".\"+d),e.xaxislayer=l.select(\".overaxes-\"+i).select(\".\"+f),e.yaxislayer=l.select(\".overaxes-\"+a).select(\".\"+d)}else 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yt=b(\"rangeslider\",\"handleDefaults\"),xt=b(\"rangeselector\",\"handleDefaults\");for(y=0;y<j.length;y++)q=j[y],W=t[q],X=e[q],yt(t,e,q),\"date\"===X.type&&xt(W,X,e,U,X.calendar),K(\"fixedrange\");for(y=0;y<U.length;y++){q=U[y],W=t[q],X=e[q];var bt=e[m(X.anchor)];K(\"fixedrange\",b(\"rangeslider\",\"isVisible\")(bt))}h.handleDefaults(t,e,{axIds:rt.concat(it).sort(d.idSort),axHasImage:P})}},{\"../../components/color\":366,\"../../components/fx/helpers\":402,\"../../components/fx/hovermode_defaults\":405,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638,\"../layout_attributes\":610,\"./axis_defaults\":556,\"./axis_ids\":558,\"./constants\":561,\"./constraints\":562,\"./layout_attributes\":569,\"./position_defaults\":572,\"./type_defaults\":582}],571:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../components/color/attributes\"),a=t(\"../../lib\");e.exports=function(t,e,r,o){var s=(o=o||{}).dfltColor;function l(r,n){return a.coerce2(t,e,o.attributes,r,n)}var c=l(\"linecolor\",s),u=l(\"linewidth\");r(\"showline\",o.showLine||!!c||!!u)||(delete e.linecolor,delete e.linewidth);var f=l(\"gridcolor\",n(s,o.bgColor,o.blend||i.lightFraction).toRgbString()),h=l(\"gridwidth\"),p=l(\"griddash\");if(r(\"showgrid\",o.showGrid||!!f||!!h||!!p)||(delete e.gridcolor,delete e.gridwidth,delete e.griddash),o.hasMinor){var d=l(\"minor.gridcolor\",n(e.gridcolor,o.bgColor,67).toRgbString()),m=l(\"minor.gridwidth\",e.gridwidth||1),g=l(\"minor.griddash\",e.griddash||\"solid\");r(\"minor.showgrid\",!!d||!!m||!!g)||(delete e.minor.gridcolor,delete e.minor.gridwidth,delete e.minor.griddash)}if(!o.noZeroLine){var v=l(\"zerolinecolor\",s),y=l(\"zerolinewidth\");r(\"zeroline\",o.showGrid||!!v||!!y)||(delete e.zerolinecolor,delete e.zerolinewidth)}}},{\"../../components/color/attributes\":365,\"../../lib\":503,tinycolor2:312}],572:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\");e.exports=function(t,e,r,a){var o,s,l,c,u=a.counterAxes||[],f=a.overlayableAxes||[],h=a.letter,p=a.grid;p&&(s=p._domains[h][p._axisMap[e._id]],o=p._anchors[e._id],s&&(l=p[h+\"side\"].split(\" \")[0],c=p.domain[h][\"right\"===l||\"top\"===l?1:0])),s=s||[0,1],o=o||(n(t.position)?\"free\":u[0]||\"free\"),l=l||(\"x\"===h?\"bottom\":\"left\"),c=c||0,\"free\"===i.coerce(t,e,{anchor:{valType:\"enumerated\",values:[\"free\"].concat(u),dflt:o}},\"anchor\")&&r(\"position\",c),i.coerce(t,e,{side:{valType:\"enumerated\",values:\"x\"===h?[\"bottom\",\"top\"]:[\"left\",\"right\"],dflt:l}},\"side\");var d=!1;if(f.length&&(d=i.coerce(t,e,{overlaying:{valType:\"enumerated\",values:[!1].concat(f),dflt:!1}},\"overlaying\")),!d){var m=r(\"domain\",s);m[0]>m[1]-1/4096&&(e.domain=s),i.noneOrAll(t.domain,e.domain,s)}return r(\"layer\"),e}},{\"../../lib\":503,\"fast-isnumeric\":190}],573:[function(t,e,r){\"use strict\";var n=t(\"./show_dflt\");e.exports=function(t,e,r,i,a){a||(a={});var o=a.tickSuffixDflt,s=n(t);r(\"tickprefix\")&&r(\"showtickprefix\",s),r(\"ticksuffix\",o)&&r(\"showticksuffix\",s)}},{\"./show_dflt\":577}],574:[function(t,e,r){\"use strict\";var n=t(\"../../constants/alignment\").FROM_BL;e.exports=function(t,e,r){void 0===r&&(r=n[t.constraintoward||\"center\"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*r;t.range=t._input.range=[t.l2r(a+(i[0]-a)*e),t.l2r(a+(i[1]-a)*e)],t.setScale()}},{\"../../constants/alignment\":471}],575:[function(t,e,r){\"use strict\";var n=t(\"polybooljs\"),i=t(\"../../registry\"),a=t(\"../../components/drawing\").dashStyle,o=t(\"../../components/color\"),s=t(\"../../components/fx\"),l=t(\"../../components/fx/helpers\").makeEventData,c=t(\"../../components/dragelement/helpers\"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t(\"../../components/shapes/draw_newshape/display_outlines\"),g=t(\"../../components/shapes/draw_newshape/helpers\").handleEllipse,v=t(\"../../components/shapes/draw_newshape/newshapes\"),y=t(\"../../lib\"),x=t(\"../../lib/polygon\"),b=t(\"../../lib/throttle\"),_=t(\"./axis_ids\").getFromId,w=t(\"../../lib/clear_gl_canvases\"),T=t(\"../../plot_api/subroutines\").redrawReglTraces,k=t(\"./constants\"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t(\"./handle_outline\").clearSelect,L=t(\"./helpers\"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return 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e=[],r=z(t[0])?0:t[0][0][0],n=r,i=z(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(z(t[o]))e.push(D(t[o]));else{var s=x.tester(t[o]);s.subtract=t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!1===e[o].subtract);return a},isRect:!1,degenerate:!1}}function F(t,e,r){e._fullLayout._drawing=!1;var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(h(a)&&p(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||B(r),o||(E(e),n._lastSelectedSubplot=i.id)}function B(t){var e=t.dragmode,r=t.plotinfo,n=t.gd;if(n._fullLayout._activeShapeIndex>=0&&n._fullLayout._deactivateShape(n),h(e)){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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e=t._fullLayout,r=t.calcdata,i=e._subplots.geo,s=0;s<i.length;s++){var l=i[s],c=n(r,o,l),u=e[l]._subplot;u||(u=a({id:l,graphDiv:t,container:e._geolayer.node(),topojsonURL:t._context.topojsonURL,staticPlot:t._context.staticPlot}),e[l]._subplot=u),u.plot(c,e,t._promises)}},updateFx:function(t){for(var e=t._fullLayout,r=e._subplots.geo,n=0;n<r.length;n++){var i=e[r[n]];i._subplot.updateFx(e,i)}},clean:function(t,e,r,n){for(var i=n._subplots.geo||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!e[o]&&s&&(s.framework.remove(),s.clipDef.remove())}}}},{\"../../lib\":503,\"../../plots/get_data\":593,\"./geo\":588,\"./layout_attributes\":590,\"./layout_defaults\":591}],590:[function(t,e,r){\"use strict\";var n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../../components/drawing/attributes\").dash,o=t(\"./constants\"),s=t(\"../../plot_api/edit_types\").overrideAll,l=t(\"../../lib/sort_object_keys\"),c={range:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},showgrid:{valType:\"boolean\",dflt:!1},tick0:{valType:\"number\",dflt:0},dtick:{valType:\"number\"},gridcolor:{valType:\"color\",dflt:n.lightLine},gridwidth:{valType:\"number\",min:0,dflt:1},griddash:a};(e.exports=s({domain:i({name:\"geo\"},{}),fitbounds:{valType:\"enumerated\",values:[!1,\"locations\",\"geojson\"],dflt:!1,editType:\"plot\"},resolution:{valType:\"enumerated\",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:\"enumerated\",values:l(o.scopeDefaults),dflt:\"world\"},projection:{type:{valType:\"enumerated\",values:l(o.projNames)},rotation:{lon:{valType:\"number\"},lat:{valType:\"number\"},roll:{valType:\"number\"}},tilt:{valType:\"number\",dflt:0},distance:{valType:\"number\",min:1.001,dflt:2},parallels:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},scale:{valType:\"number\",min:0,dflt:1}},center:{lon:{valType:\"number\"},lat:{valType:\"number\"}},visible:{valType:\"boolean\",dflt:!0},showcoastlines:{valType:\"boolean\"},coastlinecolor:{valType:\"color\",dflt:n.defaultLine},coastlinewidth:{valType:\"number\",min:0,dflt:1},showland:{valType:\"boolean\",dflt:!1},landcolor:{valType:\"color\",dflt:o.landColor},showocean:{valType:\"boolean\",dflt:!1},oceancolor:{valType:\"color\",dflt:o.waterColor},showlakes:{valType:\"boolean\",dflt:!1},lakecolor:{valType:\"color\",dflt:o.waterColor},showrivers:{valType:\"boolean\",dflt:!1},rivercolor:{valType:\"color\",dflt:o.waterColor},riverwidth:{valType:\"number\",min:0,dflt:1},showcountries:{valType:\"boolean\"},countrycolor:{valType:\"color\",dflt:n.defaultLine},countrywidth:{valType:\"number\",min:0,dflt:1},showsubunits:{valType:\"boolean\"},subunitcolor:{valType:\"color\",dflt:n.defaultLine},subunitwidth:{valType:\"number\",min:0,dflt:1},showframe:{valType:\"boolean\"},framecolor:{valType:\"color\",dflt:n.defaultLine},framewidth:{valType:\"number\",min:0,dflt:1},bgcolor:{valType:\"color\",dflt:n.background},lonaxis:c,lataxis:c},\"plot\",\"from-root\")).uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../components/drawing/attributes\":387,\"../../lib/sort_object_keys\":526,\"../../plot_api/edit_types\":536,\"../domain\":584,\"./constants\":587}],591:[function(t,e,r){\"use 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d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var 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n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1124,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)\"skip\"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var s,l=o.getContext(\"2d\",{willReadFrequently:!0}),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(this._mockAxis,t)},e.exports=_},{\"../../../stackgl_modules\":1124,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/show_no_webgl_msg\":525,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"./layout/convert\":602,\"./layout/spikes\":605,\"./layout/tick_marks\":606,\"./project\":607,\"has-passive-events\":229,\"webgl-context\":331}],609:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],610:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"./animation_attributes\"),a=t(\"../components/color/attributes\"),o=t(\"../components/shapes/draw_newshape/attributes\"),s=t(\"./pad_attributes\"),l=t(\"../lib/extend\").extendFlat,c=n({editType:\"calc\"});c.family.dflt='\"Open Sans\", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:\"string\",editType:\"layoutstyle\"},font:n({editType:\"layoutstyle\"}),xref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},x:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"layoutstyle\"},y:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"left\",\"center\",\"right\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"layoutstyle\"},pad:l(s({editType:\"layoutstyle\"}),{}),editType:\"layoutstyle\"},uniformtext:{mode:{valType:\"enumerated\",values:[!1,\"hide\",\"show\"],dflt:!1,editType:\"plot\"},minsize:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"},autosize:{valType:\"boolean\",dflt:!1,editType:\"none\"},width:{valType:\"number\",min:10,dflt:700,editType:\"plot\"},height:{valType:\"number\",min:10,dflt:450,editType:\"plot\"},margin:{l:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},r:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},t:{valType:\"number\",min:0,dflt:100,editType:\"plot\"},b:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},autoexpand:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},computed:{valType:\"any\",editType:\"none\"},paper_bgcolor:{valType:\"color\",dflt:a.background,editType:\"plot\"},plot_bgcolor:{valType:\"color\",dflt:a.background,editType:\"layoutstyle\"},autotypenumbers:{valType:\"enumerated\",values:[\"convert types\",\"strict\"],dflt:\"convert types\",editType:\"calc\"},separators:{valType:\"string\",editType:\"plot\"},hidesources:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:{valType:\"boolean\",editType:\"legend\"},colorway:{valType:\"colorlist\",dflt:a.defaults,editType:\"calc\"},datarevision:{valType:\"any\",editType:\"calc\"},uirevision:{valType:\"any\",editType:\"none\"},editrevision:{valType:\"any\",editType:\"none\"},selectionrevision:{valType:\"any\",editType:\"none\"},template:{valType:\"any\",editType:\"calc\"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:\"any\",arrayOk:!0,editType:\"plot\"},transition:l({},i.transition,{editType:\"none\"}),_deprecated:{title:{valType:\"string\",editType:\"layoutstyle\"},titlefont:n({editType:\"layoutstyle\"})}}},{\"../components/color/attributes\":365,\"../components/shapes/draw_newshape/attributes\":451,\"../lib/extend\":493,\"./animation_attributes\":548,\"./font_attributes\":585,\"./pad_attributes\":618}],611:[function(t,e,r){\"use strict\";var n=t(\"../../lib/sort_object_keys\"),i='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',a=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',i].join(\" \"),o=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),s={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:i,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map 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t&&(-1!==p.styleValuesMapbox.indexOf(t)||0===t.indexOf(\"mapbox://\"))}r.name=\"mapbox\",r.attr=\"subplot\",r.idRoot=\"mapbox\",r.idRegex=r.attrRegex=i.counterRegex(\"mapbox\"),r.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots.mapbox;if(n.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(\"\"===t._context.mapboxAccessToken)return\"\";for(var n=[],a=[],o=!1,s=!1,l=0;l<e.length;l++){var c=r[e[l]],u=c.accesstoken;d(c.style)&&(u?i.pushUnique(n,u):(d(c._input.style)&&(i.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),u&&i.pushUnique(a,u)}if(s){var f=o?p.noAccessTokenErrorMsg:p.missingStyleErrorMsg;throw i.error(f),new Error(f)}return 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m=c.select(s._subplot.div);if(!(null===m.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var g=e._glimages.append(\"g\");g.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),g.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=m.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var b=u.bBox(x.node()),_=n.w*(h.x[1]-h.x[0]);if(b.width>_/2){var w=v.split(\"|\").join(\"<br>\");x.text(w).attr(\"data-unformatted\",w).call(f.convertToTspans,t),b=u.bBox(x.node())}x.attr(\"transform\",a(-3,8-b.height)),y.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var T=1;b.width+6>_&&(T=_/(b.width+6));var k=[n.l+n.w*h.x[1],n.t+n.h*(1-h.y[0])];y.attr(\"transform\",a(k[0],k[1])+o(T))}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../components/drawing\":388,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/get_data\":593,\"./constants\":611,\"./layout_attributes\":615,\"./layout_defaults\":616,\"./mapbox\":617,\"@plotly/d3\":58,\"mapbox-gl/dist/mapbox-gl-unminified\":239}],614:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var e=u(t);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},l.removeLayer=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"./constants\":611,\"./convert_text_opts\":612}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=t(\"./constants\"),f=o({});f.family.dflt=\"Open Sans Regular, Arial Unicode MS 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e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var 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O,z,D,B=g.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),N={element:B,gd:c};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),m.visible&&h<f,{transform:l(b,_)}),N.prepFn=function(){O=null,z=null,D=null,N.moveFn=j,N.doneFn=H,w(c)},N.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<S.MINDRAG&&(t=0,e=0),[t,e]},v.init(N)}function j(t,e){if(O)O(t,e);else{var n=[t,-e],a=[Math.cos(M),Math.sin(M)],s=Math.abs(o.dot(n,a)/Math.sqrt(o.dot(n,n)));isNaN(s)||(O=s<.5?q:G)}var l={};!function(t){null!==z?t[i.id+\".radialaxis.angle\"]=z:null!==D&&(t[i.id+\".radialaxis.range[\"+r+\"]\"]=D)}(l),c.emit(\"plotly_relayouting\",l)}function H(){null!==z?a.call(\"_guiRelayout\",c,i.id+\".radialaxis.angle\",z):null!==D&&a.call(\"_guiRelayout\",c,i.id+\".radialaxis.range[\"+r+\"]\",D)}function q(t,e){if(0!==r){var n=b+t,a=_+e;z=Math.atan2(d-a,n-p),i.vangles&&(z=U(z,i.vangles)),z=F(z);var o=l(p,d)+s(-z);u[\"radial-axis\"].attr(\"transform\",o),u[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above 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r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var 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r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),c=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[c]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(u.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":376,\"./label_defaults\":750}],757:[function(t,e,r){\"use strict\";var n=t(\"../heatmap/attributes\"),i=t(\"../contour/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=i.contours;e.exports=o({carpet:{valType:\"string\",editType:\"calc\"},z:n.z,a:n.x,a0:n.x0,da:n.dx,b:n.y,b0:n.y0,db:n.dy,text:n.text,hovertext:n.hovertext,transpose:n.transpose,atype:n.xtype,btype:n.ytype,fillcolor:i.fillcolor,autocontour:i.autocontour,ncontours:i.ncontours,contours:{type:s.type,start:s.start,end:s.end,size:s.size,coloring:{valType:\"enumerated\",values:[\"fill\",\"lines\",\"none\"],dflt:\"fill\",editType:\"calc\"},showlines:s.showlines,showlabels:s.showlabels,labelfont:s.labelfont,labelformat:s.labelformat,operation:s.operation,value:s.value,editType:\"calc\",impliedEdits:{autocontour:!1}},line:{color:i.line.color,width:i.line.width,dash:i.line.dash,smoothing:i.line.smoothing,editType:\"plot\"},transforms:void 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n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":908}],789:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat;e.exports=u({z:{valType:\"data_array\",editType:\"calc\"},x:u({},n.x,{impliedEdits:{xtype:\"array\"}}),x0:u({},n.x0,{impliedEdits:{xtype:\"scaled\"}}),dx:u({},n.dx,{impliedEdits:{xtype:\"scaled\"}}),y:u({},n.y,{impliedEdits:{ytype:\"array\"}}),y0:u({},n.y0,{impliedEdits:{ytype:\"scaled\"}}),dy:u({},n.dy,{impliedEdits:{ytype:\"scaled\"}}),xperiod:u({},n.xperiod,{impliedEdits:{xtype:\"scaled\"}}),yperiod:u({},n.yperiod,{impliedEdits:{ytype:\"scaled\"}}),xperiod0:u({},n.xperiod0,{impliedEdits:{xtype:\"scaled\"}}),yperiod0:u({},n.yperiod0,{impliedEdits:{ytype:\"scaled\"}}),xperiodalignment:u({},n.xperiodalignment,{impliedEdits:{xtype:\"scaled\"}}),yperiodalignment:u({},n.yperiodalignment,{impliedEdits:{ytype:\"scaled\"}}),text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"data_array\",editType:\"calc\"},transpose:{valType:\"boolean\",dflt:!1,editType:\"calc\"},xtype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},ytype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},zsmooth:{valType:\"enumerated\",values:[\"fast\",\"best\",!1],dflt:!1,editType:\"calc\"},hoverongaps:{valType:\"boolean\",dflt:!0,editType:\"none\"},connectgaps:{valType:\"boolean\",editType:\"calc\"},xgap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},ygap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:s(),texttemplate:l({arrayOk:!1,editType:\"plot\"},{keys:[\"x\",\"y\",\"z\",\"text\"]}),textfont:a({editType:\"plot\",autoSize:!0,autoColor:!0,colorEditType:\"style\"}),showlegend:u({},i.showlegend,{dflt:!1})},{transforms:void 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W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../surface/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat;e.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],869:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../../stackgl_modules\").delaunay_triangulate,a=t(\"../../../stackgl_modules\").alpha_shape,o=t(\"../../../stackgl_modules\").convex_hull,s=t(\"../../lib/gl_format_color\").parseColorScale,l=t(\"../../lib/str2rgbarray\"),c=t(\"../../components/colorscale\").extractOpts,u=t(\"../../plots/gl3d/zip3\");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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n=t(\"../../lib\").extendFlat,i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/fx/attributes\"),l=t(\"../../constants/delta.js\"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function 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E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var m={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],896:[function(t,e,r){\"use strict\";var n=t(\"./base_index\");n.plot=t(\"./plot\"),e.exports=n},{\"./base_index\":890,\"./plot\":900}],897:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 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);\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var 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i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{\"../../lib\":503,\"./constants\":893,glslify:227}],898:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],899:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"color-rgba\"),s=t(\"../../plots/cartesian/axes\"),l=i.strRotate,c=i.strTranslate,u=t(\"../../lib/svg_text_utils\"),f=t(\"../../components/drawing\"),h=t(\"../../components/colorscale\"),p=t(\"../../lib/gup\"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t(\"./helpers\"),y=t(\"./constants\"),x=t(\"./axisbrush\"),b=t(\"./lines\");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top 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r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],904:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":901,\"fast-isnumeric\":190}],905:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],906:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],907:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":901,\"./base_plot\":902,\"./calc\":903,\"./defaults\":904,\"./layout_attributes\":908,\"./layout_defaults\":909,\"./plot\":910,\"./style\":911,\"./style_one\":912}],908:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],909:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":908}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"./render\"),s=t(\"../../components/fx\"),l=t(\"../../components/color\"),c=t(\"./constants\").cn,u=i._;function f(t){return\"\"!==t}function h(t,e){return t.filter((function(t){return t.key===e.traceId}))}function p(t,e){n.select(t).select(\"path\").style(\"fill-opacity\",e),n.select(t).select(\"rect\").style(\"fill-opacity\",e)}function d(t){n.select(t).select(\"text.name\").style(\"fill\",\"black\")}function m(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function g(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll(\".\"+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,\"source:\")+\" \",T=u(t,\"target:\")+\" \",k=u(t,\"concentration:\")+\" \",A=u(t,\"incoming flow count:\")+\" \",M=u(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||\"\",w+m.source.label,T+m.target.label,m.concentrationscale?k+a(\"%0.2f\")(m.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(m.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{\"../../components/color\":366,\"../../components/fx\":406,\"../../lib\":503,\"./constants\":920,\"./render\":924,\"@plotly/d3\":58}],924:[function(t,e,r){\"use strict\";var n=t(\"d3-force\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"@plotly/d3\"),o=t(\"@plotly/d3-sankey\"),s=t(\"@plotly/d3-sankey-circular\"),l=t(\"./constants\"),c=t(\"tinycolor2\"),u=t(\"../../components/color\"),f=t(\"../../components/drawing\"),h=t(\"../../lib\"),p=h.strTranslate,d=h.strRotate,m=t(\"../../lib/gup\"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t(\"../../lib/svg_text_utils\"),b=t(\"../../registry\"),_=t(\"../../constants/alignment\"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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g=\"projection.\"+d[m];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}var v=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");v(t,e,h||p||r,{axis:\"z\"}),v(t,e,h||p||r,{axis:\"y\",inherit:\"z\"}),v(t,e,h||p||r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":955}],960:[function(t,e,r){\"use 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../components/colorscale/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927}],962:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/line_shape_defaults\":942,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":961}],964:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],965:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],966:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":938}],967:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var 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M=p.font[r]={};M.size=(s.isTypedArray(f)?f[r]:Array.isArray(f)?n(f[r])?f[r]:0:f)*d,M.family=Array.isArray(h)?h[r]:h}else p.font={size:f*d,family:h};return p}function x(t){var e,r,n=t._length,i=t.marker,o={},l=s.isArrayOrTypedArray(i.symbol),c=s.isArrayOrTypedArray(i.color),f=s.isArrayOrTypedArray(i.line.color),d=s.isArrayOrTypedArray(i.opacity),m=s.isArrayOrTypedArray(i.size),g=s.isArrayOrTypedArray(i.line.width);if(l||(r=p.isOpenSymbol(i.symbol)),l||c||f||d){o.colors=new Array(n),o.borderColors=new Array(n);var v=u(i,i.opacity,n),y=u(i.line,i.opacity,n);if(!Array.isArray(y[0])){var x=y;for(y=Array(n),e=0;e<n;e++)y[e]=x}if(!Array.isArray(v[0])){var b=v;for(v=Array(n),e=0;e<n;e++)v[e]=b}for(o.colors=v,o.borderColors=y,e=0;e<n;e++){if(l){var _=i.symbol[e];r=p.isOpenSymbol(_)}r&&(y[e]=v[e].slice(),v[e]=v[e].slice(),v[e][3]=0)}o.opacity=t.opacity}else 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=y(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.isDotSymbol(t);return M[t]?M[t]:(e=c&&!o?a(1.1*k)+S:a(k),r=i(e,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:s?A:-A}),M[t]=r,r||null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{\"../../components/drawing\":388,\"../../components/fx/helpers\":402,\"../../constants/interactions\":478,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../scatter/make_bubble_size_func\":944,\"../scatter/subtypes\":952,\"./constants\":982,\"./helpers\":987,\"color-normalize\":89,\"fast-isnumeric\":190,\"svg-path-sdf\":310}],984:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/period_defaults\":947,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"../scatter/xy_defaults\":954,\"./attributes\":979,\"./helpers\":987}],985:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],986:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":936}],987:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":982}],988:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 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_=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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R=y.selectBatch[I]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<O;g++)F[g]||B.push(g);y.unselectBatch[I]=B}var N=P.xpx=new Array(O),j=P.ypx=new Array(O);for(g=0;g<O;g++)N[g]=x.c2p(z[g]),j[g]=b.c2p(D[g])}else P.xpx=P.ypx=null}if(S){if(y.select2d||(y.select2d=n(v._glcanvas.data()[1].regl)),y.scatter2d){var U=new Array(_);for(m=0;m<_;m++)U[m]=y.selectBatch[m].length||y.unselectBatch[m].length?y.markerUnselectedOptions[m]:{};y.scatter2d.update(U)}y.select2d&&(y.select2d.update(y.markerOptions),y.select2d.update(y.markerSelectedOptions)),y.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else y.scatter2d&&y.scatter2d.update(y.markerOptions);var V={viewport:d(v,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},H=s.repeat(V,y.count);y.fill2d&&y.fill2d.update(H),y.line2d&&y.line2d.update(H),y.error2d&&y.error2d.update(H.concat(H)),y.scatter2d&&y.scatter2d.update(H),y.select2d&&y.select2d.update(H),y.glText&&y.glText.forEach((function(t){t.update(V)}))}else y.init()}}).reglPrecompiled=p},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":943,\"../scatter/subtypes\":952,\"./edit_style\":985,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],991:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":503}],992:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{\"../scatter/subtypes\":952,\"./edit_style\":985}],993:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/mapbox/layout_attributes\":615,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927,\"../scattergeo/attributes\":969}],994:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b=\"none\"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&\"circle\"===a.marker.symbol,A=w&&\"circle\"!==a.marker.symbol,M=m(),S=m(),E=m(),L=m(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"fill-color\":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",i.extendFlat(S.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),m=i.isArrayOrTypedArray(f.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var 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n=t(\"../scatterpolar/attributes\"),i=t(\"../scattergl/attributes\"),a=t(\"../../plots/template_attributes\").texttemplateAttrs;e.exports={mode:n.mode,r:n.r,theta:n.theta,r0:n.r0,dr:n.dr,theta0:n.theta0,dtheta:n.dtheta,thetaunit:n.thetaunit,text:n.text,texttemplate:a({editType:\"plot\"},{keys:[\"r\",\"theta\",\"text\"]}),hovertext:n.hovertext,hovertemplate:n.hovertemplate,line:i.line,connectgaps:i.connectgaps,marker:i.marker,fill:i.fill,fillcolor:i.fillcolor,textposition:i.textposition,textfont:i.textfont,hoverinfo:n.hoverinfo,selected:n.selected,unselected:n.unselected}},{\"../../plots/template_attributes\":633,\"../scattergl/attributes\":979,\"../scatterpolar/attributes\":1002}],1010:[function(t,e,r){\"use 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A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":906,\"../pie/plot\":910,\"./constants\":1052,\"./fx\":1054,\"./helpers\":1055,\"./style\":1060,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1060:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1061:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1062:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../../stackgl_modules\":1124,\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../lib/str2rgbarray\":528,\"../heatmap/find_empties\":798,\"../heatmap/interp2d\":801}],1064:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:2}],34:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],35:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{\"./lib/intersect\":37,\"./lib/sweep\":41,\"typedarray-pool\":308}],36:[function(t,e,r){\"use strict\";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=c[e+y+t],_=u[v];if(!(b<d||m<x)){for(var w=e+1;w<t;++w){var T=a[w+p],k=a[w+t+p],A=c[w+y],M=c[w+t+y];if(k<A||M<T)continue t}var S=r(g,_);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u):function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=s,p=f*s;h<l;++h,p+=f){var d=c[e+p],m=c[e+p+t],g=u[h];t:for(var v=n,y=f*n;v<i;++v,y+=f){var x=a[e+y],b=a[e+y+t],_=o[v];if(!(m<x||b<d)){for(var w=e+1;w<t;++w){var T=a[w+y],k=a[w+t+y],A=c[w+p],M=c[w+t+p];if(k<A||M<T)continue t}var S=r(_,g);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u)}:function(t,e,r,n,i,a,o,s,l,c,u,f){return a-i>c-l?n?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=u[v];if(!(x<=d||m<x)){for(var _=e+1;_<t;++_){var w=a[_+p],T=a[_+t+p],k=c[_+y],A=c[_+t+y];if(T<k||A<w)continue t}var M=r(b,g);if(void 0!==M)return 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e=i.nextPow2(t);o.length<e&&(n.free(o),o=n.mallocInt32(e));s.length<e&&(n.free(s),s=n.mallocInt32(e));l.length<e&&(n.free(l),l=n.mallocInt32(e));c.length<e&&(n.free(c),c=n.mallocInt32(e));u.length<e&&(n.free(u),u=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;h.length<r&&(n.free(h),h=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,i,u,f,m,g,v){for(var y=0,x=2*t,b=t-1,_=x-1,w=r;w<n;++w){var T=u[w],k=x*w;h[y++]=i[k+b],h[y++]=-(T+1),h[y++]=i[k+_],h[y++]=T}for(w=f;w<m;++w){T=v[w]+(1<<28);var A=x*w;h[y++]=g[A+b],h[y++]=-T,h[y++]=g[A+_],h[y++]=T}var M=y>>>1;a(h,M);var S=0,E=0;for(w=0;w<M;++w){var L=0|h[2*w+1];if(L>=1<<28)p(l,c,E--,L=L-(1<<28)|0);else if(L>=0)p(o,s,S--,L);else if(L<=-(1<<28)){L=-L-(1<<28)|0;for(var C=0;C<S;++C){if(void 0!==(P=e(o[C],L)))return P}d(l,c,E++,L)}else{L=-L-1|0;for(C=0;C<E;++C){var P;if(void 0!==(P=e(L,l[C])))return P}d(o,s,S++,L)}}},sweepComplete:function(t,e,r,n,i,m,g,v,y,x){for(var b=0,_=2*t,w=t-1,T=_-1,k=r;k<n;++k){var 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e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function m(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORM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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":129,\"gl-buffer\":78,\"gl-shader\":132}],131:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":32,\"gl-fbo\":86,ndarray:259,\"typedarray-pool\":308}],132:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var 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w=c[y];if(w.type.indexOf(\"mat\")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+\"[\"+A+\"]\"),\"number\"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&\"number\"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),\"number\"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,\"uniforms\",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=m(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=g(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var 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-       "/*!\n",
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-       " * @license  MIT\n",
-       " */\n",
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l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var m=i[2],g=i[6],v=i[10],y=m*a+g*o+v*s,x=m*u+g*f+v*h,b=l(m-=y*a+x*u,g-=y*o+x*f,v-=y*s+x*h);m/=b,g/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],m=i[10],g=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-m*v),b=-(m*g-p*y),_=-(p*v-d*g),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=m;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return 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-       "/*!\n",
-       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
-       " *\n",
-       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
-       " * Licensed under the MIT license.\n",
-       " */\n",
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e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return 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-       "            window._Plotly = Plotly;\n",
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+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qc3FaMEFDWwh"
+      },
+      "source": [
+        "#### Local files\n",
+        "Make sure the following files are available in the directory of the notebook:\n",
+        "* `tutorial_utils.py` - a collection of utility functions used throughout this notebook, explained in Section [3.2](#classification)\n",
+        "* `NHTSA_NMVCCS_extract.parquet.gzip` - the data\n",
+        "\n",
+        "This notebook will create the following subdirectories:\n",
+        "* `datasets` - pre-processed datasets\n",
+        "* `models` - trained Transformer models\n",
+        "* `results` - figures and Excel files"
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-       "                            \n",
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+      "source": [
+        "#### Getting started with Python and Jupyter Notebook\n",
+        "\n",
+        "For this tutorial, we assume that you are already familiar with Python and Jupyter Notebook.\n",
+        "\n",
+        "In this section, Jupyter Notebook and Python settings are initialized.\n",
+        "For code in Python, the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/)\n",
+        "(\"PEP = Python Enhancement Proposal\") is enforced with minor variations to improve readability.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = px.bar(df[\"NUMTOTV\"].value_counts().sort_index(), width=640)\n",
-    "fig.update_layout(title=\"number of cases by number of vehicles\", xaxis_title=\"number of vehicles\",\n",
-    "                  yaxis_title=\"number of cases\")\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"num_vehicles\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pwcfXawvWNvX"
-   },
-   "source": [
-    "Most cases involve two vehicles, and only very few accidents involve more than three vehicles.\n",
-    "\n",
-    "Each of the columns `WEATHER1` to `WEATHER8` indicates the presence of a specific weather condition\n",
-    "(1: weather condition present, 9999: presence of weather condition unknown, 0 otherwise):\n",
-    "\n",
-    "| column | meaning | count |\n",
-    "|---|---|---|\n",
-    "| `WEATHER1` | cloudy | 1112 |\n",
-    "| `WEATHER2` | snow | 114 |\n",
-    "| `WEATHER3` | fog, smog, smoke | 28 |\n",
-    "| `WEATHER4` | rain | 624 |\n",
-    "| `WEATHER5` | sleet, hail (freezing drizzle or rain) | 25 |\n",
-    "| `WEATHER6` | blowing snow | 38 |\n",
-    "| `WEATHER7` | severe crosswinds | 20 |\n",
-    "| `WEATHER8` | other | 25 |\n",
-    "\n",
-    "These weather conditions are not mutually exclusive, i.e., more than one condition can be present in a single case.\n",
-    "The frequency distribution looks as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 542
     },
-    "id": "DD17G3B9WNvY",
-    "outputId": "90ff1035-709a-4cd4-e677-a8f169105301",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "weather",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "E7grvuz8WNvQ",
+        "outputId": "d3dec457-8e5c-45a6-a9d9-264d8d10b127",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       },
-       "data": [
+      },
+      "outputs": [
         {
-         "alignmentgroup": "True",
-         "hovertemplate": "x=%{x}<br>y=%{y}<extra></extra>",
-         "legendgroup": "",
-         "marker": {
-          "color": "#636efa",
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-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };                });            </script>        </div>"
+      ],
+      "source": [
+        "# Notebook settings\n",
+        "\n",
+        "# clear the namespace variables\n",
+        "from IPython import get_ipython\n",
+        "get_ipython().run_line_magic(\"reset\", \"-sf\")\n",
+        "\n",
+        "# formatting: cell width\n",
+        "from IPython.display import display, HTML\n",
+        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig=px.bar(x=range(1,9), y=[(df[\"WEATHER\"+str(i)]==1).sum() for i in range(1,9)], width=640)\n",
-    "fig.update_layout(title=\"number of cases by weather condition\", xaxis_title=\"weather condition\",\n",
-    "                  yaxis_title=\"number of cases\")\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"weather\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gVwOVGouWNvY"
-   },
-   "source": [
-    "The most frequently recorded weather conditions are \"cloudy\" (`WEATHER1`) and \"rain\" (`WEATHER4`).\n",
-    "\n",
-    "`INJSEVA` indicates the most serious sustained injury in the accident.\n",
-    "For instance, if one person was not injured, and another person suffered a non-incapacitating injury,\n",
-    "injury class 2 was assigned to the case.\n",
-    "\n",
-    "Information on injury severity has been taken from police accident reports, which are not available in the data.\n",
-    "Unfortunately, this information does not necessarily align with the case description:\n",
-    "There are many cases for which the case description indicates the presence of an injury,\n",
-    "but `INJSEVA` does not, and vice versa.\n",
-    "\n",
-    "For this reason, we created manually an additional column `INJSEVB` based on the case description,\n",
-    "to indicate the presence of a (possible) bodily injury.\n",
-    "The table below shows the distribution of number of cases by the two variables.\n",
-    "\n",
-    "| `INJSEVA` | meaning | count | `INJSEVB`=0 | `INJSEVB`=1 \n",
-    "|---|---|---|---|---|\n",
-    "|  0 | O - No injury | 1'458 | 96| 1'554 |\n",
-    "|  1 | C - Possible injury | 1'112 | 1'298 | 2'410 |\n",
-    "|  2 | B - Non-incapacitating injury | 729 | 945 | 1'674 |\n",
-    "|  3 | A - Incapacitating injury | 304 | 373 | 677 |\n",
-    "|  4 | K - Killed | 5 | 114 | 119 |\n",
-    "|  5 | U - Injury, severity unknown | 44 | 122 | 166 |\n",
-    "|  6 | Died prior to crash  | 0 | 0| 0 |\n",
-    "|  9 | Unknown if injured  | 51 | 16 | 67 |\n",
-    "| 10 | No person in crash  | 1 | 0| 1 |\n",
-    "| 11 | No PAR (police accident report) obtained | 231 | 50 | 281 |\n",
-    "|**Total**| | **3'935** | **3'014**| **6'949**|\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Mi0yKfm2WNvZ"
-   },
-   "source": [
-    "Now we turn to the verbal accident descriptions.\n",
-    "First, we examine the length of the English texts, `SUMMARY_EN`.\n",
-    "To this end, we split the texts into words, with blank spaces as separator,\n",
-    "and show a box plot of the text length by number of vehicles involved in the accident:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 559
     },
-    "id": "70q6pUwuWNvZ",
-    "outputId": "9dcead0e-d1b9-46fa-dfef-dc4e29084a4e",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overall number of words by case summary: min 60, average 419, max 1248\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FGxBx9MVWNvS"
+      },
+      "source": [
+        "#### Importing Required Libraries\n",
+        "\n",
+        "The following libraries are required:"
+      ]
     },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "text_length",
-         "format": "svg"
-        }
-       },
-       "data": [
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PFZRb_nWZnG5",
+        "outputId": "805e7052-ed79-4aa4-8daa-3f2dec75c3b5"
+      },
+      "outputs": [
         {
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-         "marker": {
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-         },
-         "name": "",
-         "notched": false,
-         "offsetgroup": "",
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per case summary\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"boxmode\":\"group\",\"width\":640},                        {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"text_length\"}, \"responsive\": true}                    ).then(function(){\n",
-       "                            \n",
-       "var gd = document.getElementById('fb71270e-9d72-491a-89cd-ff715e1cae4f');\n",
-       "var x = new MutationObserver(function (mutations, observer) {{\n",
-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        if (!display || display === 'none') {{\n",
-       "            console.log([gd, 'removed!']);\n",
-       "            Plotly.purge(gd);\n",
-       "            observer.disconnect();\n",
-       "        }}\n",
-       "}});\n",
-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };                });            </script>        </div>"
+      ],
+      "source": [
+        "!pip install transformers[torch]"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# statistics of summary length\n",
-    "df[\"words per case summary\"] = df[\"SUMMARY_EN\"].str.split().apply(len)\n",
-    "print(f\"Overall number of words by case summary: min {df['words per case summary'].min()}, \"\n",
-    "      f\"average {df['words per case summary'].mean():.0f}, max {df['words per case summary'].max()}\")\n",
-    "fig = px.box(df, x=\"NUMTOTV\", y=\"words per case summary\", width=640)\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"text_length\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mrk7jvqtWNvZ"
-   },
-   "source": [
-    "Not surprisingly, the length of the descriptions correlates with the number of vehicles involved.\n",
-    "\n",
-    "The average length is above 400 words.\n",
-    "As we will see later in this notebook, this poses some challenges with the NLP models that we are using in this notebook,\n",
-    "because these are limited to text up to a length of 512 so-called \"tokens\" (vocabulary items).\n",
-    "Since a single word may be tokenized into more than one token, some accident descriptions will be truncated.\n",
-    "\n",
-    "Let's examine one of the English texts and its German translation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 138
     },
-    "id": "A-eL7RyAWNva",
-    "outputId": "f55d85be-49e2-4221-ff01-b6b1c4ba7c5a",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.  The posted speed limit on this roadway was 80 kmph (50 MPH). V1 entered the roadway by crossing over the two southbound lanes and then entering the third northbound lane, which was a left turn-only lane at a 4-way intersection. The driver of V1 intended to travel straight through the intersection, and so he began to change lanes to the right. He did not see V2, a 1994 Pontiac Grand Am, that was traveling in the second northbound lane. The northbound roadway had curved to the right prior to the private driveway that V1 had exited. As V1 began to change lanes to the right, the front of V1 contacted the left rear of V2 before coming to final rest on the roadway.\r",
-       " \r",
-       " The driver of V1 was a 60-year old male who reported that he had been traveling between 2-17 kmph (1-10 mph) prior to the crash.  He had no health-related problems, and had taken no medication prior to the crash.  He was rested and traveling back home.  He was wearing his prescribed lenses that corrected a myopic (nearsighted) condition.  He did not sustain any injuries from the crash and refused treatment.\r",
-       " \r",
-       " The Critical Precrash Event for the driver of V1 was when he began to travel over the lane line on the right side of the travel lane. The Critical Reason for the Critical Precrash Event was inadequate surveillance (failed to look, looked but did not see). Associated factors coded to the driver of V1 include an illegal use of a left turn lane (cited by police) and an unfamiliarity with the roadway. As per the driver of V1, this was the first time he had driven on this roadway.  \r",
-       " \r",
-       " The driver of V2 was a 28-year old woman who reported that she had been traveling between 66-80 kmph (41-50 mph) prior to the crash.  She had no health-related problems, and had taken no medication prior to the crash.  She was rested and on her way home.  She does not wear corrective lenses.  She sustained minor injuries and was transported to a local trauma facility.\r",
-       " \r",
-       " The Critical Precrash Event for the driver of V2 was when the other vehicle encroached into her lane, from an adjacent lane (same direction) over the left lane line.  The Critical Reason for the Critical Precrash Event was not coded to the driver of V2 and no associated factors were coded to her."
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HDmc_CXBZmQz",
+        "outputId": "c6634041-ec17-42a0-de9d-95d21682dc7b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
+            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
+            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting multiprocess (from datasets)\n",
+            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
+            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.16.4)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
+            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.2.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
+            "Successfully installed datasets-2.14.4 dill-0.3.7 multiprocess-0.70.15 xxhash-3.3.0\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "!pip install datasets"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(HTML(df.loc[0, \"SUMMARY_EN\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 156
     },
-    "id": "74OyQip6WNva",
-    "outputId": "8ba860d0-896b-45c9-bbf3-591576815eda",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.  Die zulässige Höchstgeschwindigkeit auf dieser Fahrbahn betrug 80 km/h (50 MPH). V1 fuhr auf die Fahrbahn, indem er die beiden Fahrspuren in Richtung Süden überquerte und dann auf die dritte Fahrspur in Richtung Norden einfuhr, die an einer Kreuzung mit vier Fahrspuren nur für Linksabbieger vorgesehen war. Der Fahrer von V1 beabsichtigte, geradeaus über die Kreuzung zu fahren, und begann daher, die Spur nach rechts zu wechseln. Dabei übersah er V2, einen Pontiac Grand Am von 1994, der auf der zweiten Fahrspur in Richtung Norden unterwegs war. Die Fahrbahn in nördlicher Richtung war vor der privaten Einfahrt, aus der V1 herausgefahren war, nach rechts gebogen. Als V1 begann, die Spur nach rechts zu wechseln, berührte die Front von V1 das linke Heck von V2, bevor er auf der Fahrbahn zum Stehen kam. Der Fahrer von V1 war ein 60-jähriger Mann, der angab, vor dem Unfall mit einer Geschwindigkeit von 2 bis 17 km/h unterwegs gewesen zu sein.  Er hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Er war ausgeruht und auf dem Weg nach Hause.  Er trug die ihm verschriebenen Kontaktlinsen, die eine Kurzsichtigkeit korrigieren.  Er zog sich bei dem Unfall keine Verletzungen zu und lehnte eine Behandlung ab. Das kritische Ereignis vor dem Unfall war für den Fahrer von V1, als er begann, die Fahrspurlinie auf der rechten Seite der Fahrbahn zu überfahren. Der kritische Grund für das kritische Ereignis vor dem Unfall war unzureichende Überwachung (nicht hingesehen, hingesehen, aber nicht gesehen). Zu den assoziierten Faktoren, die dem Fahrer von V1 zugeschrieben werden, gehören das illegale Benutzen einer Linksabbiegerspur (von der Polizei verwarnt) und die Unkenntnis der Fahrbahn. Für den Fahrer von V1 war es das erste Mal, dass er diese Fahrbahn befuhr.  \r",
-       " \r",
-       " Bei der Fahrerin von V2 handelte es sich um eine 28-jährige Frau, die angab, vor dem Unfall mit einer Geschwindigkeit von 66-80 km/h unterwegs gewesen zu sein.  Sie hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Sie war ausgeruht und befand sich auf dem Heimweg.  Sie trägt keine Korrekturgläser.  Sie erlitt leichte Verletzungen und wurde in eine örtliche Unfallklinik gebracht. Das kritische Ereignis vor dem Unfall war für die Fahrerin von V2, als das andere Fahrzeug von einer benachbarten Fahrspur (gleiche Richtung) über die linke Fahrspurlinie in ihre Spur eindrang.  Der kritische Grund für das kritische Vorunfallereignis wurde der Fahrerin von V2 nicht zugeordnet, und es wurden ihr keine zugehörigen Faktoren zugeordnet."
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fap9tsxwa5QR",
+        "outputId": "aeedeeb5-7779-4ed8-f34e-6e0d3526c601"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting transformers_interpret\n",
+            "  Downloading transformers_interpret-0.10.0-py3-none-any.whl (45 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/45.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.8/45.8 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting captum>=0.3.1 (from transformers_interpret)\n",
+            "  Downloading captum-0.6.0-py3-none-any.whl (1.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: ipython<8.0.0,>=7.31.1 in /usr/local/lib/python3.10/dist-packages (from transformers_interpret) (7.34.0)\n",
+            "Requirement already satisfied: transformers>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from transformers_interpret) (4.32.1)\n",
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+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from captum>=0.3.1->transformers_interpret) (1.23.5)\n",
+            "Requirement already satisfied: torch>=1.6 in /usr/local/lib/python3.10/dist-packages (from captum>=0.3.1->transformers_interpret) (2.0.1+cu118)\n",
+            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (67.7.2)\n",
+            "Collecting jedi>=0.16 (from ipython<8.0.0,>=7.31.1->transformers_interpret)\n",
+            "  Downloading jedi-0.19.0-py2.py3-none-any.whl (1.6 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m71.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (4.4.2)\n",
+            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (0.7.5)\n",
+            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (5.7.1)\n",
+            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (3.0.39)\n",
+            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (2.16.1)\n",
+            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (0.2.0)\n",
+            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (0.1.6)\n",
+            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython<8.0.0,>=7.31.1->transformers_interpret) (4.8.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (3.12.2)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (0.16.4)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (23.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (2023.6.3)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (2.31.0)\n",
+            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (0.13.3)\n",
+            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (0.3.3)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->transformers_interpret) (4.66.1)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers>=3.0.0->transformers_interpret) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers>=3.0.0->transformers_interpret) (4.7.1)\n",
+            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<8.0.0,>=7.31.1->transformers_interpret) (0.8.3)\n",
+            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<8.0.0,>=7.31.1->transformers_interpret) (0.7.0)\n",
+            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython<8.0.0,>=7.31.1->transformers_interpret) (0.2.6)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->captum>=0.3.1->transformers_interpret) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->captum>=0.3.1->transformers_interpret) (3.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->captum>=0.3.1->transformers_interpret) (3.1.2)\n",
+            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->captum>=0.3.1->transformers_interpret) (2.0.0)\n",
+            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6->captum>=0.3.1->transformers_interpret) (3.27.2)\n",
+            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6->captum>=0.3.1->transformers_interpret) (16.0.6)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (4.42.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (1.4.4)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->captum>=0.3.1->transformers_interpret) (2.8.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=3.0.0->transformers_interpret) (3.2.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=3.0.0->transformers_interpret) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=3.0.0->transformers_interpret) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=3.0.0->transformers_interpret) (2023.7.22)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->captum>=0.3.1->transformers_interpret) (1.16.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6->captum>=0.3.1->transformers_interpret) (2.1.3)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6->captum>=0.3.1->transformers_interpret) (1.3.0)\n",
+            "Installing collected packages: jedi, captum, transformers_interpret\n",
+            "Successfully installed captum-0.6.0 jedi-0.19.0 transformers_interpret-0.10.0\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "!pip install transformers_interpret"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(HTML(df.loc[0, \"SUMMARY_GE\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2Gb03bROWNva"
-   },
-   "source": [
-    "To get an impression of the most frequent words, we generate a simple word cloud form all English case descriptions.\n",
-    "By default, the word cloud excludes so-called stop words (such as articles, prepositions, pronouns, conjunctions, etc.),\n",
-    "which are the most common words and do not add much information to the text."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 542
     },
-    "id": "fUhYGO10WNvb",
-    "outputId": "12256faa-ccc0-4689-d1e3-84e69bc35dda",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "word_cloud",
-         "format": "svg"
-        }
-       },
-       "data": [
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PzHnOM3JZq7G",
+        "outputId": "5b67ae59-6d9e-4442-a2e6-6eaf1abe0529"
+      },
+      "outputs": [
         {
-         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: [%{z[0]}, %{z[1]}, %{z[2]}]<extra></extra>",
-         "name": "0",
-         "source": 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                       {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"word_cloud\"}, \"responsive\": true}                    ).then(function(){\n",
-       "                            \n",
-       "var gd = document.getElementById('f6e8e61e-e539-4ac1-bc37-083f2eb78c5c');\n",
-       "var x = new MutationObserver(function (mutations, observer) {{\n",
-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        if (!display || display === 'none') {{\n",
-       "            console.log([gd, 'removed!']);\n",
-       "            Plotly.purge(gd);\n",
-       "            observer.disconnect();\n",
-       "        }}\n",
-       "}});\n",
-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };                });            </script>        </div>"
+      ],
+      "source": [
+        "!pip install plotly"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text = df[\"SUMMARY_EN\"].str.cat(sep=\" \")\n",
-    "\n",
-    "# Create and generate a word cloud image:\n",
-    "word_cloud = WordCloud(max_words=100, background_color=\"white\").generate(text)\n",
-    "\n",
-    "# Display the generated image:\n",
-    "fig = px.imshow(word_cloud, width=640)\n",
-    "fig.update_layout(xaxis_showticklabels=False, yaxis_showticklabels=False)\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"word_cloud\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "n8NkwCGKWNvb"
-   },
-   "source": [
-    "<a id='huggingface'></a>\n",
-    "<a name='huggingface'></a>\n",
-    "## 2.&nbsp;A Brief Introduction to the HuggingFace Ecosystem\n",
-    "\n",
-    "This tutorial uses NLP models provided by [*HuggingFace*](https://huggingface.co/).\n",
-    "\n",
-    "HuggingFace is a community that builds, trains and deploys state-of-the-art models for natural language processing,\n",
-    "audio, computer vision etc. HuggingFace's model hub provides thousands of pre-trained models for these applications.\n",
-    "The [Transformers](https://huggingface.co/docs/transformers/index) library offers functionality to\n",
-    "quickly download and use those pre-trained models on a given input, fine-tune them on the own datasets\n",
-    "and then share them with the community.\n",
-    "The library is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow.\n",
-    "\n",
-    "In this notebook, the following elements of the HuggingFace ecosystem will be used:\n",
-    "\n",
-    "* datasets – a library to load and process inputs and outputs of the NLP model\n",
-    "* tokenizers – translating the raw input text into tokens, which are the vocabulary items of a given NLP model\n",
-    "* models – loading and saving models\n",
-    "* trainer - training of models, making predictions\n",
-    "\n",
-    "In the next sections we will briefly explore the first three components in turn.\n",
-    "The trainer functionality will be used in [Section 4](#finetuning) of this notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "iBkzTtL6WNvb"
-   },
-   "source": [
-    "<a id='dataset'></a>\n",
-    "<a name='dataset'></a>\n",
-    "### 2.1. Loading the Data into a Dataset\n",
-    "\n",
-    "[*Datasets*](https://huggingface.co/docs/datasets/) is a library for easily accessing and sharing datasets,\n",
-    "and evaluation of metrics for NLP, computer vision, and audio tasks.\n",
-    "\n",
-    "A dataset can be loaded in a single line of code, in our case directly from the pandas DataFrame.\n",
-    "At the same time, we split the dataset into a training (80%) and a test dataset (20%).\n",
-    "We fix the random seed for the sake of reproducibility."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "id": "g5EUHu8NWNvc",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "xJQZP6vFWNvc"
-   },
-   "source": [
-    "Since the texts are relatively long, some parts of this notebook require computing resources. Uncomment the following line to reduce the size of the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "Nw7H4keYWNvc",
-    "outputId": "2e1aa222-0c72-43b4-ed5e-c28780139651"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
-      "        num_rows: 5559\n",
-      "    })\n",
-      "    test: Dataset({\n",
-      "        features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
-      "        num_rows: 1390\n",
-      "    })\n",
-      "})\n"
-     ]
-    }
-   ],
-   "source": [
-    "# dataset = DatasetDict({\"train\": dataset[\"train\"].select(range(1000)), \"test\": dataset[\"train\"].select(range(250))})\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "GEYz-kTWWNvc"
-   },
-   "source": [
-    "The resulting `DatasetDict` behaves like a Python dictionary.\n",
-    "Therefore, you can access the `Dataset` corresponding to each split by"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "XJZ211STWNvd",
-    "outputId": "1cce6d42-a64e-4b5a-9b0f-e92e29d92223",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset({\n",
-      "    features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
-      "    num_rows: 5559\n",
-      "})\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_train = dataset[\"train\"]\n",
-    "print(ds_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RL4dLtZ5WNvd"
-   },
-   "source": [
-    "The `Dataset` object behaves like a normal Python container.\n",
-    "You can query its length, get rows or columns, etc. For instance, its length is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "V5KYuXD7WNvd",
-    "outputId": "1b72a9e6-135e-474e-9f2f-e17aa486114e",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5559"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cFmU2CO5Zxe-",
+        "outputId": "eb908e7d-8147-4661-a391-7e59a086d4d6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting kaleido\n",
+            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: kaleido\n",
+            "Successfully installed kaleido-0.2.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install kaleido"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(ds_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rYPGO6cBWNvd"
-   },
-   "source": [
-    "To query a single row, you can use its index, like in a list: `ds_train[0]`.\n",
-    "This returns a dictionary representing the row.\n",
-    "Its elements can be accessed by the column names as keys,\n",
-    "e.g. `ds_train[0][\"SCASEID\"]`.\n",
-    "Multiple rows can be accessed by index slices, e.g. `dataset[\"train\"][:2]`,\n",
-    "or by a list of indices, e.g. `dataset[\"train\"][0, 2]`.\n",
-    "\n",
-    "You can list the column names and get their detailed types (called features):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "m6RObDO1WNve",
-    "outputId": "489cbfbb-0a6f-4352-982c-e0f23f13088c",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'level_0': Value(dtype='int64', id=None),\n",
-       " 'index': Value(dtype='int64', id=None),\n",
-       " 'SCASEID': Value(dtype='int64', id=None),\n",
-       " 'SUMMARY_EN': Value(dtype='string', id=None),\n",
-       " 'SUMMARY_GE': Value(dtype='string', id=None),\n",
-       " 'INJSEVA': Value(dtype='int64', id=None),\n",
-       " 'NUMTOTV': Value(dtype='int64', id=None),\n",
-       " 'WEATHER1': Value(dtype='int64', id=None),\n",
-       " 'WEATHER2': Value(dtype='int64', id=None),\n",
-       " 'WEATHER3': Value(dtype='int64', id=None),\n",
-       " 'WEATHER4': Value(dtype='int64', id=None),\n",
-       " 'WEATHER5': Value(dtype='int64', id=None),\n",
-       " 'WEATHER6': Value(dtype='int64', id=None),\n",
-       " 'WEATHER7': Value(dtype='int64', id=None),\n",
-       " 'WEATHER8': Value(dtype='int64', id=None),\n",
-       " 'INJSEVB': Value(dtype='int64', id=None),\n",
-       " 'words per case summary': Value(dtype='int64', id=None)}"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "_z92uLSKWNvS",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import Dataset, DatasetDict, load_from_disk\n",
+        "from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, trainer_utils, AutoModelForMaskedLM,\\\n",
+        "    DataCollatorForLanguageModeling, AutoModelForSequenceClassification, pipeline\n",
+        "from transformers_interpret import SequenceClassificationExplainer\n",
+        "import torch\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from scipy.special import softmax\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.dummy import DummyClassifier\n",
+        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score\n",
+        "import plotly.express as px\n",
+        "from wordcloud import WordCloud\n",
+        "\n",
+        "from tutorial_utils import extract_sequence_encoding, get_xy, dummy_classifier, logistic_regression_classifier, evaluate_classifier"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_train.features"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yWaiHGOCWNve"
-   },
-   "source": [
-    "Later in this tutorial we will get to know methods to process datasets,\n",
-    "such as filtering the rows based on conditions, and processing the data in each row.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZTm6W4KUWNve"
-   },
-   "source": [
-    "<a id='tokenize'></a>\n",
-    "<a name='tokenize'></a>\n",
-    "### 2.2 Tokenization: Split Raw Text into Vocabulary Items\n",
-    "\n",
-    "Next, we convert the summary texts into tokens,\n",
-    "i.e., the text strings are split into elements of the vocabulary of the NLP model.\n",
-    "\n",
-    "As such, the tokenizer and the NLP model need to be aligned.\n",
-    "Changing the tokenizer after training the model would produce unpredictable results.\n",
-    "\n",
-    "Let's start with the model\n",
-    "[`distilbert-base-multilingual-cased`](https://huggingface.co/distilbert-base-multilingual-cased).\n",
-    "As the name implies, this model is cased: it does make a difference between \"english\" and \"English\".\n",
-    "\n",
-    "The model is trained on the concatenation of Wikipedia in 104 different languages listed\n",
-    "[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).\n",
-    "The model has 6 layers, 768 dimensions and 12 heads, totalizing 134 million parameters.\n",
-    "This model is a distilled version of the\n",
-    "[BERT base multilingual model](https://huggingface.co/bert-base-multilingual-cased)\n",
-    "which has 177 million parameters.\n",
-    "On average, the distilled model is twice as fast as the original model.\n",
-    "\n",
-    "**If you want to use another model throughout this notebook, please feel free to simply change the following line!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "Irl_VYLSWNve",
-    "outputId": "bff2c06d-4edf-43e8-ff41-f7607bba108f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c41d4c566c594fa489612f9f492e0806",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ut5VN51xWNvT"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
+      "source": [
+        "In addition, we require `openpyxl` to enable export from Pandas to Excel."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27ba00051803455ba86df9670ec177c4",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u8MaU-VkWNvU"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/466 [00:00<?, ?B/s]"
+      "source": [
+        "<a id='dataexploration'></a>\n",
+        "<a name='dataexploration'></a>\n",
+        "### 1.2. Exploring the Data\n",
+        "\n",
+        "The data used throughout this tutorial is derived from data of a vehicle crash causation study performed\n",
+        "in the United States from 2005 to 2007.\n",
+        "The dataset has almost 7'000 records, each relating to one accident.\n",
+        "For each case, a verbal description of the accident is available in English,\n",
+        "which summarizes road and weather conditions,\n",
+        "vehicles, drivers and passengers involved, preconditions, injury severities, etc.\n",
+        "The same information is also encoded in tabular form,\n",
+        "so that we can apply supervised learning techniques to train the NLP models and\n",
+        "compare the information extracted from the verbal descriptions with the encoded data.\n",
+        "\n",
+        "The original data consists of multiple tables. For this tutorial, we have aggregated it into a single dataset\n",
+        "and added German translations of the English accident descriptions.\n",
+        "The translations were generated using the new\n",
+        "[DeepL python API](https://pypi.org/project/deepl/).\n",
+        "\n",
+        "To explore the data, let's load it into a Pandas DataFrame and examine its shape, columns and data types:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0fc13388a3db46259b6e2c9d034067ed",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RJDyLey0WNvU",
+        "outputId": "ad5d39e6-986e-4884-d738-f1a4b745813a",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/972k [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "shape of DataFrame: (6949, 16)\n",
+            "('level_0', dtype('int64'))\n",
+            "('index', dtype('int64'))\n",
+            "('SCASEID', dtype('int64'))\n",
+            "('SUMMARY_EN', dtype('O'))\n",
+            "('SUMMARY_GE', dtype('O'))\n",
+            "('INJSEVA', dtype('int64'))\n",
+            "('NUMTOTV', dtype('int64'))\n",
+            "('WEATHER1', dtype('int64'))\n",
+            "('WEATHER2', dtype('int64'))\n",
+            "('WEATHER3', dtype('int64'))\n",
+            "('WEATHER4', dtype('int64'))\n",
+            "('WEATHER5', dtype('int64'))\n",
+            "('WEATHER6', dtype('int64'))\n",
+            "('WEATHER7', dtype('int64'))\n",
+            "('WEATHER8', dtype('int64'))\n",
+            "('INJSEVB', dtype('int64'))\n"
+          ]
+        }
+      ],
+      "source": [
+        "df = pd.read_parquet(\"NHTSA_NMVCCS_extract.parquet.gzip\")\n",
+        "print(f\"shape of DataFrame: {df.shape}\")\n",
+        "print(*list(zip(df.columns, df.dtypes)), sep=\"\\n\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01285c00184b47b6b6f7da75d8e1e5d5",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k2Ee4mjtWNvW"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/1.87M [00:00<?, ?B/s]"
+      "source": [
+        "The column `SCASEID` is a unique case identifier.\n",
+        "\n",
+        "The columns `SUMMARY_EN` and `SUMMARY_GE` are strings representing the verbal descriptions of the accident\n",
+        "in English and German, respectively.\n",
+        "\n",
+        "`NUMTOTV` is the number of vehicles involved in the case. Let's have a look at the distribution of this feature:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokenizer vocab_size: 119547\n",
-      "Tokenizer model_max_length (maximum context size): 512\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name = \"distilbert-base-multilingual-cased\"\n",
-    "\n",
-    "# load tokenizer\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "print(f\"Tokenizer vocab_size: {tokenizer.vocab_size}\")\n",
-    "print(f\"Tokenizer model_max_length (maximum context size): {tokenizer.model_max_length}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "O0vvS4YIWNvf"
-   },
-   "source": [
-    "As we can see, the tokenizer has a vocabulary of size 119'547.\n",
-    "The maximum sequence length of the model is 512 tokens.\n",
-    "\n",
-    "To see the tokenizer in action, we tokenize the first sentence of an accident description:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "id": "x8B8xjQMWNvf",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "text = \"V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.\"\n",
-    "result = tokenizer(text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kIlaAMtDWNvf"
-   },
-   "source": [
-    "Calling the tokenizer returns a `BatchEncoding` object,\n",
-    "which behaves just like a standard Python dictionary that holds input items used by the NP model.\n",
-    "`input_ids` is the list of token IDs for each token.\n",
-    "`attention_mask` is a list containing 1 for all elements that corresponds to tokens of the input text,\n",
-    "and 0 for padding tokens that are appended to attain a specified sequence length.\n",
-    "\n",
-    "To illustrate the meaning of the input IDs, we convert them back to token strings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "qrGAeN6tWNvf",
-    "outputId": "ad77c2f7-aaf3-4323-d2a7-134c45f4c933",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 159, 10759, 117, 169, 10180, 23986, 46917, 24408, 25103, 12955, 117, 11019, 169, 12153, 18923, 10188, 169, 14591, 23806, 14132, 31095, 169, 12756, 47755, 126, 118, 23636, 10551, 118, 13170, 117, 36796, 28438, 27015, 15485, 14132, 10135, 169, 12935, 32049, 21958, 119, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
-      "['[CLS]', 'V', '##1', ',', 'a', '2000', 'Pont', '##iac', 'Montana', 'mini', '##van', ',', 'made', 'a', 'left', 'turn', 'from', 'a', 'private', 'drive', '##way', 'onto', 'a', 'north', '##bound', '5', '-', 'lane', 'two', '-', 'way', ',', 'dry', 'asp', '##halt', 'road', '##way', 'on', 'a', 'down', '##hill', 'grade', '.', '[SEP]']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result)\n",
-    "print(tokenizer.convert_ids_to_tokens(result[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6iRVhlrDWNvf",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We observe that words like \"V1\", \"Pontiac\", \"minivan\", \"driveway\" etc. are split into multiple tokens each.\n",
-    "This is typical for WordPiece tokenization adopted by BERT, an approach designed to reduce vocabulary size.\n",
-    "This tokenizer marks sub-words by the prefix `##`.\n",
-    "\n",
-    "It is interesting to note that `2000` is a separate element of the vocabulary.\n",
-    "\n",
-    "The first and last tokens of the tokenized sequence are `CLS` and `SEP`, respectively.\n",
-    "* `CLS` stands for \"classification\".\n",
-    "The output of the BERT encoder corresponding to this input token is sometimes interpreted to represent the meaning of\n",
-    "the entire sequence (we will check this in [Section 3.2](#classification) of this notebook).\n",
-    "* `SEP` stands for \"separation\".\n",
-    "In next-sequence prediction tasks, it is used to separate the first from the second sequence.\n",
-    "\n",
-    "Here is a list of other special tokens used by the BERT tokenizer:\n",
-    "* The `UNK` token is used to represent tokens that are not available in the dictionary.\n",
-    "* The `PAD` token is used to pad the length of the tokenized sequence to a fixed length.\n",
-    "A fixed length is required when multiple sequences of different length are tokenized and fed into a BERT model\n",
-    "at the same time.\n",
-    "* The `MASK` token is used for pre-training the BERT model by masked language modeling.\n",
-    "For this task, the model is used to predict the masked token."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "e7HqEAAUWNvg",
-    "outputId": "c3057ba2-3877-4b6d-85b0-a4b849be200f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokenizer special_tokens_map: {'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Tokenizer special_tokens_map: {tokenizer.special_tokens_map}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "PwVPgPCJWNvg"
-   },
-   "source": [
-    "It is instructive to look at the tokenization of the German translation of the same text:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "6r2vF-hyWNvg",
-    "outputId": "1995c270-253a-4f51-d715-1bfafb819019",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 159, 10759, 117, 10290, 32930, 12955, 10118, 73879, 23986, 46917, 24408, 10441, 10268, 11218, 10180, 117, 66298, 10166, 10599, 73655, 12210, 25131, 10496, 23608, 10329, 10359, 11615, 54609, 13091, 10525, 117, 42169, 21181, 10112, 10882, 37590, 72847, 43968, 10221, 126, 44271, 16757, 54609, 30064, 10106, 28253, 10165, 20139, 10130, 10745, 144, 16822, 38064, 11357, 119, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
-      "['[CLS]', 'V', '##1', ',', 'ein', 'Mini', '##van', 'der', 'Marke', 'Pont', '##iac', 'Montana', 'aus', 'dem', 'Jahr', '2000', ',', 'bog', 'von', 'einer', 'privaten', 'Ein', '##fahrt', 'nach', 'links', 'auf', 'eine', 'zwei', '##sp', '##uri', '##ge', ',', 'tro', '##cken', '##e', 'As', '##pha', '##lts', '##traße', 'mit', '5', 'Fa', '##hr', '##sp', '##uren', 'in', 'nördlich', '##er', 'Richtung', 'und', 'einem', 'G', '##ef', '##älle', 'ab', '.', '[SEP]']\n"
-     ]
-    }
-   ],
-   "source": [
-    "text = \"V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.\"\n",
-    "result = tokenizer(text)\n",
-    "print(result)\n",
-    "print(tokenizer.convert_ids_to_tokens(result[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wGEujP4IWNvg"
-   },
-   "source": [
-    "Tokenizers of multi-lingual models use the same vocabulary for all languages.\n",
-    "Obviously, the tokenizer simply splits the input string into pieces and does not perform any translation:\n",
-    "the English pronoun \"a\" (169) is a different token than the equivalent German \"ein\" (10290).\n",
-    "\n",
-    "We observe that the tokenizer is case-sensitive:\n",
-    "It differentiates between the tokens `mini` (25103) and `Mini` (32930).\n",
-    "\n",
-    "So far, we have tokenized single sentences only.\n",
-    "Next, we want to tokenize the entire dataset.\n",
-    "This is easily achieved by applying the `map` function to the dataset.\n",
-    "\n",
-    "All we need to provide to the `map` function is a function that takes a record or a batch of records from the dataset,\n",
-    "applies an operation to it, and returns a `DataSet` or a `dict` which defines the columns to be added or updated.\n",
-    "\n",
-    "In our case, we supply a function that calls the `tokenizer` as shown before.\n",
-    "As we have seen, calling the tokenizer returns a dict with the keys `input_ids` and `attention_mask`.\n",
-    "Therefore, the `map` function will add columns with these names to the original dataset.\n",
-    "\n",
-    "Since we plan to feed the tokenized sequences into a transformer model,\n",
-    "we need to truncate their length to the maximum length accepted by the transformer.\n",
-    "Moreover, the shorter sequences need to be padded at the end, so that all tokenized sequences have the same length.\n",
-    "\n",
-    "Overall, only a few lines of code are required to complete the tokenization:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 98,
-     "referenced_widgets": [
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-    "pycharm": {
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-    }
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+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "wx8Bp2n-WNvW",
+        "outputId": "73dbe600-7f06-4459-ba60-b97eac1241a9",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "                            \n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+              "    x.observe(notebookContainer, {childList: true});\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = px.bar(df[\"NUMTOTV\"].value_counts().sort_index(), width=640)\n",
+        "fig.update_layout(title=\"number of cases by number of vehicles\", xaxis_title=\"number of vehicles\",\n",
+        "                  yaxis_title=\"number of cases\")\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"num_vehicles\"}})"
       ]
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-     "metadata": {},
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+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pwcfXawvWNvX"
       },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      "source": [
+        "Most cases involve two vehicles, and only very few accidents involve more than three vehicles.\n",
+        "\n",
+        "Each of the columns `WEATHER1` to `WEATHER8` indicates the presence of a specific weather condition\n",
+        "(1: weather condition present, 9999: presence of weather condition unknown, 0 otherwise):\n",
+        "\n",
+        "| column | meaning | count |\n",
+        "|---|---|---|\n",
+        "| `WEATHER1` | cloudy | 1112 |\n",
+        "| `WEATHER2` | snow | 114 |\n",
+        "| `WEATHER3` | fog, smog, smoke | 28 |\n",
+        "| `WEATHER4` | rain | 624 |\n",
+        "| `WEATHER5` | sleet, hail (freezing drizzle or rain) | 25 |\n",
+        "| `WEATHER6` | blowing snow | 38 |\n",
+        "| `WEATHER7` | severe crosswinds | 20 |\n",
+        "| `WEATHER8` | other | 25 |\n",
+        "\n",
+        "These weather conditions are not mutually exclusive, i.e., more than one condition can be present in a single case.\n",
+        "The frequency distribution looks as follows:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary', 'input_ids', 'attention_mask']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# define a function to tokenize a batch\n",
-    "def tokenize(batch, column):\n",
-    "    return tokenizer(batch[column], truncation=True, padding=True)\n",
-    "\n",
-    "# encode the full dataset\n",
-    "dataset_en = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_EN\"})\n",
-    "print(dataset_en[\"train\"].column_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "GUQ2SaowWNvh"
-   },
-   "source": [
-    "The additional argument `column` is passed to `tokenize` via the the dictionary `fn_kwargs`.\n",
-    "As we can see from the progress bars, the map function gets called twice - once for each split.\n",
-    "As expected, new columns `input_ids` and `attention_mask` have been added to the dataset.\n",
-    "\n",
-    "We repeat the same procedure for the German texts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
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-    "pycharm": {
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-   "outputs": [
     {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9695b34364f41e5a4470ce3f8d220a8",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "DD17G3B9WNvY",
+        "outputId": "3678887d-537c-4eb1-9aba-01d7023ce1f4",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+      ],
+      "source": [
+        "fig=px.bar(x=range(1,9), y=[(df[\"WEATHER\"+str(i)]==1).sum() for i in range(1,9)], width=640)\n",
+        "fig.update_layout(title=\"number of cases by weather condition\", xaxis_title=\"weather condition\",\n",
+        "                  yaxis_title=\"number of cases\")\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"weather\"}})"
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-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      "source": [
+        "The most frequently recorded weather conditions are \"cloudy\" (`WEATHER1`) and \"rain\" (`WEATHER4`).\n",
+        "\n",
+        "`INJSEVA` indicates the most serious sustained injury in the accident.\n",
+        "For instance, if one person was not injured, and another person suffered a non-incapacitating injury,\n",
+        "injury class 2 was assigned to the case.\n",
+        "\n",
+        "Information on injury severity has been taken from police accident reports, which are not available in the data.\n",
+        "Unfortunately, this information does not necessarily align with the case description:\n",
+        "There are many cases for which the case description indicates the presence of an injury,\n",
+        "but `INJSEVA` does not, and vice versa.\n",
+        "\n",
+        "For this reason, we created manually an additional column `INJSEVB` based on the case description,\n",
+        "to indicate the presence of a (possible) bodily injury.\n",
+        "The table below shows the distribution of number of cases by the two variables.\n",
+        "\n",
+        "| `INJSEVA` | meaning | count | `INJSEVB`=0 | `INJSEVB`=1\n",
+        "|---|---|---|---|---|\n",
+        "|  0 | O - No injury | 1'458 | 96| 1'554 |\n",
+        "|  1 | C - Possible injury | 1'112 | 1'298 | 2'410 |\n",
+        "|  2 | B - Non-incapacitating injury | 729 | 945 | 1'674 |\n",
+        "|  3 | A - Incapacitating injury | 304 | 373 | 677 |\n",
+        "|  4 | K - Killed | 5 | 114 | 119 |\n",
+        "|  5 | U - Injury, severity unknown | 44 | 122 | 166 |\n",
+        "|  6 | Died prior to crash  | 0 | 0| 0 |\n",
+        "|  9 | Unknown if injured  | 51 | 16 | 67 |\n",
+        "| 10 | No person in crash  | 1 | 0| 1 |\n",
+        "| 11 | No PAR (police accident report) obtained | 231 | 50 | 281 |\n",
+        "|**Total**| | **3'935** | **3'014**| **6'949**|\n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-   ],
-   "source": [
-    "dataset_ge = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_GE\"})"
-   ]
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-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Mhb6ViiHWNvh"
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-   "source": [
-    "Later on, we will also use a dataset which has 80% English texts and 20% German texts:"
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-    "id": "J_Mve0bUWNvh",
-    "outputId": "1952d652-3bad-4540-87c4-a8d9d7334b09"
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-   "outputs": [
     {
-     "data": {
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-       "model_id": "1956c3d386b0412081a9b1ffd7d21389",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mi0yKfm2WNvZ"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "Now we turn to the verbal accident descriptions.\n",
+        "First, we examine the length of the English texts, `SUMMARY_EN`.\n",
+        "To this end, we split the texts into words, with blank spaces as separator,\n",
+        "and show a box plot of the text length by number of vehicles involved in the accident:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
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+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "70q6pUwuWNvZ",
+        "outputId": "499a601c-1323-4cc5-d228-131ba4e51b63",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Overall number of words by case summary: min 60, average 419, max 1248\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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per case summary\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"boxmode\":\"group\",\"width\":640},                        {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"text_length\"}, \"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('64618fba-f4d5-454b-9ca5-4b4fd67705c9');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# statistics of summary length\n",
+        "df[\"words per case summary\"] = df[\"SUMMARY_EN\"].str.split().apply(len)\n",
+        "print(f\"Overall number of words by case summary: min {df['words per case summary'].min()}, \"\n",
+        "      f\"average {df['words per case summary'].mean():.0f}, max {df['words per case summary'].max()}\")\n",
+        "fig = px.box(df, x=\"NUMTOTV\", y=\"words per case summary\", width=640)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"text_length\"}})"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fba24976bb6a463cb0f12f0e5e6175fc",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mrk7jvqtWNvZ"
       },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?ba/s]"
+      "source": [
+        "Not surprisingly, the length of the descriptions correlates with the number of vehicles involved.\n",
+        "\n",
+        "The average length is above 400 words.\n",
+        "As we will see later in this notebook, this poses some challenges with the NLP models that we are using in this notebook,\n",
+        "because these are limited to text up to a length of 512 so-called \"tokens\" (vocabulary items).\n",
+        "Since a single word may be tokenized into more than one token, some accident descriptions will be truncated.\n",
+        "\n",
+        "Let's examine one of the English texts and its German translation:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27ef6eeaaccd4aa5896dd377dd6f76bf",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138
+        },
+        "id": "A-eL7RyAWNva",
+        "outputId": "caf6abee-59b4-48d4-99a3-37f32b35ea14",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.  The posted speed limit on this roadway was 80 kmph (50 MPH). V1 entered the roadway by crossing over the two southbound lanes and then entering the third northbound lane, which was a left turn-only lane at a 4-way intersection. The driver of V1 intended to travel straight through the intersection, and so he began to change lanes to the right. He did not see V2, a 1994 Pontiac Grand Am, that was traveling in the second northbound lane. The northbound roadway had curved to the right prior to the private driveway that V1 had exited. As V1 began to change lanes to the right, the front of V1 contacted the left rear of V2 before coming to final rest on the roadway.\r \r The driver of V1 was a 60-year old male who reported that he had been traveling between 2-17 kmph (1-10 mph) prior to the crash.  He had no health-related problems, and had taken no medication prior to the crash.  He was rested and traveling back home.  He was wearing his prescribed lenses that corrected a myopic (nearsighted) condition.  He did not sustain any injuries from the crash and refused treatment.\r \r The Critical Precrash Event for the driver of V1 was when he began to travel over the lane line on the right side of the travel lane. The Critical Reason for the Critical Precrash Event was inadequate surveillance (failed to look, looked but did not see). Associated factors coded to the driver of V1 include an illegal use of a left turn lane (cited by police) and an unfamiliarity with the roadway. As per the driver of V1, this was the first time he had driven on this roadway.  \r \r The driver of V2 was a 28-year old woman who reported that she had been traveling between 66-80 kmph (41-50 mph) prior to the crash.  She had no health-related problems, and had taken no medication prior to the crash.  She was rested and on her way home.  She does not wear corrective lenses.  She sustained minor injuries and was transported to a local trauma facility.\r \r The Critical Precrash Event for the driver of V2 was when the other vehicle encroached into her lane, from an adjacent lane (same direction) over the left lane line.  The Critical Reason for the Critical Precrash Event was not coded to the driver of V2 and no associated factors were coded to her."
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "display(HTML(df.loc[0, \"SUMMARY_EN\"]))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def map_mixed(x, idx):\n",
-    "    return {\"SUMMARY_MX\" : x[\"SUMMARY_GE\"] if idx % 5 == 0 else x[\"SUMMARY_EN\"]}\n",
-    "dataset = dataset.map(map_mixed, batched=False, with_indices=True)\n",
-    "dataset_mx = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_MX\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ItDUbmVvWNvi"
-   },
-   "source": [
-    "Now we have created three datasets - with the tokenized English, German and mixed language texts, respectively.\n",
-    "\n",
-    "We could have stored the results in a single dataset (with different column names),\n",
-    "but keeping languages separately will make it easier to convince ourselves in the following examples\n",
-    "that the languages have not been mixed up!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CMoSQ04TWNvi"
-   },
-   "source": [
-    "<a id='transformer'></a>\n",
-    "<a name='transformer'></a>\n",
-    "### 2.3. Transformer model\n",
-    "\n",
-    "After completing the tokenization of the raw texts, we are ready to apply the transformer model,\n",
-    "in our case the multilingual DistilBERT model.\n",
-    "\n",
-    "First, we load the model.\n",
-    "To speed up the following calculations, we opt for GPU support if available.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "-iKVTkH_WNvi",
-    "outputId": "fb57c9a2-6c21-4d5d-c854-91446083b71c",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78af46f672764221a9a83c9d8b3fbd5c",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "id": "74OyQip6WNva",
+        "outputId": "e96ea679-9fa9-4e56-c666-6318cf34310d",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/517M [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.  Die zulässige Höchstgeschwindigkeit auf dieser Fahrbahn betrug 80 km/h (50 MPH). V1 fuhr auf die Fahrbahn, indem er die beiden Fahrspuren in Richtung Süden überquerte und dann auf die dritte Fahrspur in Richtung Norden einfuhr, die an einer Kreuzung mit vier Fahrspuren nur für Linksabbieger vorgesehen war. Der Fahrer von V1 beabsichtigte, geradeaus über die Kreuzung zu fahren, und begann daher, die Spur nach rechts zu wechseln. Dabei übersah er V2, einen Pontiac Grand Am von 1994, der auf der zweiten Fahrspur in Richtung Norden unterwegs war. Die Fahrbahn in nördlicher Richtung war vor der privaten Einfahrt, aus der V1 herausgefahren war, nach rechts gebogen. Als V1 begann, die Spur nach rechts zu wechseln, berührte die Front von V1 das linke Heck von V2, bevor er auf der Fahrbahn zum Stehen kam. Der Fahrer von V1 war ein 60-jähriger Mann, der angab, vor dem Unfall mit einer Geschwindigkeit von 2 bis 17 km/h unterwegs gewesen zu sein.  Er hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Er war ausgeruht und auf dem Weg nach Hause.  Er trug die ihm verschriebenen Kontaktlinsen, die eine Kurzsichtigkeit korrigieren.  Er zog sich bei dem Unfall keine Verletzungen zu und lehnte eine Behandlung ab. Das kritische Ereignis vor dem Unfall war für den Fahrer von V1, als er begann, die Fahrspurlinie auf der rechten Seite der Fahrbahn zu überfahren. Der kritische Grund für das kritische Ereignis vor dem Unfall war unzureichende Überwachung (nicht hingesehen, hingesehen, aber nicht gesehen). Zu den assoziierten Faktoren, die dem Fahrer von V1 zugeschrieben werden, gehören das illegale Benutzen einer Linksabbiegerspur (von der Polizei verwarnt) und die Unkenntnis der Fahrbahn. Für den Fahrer von V1 war es das erste Mal, dass er diese Fahrbahn befuhr.  \r \r Bei der Fahrerin von V2 handelte es sich um eine 28-jährige Frau, die angab, vor dem Unfall mit einer Geschwindigkeit von 66-80 km/h unterwegs gewesen zu sein.  Sie hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Sie war ausgeruht und befand sich auf dem Heimweg.  Sie trägt keine Korrekturgläser.  Sie erlitt leichte Verletzungen und wurde in eine örtliche Unfallklinik gebracht. Das kritische Ereignis vor dem Unfall war für die Fahrerin von V2, als das andere Fahrzeug von einer benachbarten Fahrspur (gleiche Richtung) über die linke Fahrspurlinie in ihre Spur eindrang.  Der kritische Grund für das kritische Vorunfallereignis wurde der Fahrerin von V2 nicht zugeordnet, und es wurden ihr keine zugehörigen Faktoren zugeordnet."
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "display(HTML(df.loc[0, \"SUMMARY_GE\"]))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\n",
-      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load model\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model \n",
-    "model = AutoModel.from_pretrained(model_name).to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6SjLBsnHWNvi"
-   },
-   "source": [
-    "The warning message can be ignored for our application.\n",
-    "\n",
-    "Let's examine the model structure:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "G9_RcTV-WNvj",
-    "outputId": "ddf78938-3405-4237-b229-81a0d42edd06",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DistilBertModel(\n",
-       "  (embeddings): Embeddings(\n",
-       "    (word_embeddings): Embedding(119547, 768, padding_idx=0)\n",
-       "    (position_embeddings): Embedding(512, 768)\n",
-       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "    (dropout): Dropout(p=0.1, inplace=False)\n",
-       "  )\n",
-       "  (transformer): Transformer(\n",
-       "    (layer): ModuleList(\n",
-       "      (0): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "      (1): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "      (2): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "      (3): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "      (4): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "      (5): TransformerBlock(\n",
-       "        (attention): MultiHeadSelfAttention(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
-       "        )\n",
-       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "        (ffn): FFN(\n",
-       "          (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
-       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
-       "          (activation): GELUActivation()\n",
-       "        )\n",
-       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       ")"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Gb03bROWNva"
+      },
+      "source": [
+        "To get an impression of the most frequent words, we generate a simple word cloud form all English case descriptions.\n",
+        "By default, the word cloud excludes so-called stop words (such as articles, prepositions, pronouns, conjunctions, etc.),\n",
+        "which are the most common words and do not add much information to the text."
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "X3lhtdAsWNvj",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "As we can see, the first block of the model deals with embeddings, with the word embedding as the first layer.\n",
-    "This is followed by the transformer which consists of 6 transformer blocks.\n",
-    "\n",
-    "Let's first explore the word embedding.\n",
-    "\n",
-    "The goal of the word embedding layer is to assign each element of the vocabulary a vector of length $E$.\n",
-    "\n",
-    "The multilingual DistilBERT model has a vocabulary of size $V=119'547$ and a word embedding size of $E=768$.\n",
-    "We can confirm this by looking at the dimension of the word embedding weight tensor:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "k9yHSGD0WNvj",
-    "outputId": "8c3ea6f3-0b8a-48a6-8a59-c79d03c7f7c1"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Embedding(119547, 768, padding_idx=0)"
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "fUhYGO10WNvb",
+        "outputId": "6e65c641-c672-4f47-dabd-b3fe523e63ec",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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UAMNs3cGlQuIctV6pWXWus25mfXdcjXnZ075llDzqw2MRsQ+elK+dwazXJ0+eRiDgXG06nXHayumxfaeEHN68QU98BH7+6yHD3szeWGO4CwLqlY9YtHaPfDQty1ZskQ9v07Iap+m1r6DNYPAdbEpkk7pKqFGo608SP+p\\u002fAyqu4fvxW6tFsFx+Hb86\\u002fxhX0t2t+fHmvQap\\u002fDYGrHQAEx\\u002fk+\\u002fdVaos6AoV4FQkLGzIoeMyu6q0X002v7Uo\\u002fd2vrcH58c3aJP4OjOB4CuFmI4El7jM655WcPq0Kh30y4Vd3UUdrZHODgRtEcrSgDAiW2T5O6tF7bJej+8eQUA1oRGfThhul4e7+w21z946r5fRErF22mXLqx4mNCOeCA8ZnlQOJ\\u002fJ0kv87ewT3TzdOLbvpl3qUSlTm+pm+fZ7Ng157tLpad7+302ba9g6G6kqyeGKYtxaPR03ZktCkl4e6+Q6w9t\\u002f8dE9XQr566kpO5IX6lXDI7e99VR1OQBM9\\u002fbbMXMRfncC7OwT3Tz9ePbv37g8MPl\\u002fkr5mLZ1JC4j2QnVo2omcgQn+IWhMKgBoVHcUIGflVdQUNQLAuLmxhu95S02HXEqsmPwr4LFReddKtRodUWfAsK9C4Gr3ys8bGSx6cXp\\u002fCwUAYieHAkDm+QJDoV5CCAGzkiWBYQwKBQD2lRKL7ZTL8Bbc4sBQsoHp2V2cp9Rq2VTqG4mT9EIcPoP5YHgMANSIhQWdbQQti0I1tFZ65vn3nXmNmGiL9XBo9K+mzB5ZX5KeXQW3AMDFhrM5jhgxEypwfCg8FgBSaqtqxAMa3cPgcEVf9PUrYyYSmhkPRcQ6s\\u002fuCZv7T9BksAFjy5AwA+On1\\u002fRU5dXohADRVtvX2DPCb3A1cfRwBIPN8IVExRKy5Ctw93FDeqtfKJIpvn\\u002f9Tv\\u002fvv4h3iNnZ2dHN1+\\u002fZX9qqV\\u002fZ0pWrX25um8YVxF2okcPC5XT2VenVKuIvihxs2Lc\\u002fN3unEy9+qRLL2w\\u002fFbt3z9eoDNp8x6dbJDWWmzp9Ll+wQBwrKoUb\\u002f3pOVZVqsMwAFgaNMDNf6WhFgBinVyZlL4uSEP87frcf\\u002fkdRINlDgGThRvEXo3Znqn5\\u002fsFsqonD3TldCnlJVwcAJHsHUEy5vef4BQEABnC5oZaoGyLZrU0A4M3lGfcwkBFknJsXQfhfpN\\u002ftOmFRQtmt2iPbzm+e9mFQnI+zl0AmUbTUdjRXtW+79rZNhIlvlwXSz+aLOyQyiaK5ug0A2hu7935xkmXLZHGYAVGexqES01Yl7v\\u002f69MX9NxvKWzwCXJQKtVwiN4xCtLJAa65i3NyYPz76O\\u002fN8wYtzPvOL9BR1SHKvlrj5OoWPDSi6OaDSkXo0WyqWyyXy6sJGAGioaN375Sm2LZNtywyO83XzJ7ZxRornv3\\u002f49SVfn\\u002fzlStrJHN8IDxaH2d0iqi9rkUkUv+d\\u002fYsMd2lX89dmJ2pImr2A3Jw8+g83obOouza5BEOTBNwa4IShU8pu\\u002fP\\u002fHKwi8\\u002fXv\\u002fT4W3n3HydhO09hTcqSCTkpZ8eNQzCGBJrQqMOlRf1qtWnqssNbdPRilIAiHJ0MXy7UAwrE3YCQGpTvff2L\\u002fRyY0RKExVJlU57uaH2RnNDubCrSy6TqFUKrUap1eKWEcCEMw4n1L7PMzjilHZ34hvB9kR3IU4QX4AAYADFXe1E3RCp6REBgD+vz6YT8B4hv\\u002f6\\u002fy4B+osc+WB43OezkL5fLsmuq8usZbLrAxW7xpulOHqZvgQW+e353d2t\\u002fDbyrRfT7h0fxbeMAawDgO3E\\u002fP\\u002fXSb+\\u002f\\u002fXZlXX1vcxLFj+0cO+CBYX+CgV8EVcD47+dJv7x0pzaouu1UjcLFLXpu05qV5h749R3jVP350B6rr76hqrmr\\u002f\\u002fYO\\u002f8W18eI1eNbJw7Nhfnnnl9G9XLx\\u002fKKMmo1mq0do7c0NH+4+bG6qtF1l\\u002fFiudmpey5UVvc1FTZimGYLZ8zdlb0oiemRYwLMkwGAN6h7tuuvb33i1OZ5\\u002fJrChtteKxx82JXPDvLP2r4H+cYJ5cQe4fS7s69pQV6g1UtFhZ1tQPAsqAB7naJWmXYM2gB42QX6qvfuJbSJuvFd6kksj2T6cSyYVNpxu1HAoQ+yhFEfNuw2jEYAzV90MhkFpUm06iFpkyw9Si1WrVOBwBcuukDmexwIFDUc6tN2TTNaQFRAQAAdbLKSx0nH\\u002fF5jiCv6i3xtyGO8RoUc8f6re7bCYJkXxviw4kzYGjOfe5zN9hdnPdG6gUASFnxcICdPQB8npm6LSeDRiZnPfCE4QsmVCpif9sGAPP9g5838E8bw2MweAYZU5vqHzx1CMUwPpP1RPSoad5+XrY83CuvRVH\\u002fHV8BwBMxo14ePUGfBQxmRNiRvHCGj7+haqQ4WV32VMpJANievGCmmbiKsF1bZRrNJE+f32YvIeoAwLqhOUqtNvjnbwBgSVDYl5NNfEr1v8Kwh+aYM1hbK9\\u002fZHPAOQThsLBusATWsu4FG\\u002fAyZ9QCJFk9UAOhkv+gUR8iMmWSbp\\u002fRCVHkB01aRbR43SEjEOA2qTifR+vtQ73NPsSgw9OP0qzKNZn9pIe5KP15ZBgAzvP0J1QEunUFCEBTDVDqdN5dnqLLM5xmpKIYxKJTDC1cRYpqM62L\\u002fJHaMPl+KyTYsAKh0WrlGAwB2DCZRNxQYFAqVRNagOolKRdQBAIBUPcCFt6P6swBOWK2sokcj2uT\\u002fKp3ETO08nym8GsgJn+faNxr5WPNfQnVnt7pDpu1d6v4Qm8Lp1Up+rf1GohHb0ezXeT\\u002fVpmxKaTvWKK\\u002fdUf0ZAGzwexEBgrsfACCl\\u002fWi5pAgDVEB3WuW5EQCMj3W27XCpJJ9H5Uu1ff7WG10Xc0Q3UUD9bUJmuyzDhXfdYFmAzH4EEDqg\\u002fQ09ACAxpgFMM5QYY5xGJ\\u002f2KZH\\u002fAUHKfewc2lTbfP2RvacHflSWvjJlQ1NXeKO0BI3c7AJARJNDOvkzYld\\u002fRioGpZ98Uap0Ob\\u002fSNd\\u002fcyjsBslUkJkn+SMIEj7qLSO7MIlHV34W2cO\\u002fej+fDsKoRd1WZ6GxulYoKESqIZVpfGO8xgklmtykZ8V4dpiyU5r4V8KdfJvq98P4wbWyerFKm7Nvm\\u002fRkGo31a+265sdmF4rPV64v2S8sf8XtKXY0xmd+qDPk+7M72x225EwrE6VC3FPTnPB32AYdjHpS8CQJeq\\u002fZYo7amANxFAtlV92CCv9mT5gbHB0vX+gKrSAFCE4knhfoqqs3WynwGhgbYZoSdROM8BgE6+F1WcBNAhtNFmJb1bUeU1IDsD2kU4hAV08j918kMk+jgK5yUA0Eo+AkyBanLIjBk6xRkK920SbSwhDaat0Pb+gGqKNML1AEDl7\\u002fwHJqq\\u002fz1BZExa1t7SgWyG\\u002f0dyQ2lQHAI4stnHwJwBM8fIrE3a1yXov1VdP9fIjqk2h7380OQDofG0VUfQPwqMz4pzdstuaz9dWvpk4iUoiExKcrC7DNyaYuhtDIt7ZtULYVSMW1vWIjeunhGEAAODLNt3swiEjlACbsF9qvwaAiY59bUw3pjcFoQKADcVWqTNdZzTmUd\\u002fnL3ac7FZ1THGaG2YbQ1QDdKraXZieCCAIgrgw3QGgTdncqWr\\u002foeojPIFSp8Q3jAyW4giVtxWhhgH0VaQxbR3N4TQAqLsWYcz5gFBRxXGq\\u002fR4ARCNci2kKgMQjShAbnfISTfA3AKbuTB5wAIuQWesA4WDaCr2ERE9CKD4YKqXYvomqUkm0sYQ0CCWQyvtS3TGJyt+lz6Wnsb5r\\u002fYofiFIAAPANcNr+50ai9D63Uau1R\\u002fdnXr1Y3FTfrdHq+PY2UXHeK9aN8\\u002fQejvsjXOAU6eBc0Nl2qb4mrbkeABYNDL\\u002fS83BE7K+FOQqt5o1rF\\u002fwX2HvZ8ggJMIBGSY+nbb8rx5bOsKHRetXqgs52wsC0arFwW266fvdfYWN0QvbZ5jZZ71dZN14ePd5QVdzV8WdxHgCM9\\u002fAenl\\u002fJkEWBYXtKCgDg4\\u002fSr25MXGN7cIxUldT1iAwEAAGLq\\u002fhsi1fbMdV3hSHfVS0hG87giCEmDqjHATDYGcQR0p9WeG+W63o9KXvggYjtRDSCgObYqGvD6V7uyBQCcGW52NPsn\\u002fF4lISQdptMfl2iwqHbbdbIdmLaJbLOBRJ8MAAjFF6+zkChBmK4BMBTT1WuEfVOLYZgMNJ1ECdpNogbhuRCKaUejtZD4gIoRMh0QOmCmG+f3Jt8+9cupny+eU\\u002f5FVAykKrfujQWfPbd9w+jZJr48lnl\\u002f1dbrf2fi2x8efyl+RtRA\\u002ffBRq7UvbvqjtKhJL+lo60k5lX\\u002ftYsnH36wJj\\u002fY0SGsta8KiCq60XaivbjLTHsRxYLE\\u002fmThj88VTrTLprAO\\u002frwiJSHBx59IZCq2mWSop6mpPbayLcXL9ccZ8fRYEINkn4HB5cZWo+6Ur5x6PTnBmczoVspS6qu9z0lEMs2MwzbmQ\\u002fgGme\\u002fsvCQo7XF78Y25GrVi4IiTCncPtVauvNtbuzM9WarUcGv0jg5h+Y9DbHWP6DZMkOLvN8PE\\u002fX1t1rrbykdNHHo6IdbXhCJWKlLqqXwtznNk2+i5UY1BM91fD9nZls1KnEKm7Zros4VLtMMD2N+wiISQ1qlrrtYmYBwAAEECi7cZ+Wf46n+Zg7I8HAAyw7yrfpyBUDNDxDjPA1LGcGG5BnMivK96ypzkK6E4AIKA7jRNM21b1IQkhYRj2mN+LNBIdjA0WQvaicD8DVKzunEFzygQATFsFoANAUG0Zmfw4IBSE7Erl\\u002fw5ABtACkDBdE1GirUE15XgdDdPWEA4xRMyabQMQwJQA6H+3MajTWgpqN8fz2zc89M6y60ezfnt7hP13h\\u002fekG1orPSql5quPTuza\\u002f+Rg32YTzPcP\\u002fuDGFdxaRTk6492FJlkQEEJCkFeunpNpNL8W5vxaSBy3YDy4\\u002f5XRE9JbGpulkkPlRYfKi\\u002fRyWzr999lLfyvKOVHV1\\u002fL6V\\u002fh0YjIFIe0vKzxbW3nWYAg0ALiwObtmLfIY2Pd3o7nh4\\u002fSrvWq1RK2SqlV4vAIATNizk02l2tDoNlQah0b\\u002fZdYiQnD\\u002fl5NnrZUdzO9ou9xQc7mh\\u002f9WLdXJ9N2nqvMN\\u002f6iUErxMJIa\\u002fzetJQcqXjdBAnIkkwHQBOtuyrlVWM4k94xOc5jVZHpZANbdNS94f028YggDwb+K5+93puTVKML36skpo2D0c7Dp0OAHNdVwCsEEsVx68UOrv6AMAo\\u002foRR\\u002fAG9umBksFCNcBUAHQAls\\u002ftqTAiJqxFtBl0LiT4ZofgCAIm1StO9DhASYBiV\\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\\u002fbAQLmEjJzQMCIsYTCeQH6j2IKhEXlfU2QUXhfDdi1fQ0A4HZIBB4bQUiDQ7F9hyi6z3DBUKyhzlJXSV11xzAMFgBQEAQAaGSyfnyfBfgM5tNxY56OszZaxY7BfCNxkvEIRAB4JDLukcg4ohQgyd2r7vEX9Luf70g5mlKQdnCLQZKRZIyrxxhXD6LUFPP8g625RSahksjrI+PWm7reysdMNNnM4Uh3eSrgDYJQo9VJZUoalQIAZ66XyhTq2FD3+hahqyM35WbZg\\u002fNHc28vGHruRtmCSRGnUouTx4WkpZUiCJJ\\u002fuemxpeMAoLyuI6Ow7pnVEzMK610EXBcH2+yShtKa9mAfJ0NH2FmDXDxOX8wHsUl4HwDoahauDXhm8TOzHvtkjaH82YnvCNvEv5d9jXsrtWrt3s+OXfzremdTN8+RO3HpmAffXkZn9ccTk8ikhrLmn17cXXSjnM6ghYwJeOzTNW7+zrj268d3nv3tCr790q9PTF1lIk6yILX04JcnSzOrFFIFh88JiPV56psHnbxMD\\u002fIwZtAzNIlSpcEsOksUw5ouUouix6rKAGCal59hzOd\\u002fharS1mN7bgKCqNVaMgmhM6ib31pATHTPsPto5qxJYfa8\\u002firPiODI59hxWYFeDgBAo5HZLM7V7CoWg7ZsRoxao2toFUXcru8nJwZfyCgXSeQCHru8rsPJnuPqyNNodRhgU0YF9vQqpTKVwM4GT1xa0z57fKhhBQ0ADHPphYM4fUi0eEL16n8BgRs\\u002femLolf03Dd\\u002fbtrrO0ozKqavH4dYKw7D3V209+NXJ8UtGP7d9w5SViSd+Snlj4WeGWUhk0iuzPuYKbJ\\u002f8+sEFm2bkXyt5c+Hn+jkYHv983a6CL57a+pA+PYGLe66\\u002flPxhY0XrkmdmPf\\u002fTY\\u002fM2TlMr1PYudsR0ZrDmDE3CYNCMJ4c0xOb2525InK2t6FbIAWB16Ih1DvyT+Ie4bHl\\u002fsbuX\\u002fTNvzHvhg8UOztx7dpSITKHesS+tx2hatBFBq0WPXioAACqFIuCxhT3yuFCP345n5JQ24YYMx1lgW1HfGernDAAT4vx6ehUYhuEmCfdAqjXagormlPQymUKdGO3z+\\u002fHMPadv1TR1F1a2nEsrU6g0hFw492tYppm2dvzn67fnXyuJnhSGSy7vSwOAqav76kE3jmWnn8p5c+\\u002fmpEWjcIm9K\\u002f\\u002fHLX+kn8oZO6+vNq5VaycsGf34F33eQJYta\\u002fuLf5ZlVoWPCwIAJofhznERtolxLQGFVLntud\\u002fd\\u002fJ233fyQcXsC1TWvLRqYyhLWnKFJEAS8fR1rqkz7UAAgMGTI7cFetfrLzDQACBM4jnM349b8L9DVIW1u6Gax6E11XRiGDRoZ8K+QXVCvMxgDO7JsXjMRr\\u002fIsnhoJABPi\\u002fAAgIcyTPHBGbwB47dG+rs+YYPeoQDdAgIQgT64YD7fzvv\\u002fkHDyBn7vgubWTMQwjkZAPn55rnAuXwH2DZY5xCxK+e+bXS3vT9Abr0r4bwQn+eg936t+ZDDY9cX6ffw0AYqeGA0D+1RJDczB7\\u002fRT9dmC8LwC013fiBssyuZeLZD3yRz9apbdWQ8XKMzTJ5ORwcwYrKs7b3eIUz3qqxcJetZpJodT2iL7PSa\\u002ftESEAb4+bYvkVzy1p+utYZnFFq1yh5nKYQb5Ozz861cXBFte+\\u002fuVxBJA3npr5\\u002fR9XL6dXyBUadxfeRy\\u002fM93CxAwCdDj1ztTjlelltY3ePVMHnsccn+D+xZjyT0efV7pEqdh24ceNWTZdYZsOie7jYzZkcNndKhP7oJBJS1yz89rfL+aXNdBolPMj16Qcm4oXjrNs0+cKJPJVCs3LDRJKp6WI0Wt0fRzLOXivp6JLyeewpYwM3rBzHoFMBoKNbuuSJnSvmxj31wETDLBtf39st6j24bQOCWMoOAJ\\u002fvSKms73x908ytZs7wx79Sr2ZUNreJAWDdlt9vHwGu7XvO2KAMG6rRfLODFm65zg7Qt\\u002fQsQWic677BMg3ThjFufsL1o1lPbX2YxqBW5dU1lDUbNt+aq9uUMtWs232peiTCAaEuhv4mfN0atXWTFLbWdACAZ7AbUWE1Vp6hSRavHJOeWlFc0EiQu7rzX357IUFojt+LcvE+QT0vjR4\\u002fysXdUELg7LWSD78\\u002f6+LIXTkv3knAae2Q5BY3CuwGuDY6hb2vfHbMhkXfuCpJq0OzCuqdBX3mjEwmHU0pcHGwXbMwwdaGmVvcePhsLoZhWx6diid47Yvj9c3CVfPiHextukWynKLGTqGsv2gAMom0+d2DcREez6+f0tEt3XM8+8WP\\u002f9799UOU2y+kLZe1eG2iYRZDMAxe\\u002f\\u002fLErcKGpbNivN3taxu7Dp7OLa\\u002ft+PbtZSQEcbTnxIZ7pFwv27Rugr7W0NrRU1TR8tCSMQgySHY8fU1D1+b3DsaGmz7DKWMDx0R7X06vOHw277VNya6OXDyXSdv6X2SEDdau2u8nOUz3MzPSuqAnp1XRnOw8j6i4J5m6Juni3uuZZ3KTFo26tC+NQqNMXDZWr8VQjCuwffrbhw1yAAAQ5o0a1MNtDtw\\u002fQiITvzDWY+UZmoRKJX+27YGj+zOuXChurO\\u002fSalEXV7sJU0OXrRnLsrrG52Zj687hdsplFBIp3MHp0ci46d7+xEQGyBXqr3ddcnex+\\u002fWztfo6xcMG84LjFFW0rFs46vE14\\u002fHdxcnRhtqfP+7vJ5k1MbStU3Itswo3WGqNNr+06cHFY9YsSMATrJrXX\\u002f3E0Wh1U8YGbn54Mr7LYtK+\\u002fe1KcUVL1O01rGoq2vb9fG3F+gmiLmn8uID+nAAAcC2zMi27+sMt8yeN6VM58G2++fVyWnb1+AR\\u002fAJg5MeyD78\\u002fkFjfGhfcF356\\u002fXgYAMyeGWpMdABRKzbwpEebOMMjXCQAq6joAIMTP2deK39qQvSk5J9OKp8QFrp83mqB6bfup5VOjowOG+QW1UPKQGGGDZZlIbmwkN5YovVeJnRJu72J3af+NpEWjUg9njJoZrV84GgBcfJ1qChrGzImh3n61RhZnbwcAaKpoDR0TSNRZxx2eIZVKXrY2cZn52sSgbIxO2BjdZxqsIbuwoVeu2rRugt5amWOlkaExh7+Xw62iBhTFSCSERqV4uvJPXCoM8HacMMrfuLmBs2B6pH47xM8ZAFo7JVEhfZKLJ\\u002fNnLIiRSRWVJS3Ro30pAxtHl9MrGHTqhFH9djkhyhsAcooacYszaXTAFzsvnE8t7TdY10pCA1zwNt2g2XEsn+GdsGp6LI1KEQ82y\\u002fYwGKmSR8ZgnWo9UiIpMJwa4ofqL4I4YdW9FRKN+Cn\\u002flxlkxtXOlPTua0Gc8IVuKwAgreuySNM912UpABxr2e9Ad0q0n3S961K2KB3F0EBO8FyXpbWyqnNtx0kIWart4dMED3k\\u002fYWG80oiDkJApq8Yd\\u002f\\u002fF84fWyjsbux794wFA7YfHoa4fSj\\u002f+YsuTZ2YbykXLERk8OZ9owjnx3dtLyRNptF8yQuNtnaIgG1RkP6x0UQq7mdjEAeA+22gWLSePZmu2mLKtu\\u002f\\u002ft8XklVW7dIplRp1Bot9FVXEQD46IX5H2w7+\\u002fqXx+3t2LMnhS+bFWM\\u002fsL0JAM63\\u002fWUAgAccaQwm16dQya1NQiaL3tokJFgrAGhqEytVmvEriKGCkl4lvsFkUCeO9r+SXrnl0ak0KqWitqOuWahvsQ6aHcfyGVrPrhMZGcX1ADAh2nftTNPfgJ3Hbt4sqnO04wglA9rOerbuv6pUa\\u002fOrWibH+l\\u002fMrtyyelJCiOfHf1xoaBMpVJqx4d4bF5n+5v19teB8RrkOxeKC3M2lMWYEDFa7srWoJ\\u002fel4PcwDHu\\u002f9GW9nIpQH\\u002fPdrN+d6DCdSWa1KJrw3QR+4idlb852XgyA5YmzXw3+sFPVniW8+WzgawggWys\\u002frpfXAECTouGdsM8pCPWrivfblM0uDEsekBFn2prxB786+csb+zl8m9Gzog1VSYsSxi8etfPVPbVFDeFJwRiKtVS33Th+69OzrwkGe+UAQKvRCdvEsh55XUkTADRXtdcUNLBsmXaOXLwVacNjbfx87dZNu54c+\\u002fr0NePt3ey6W8R5l4ue2vqwq58TAGhUGmGbWC5RNFW0AEB9abO9ix3LlmnnxMMN3B2eoSEl4jY+neXMtNWgugJRswuT68rilve008kUbxv7bpVsZ0XaTLfQaH7\\u002fr9OhlGIY8OkssVrhwLDRl2AhFx4kMKi3xdjjqyc9t\\u002falT48G+jiuXTDK253PYTP++DvjxMVCfQJvd\\u002fufP16TU9R4\\u002fGLBvhPZB0\\u002fnvPfc3HFxvgZlgOX63YpHxp8\\u002fmtPRKl77eF+jzBAUxXi2zBc2TCPI9V42AJg5IfTctdIbt2onjQk4n1pKpZCnJva5UKzJDoOdoZXkV7bkVzX\\u002f9PJyANj8zZHoQLdwX2Lnb32bKDW\\u002f5rc3VwMGy9\\u002f4jaDVMyrU09PZTipXPb9qUkZxfUKI5wurJ1MpZBTF5r3482MLE42\\u002fj00d4rPpZdtfWo4gsOmLQyW1baE+ffGJlhkBg9WpandlevRNDWFgUPxsLLVlaCR6ICe0SJKLYlgIJ5xGorUpmztVbd9WfoInUOqUNBLNg+WFT2fBodjqp5j4x\\u002fAOc\\u002feL8ipJr5j72DTCsqMIgry++5ljP54\\u002f9\\u002fuVq4fSKTSKo4f9mLmxnNuxcJYpTC19ZfbH+t2\\u002fPjzy14dHAGDDx6uXPtfX1zvr4cmOHoKDX53c+9kxtVLDteeEjAmwuV0jSDuW\\u002ffED3+tL2PHyX\\u002fjGlp0bZ6ybAHd8hnqONxQiCHKgtn5z2GSVTqtF0a9LLi3wiKyRdgVxnbxt7DWoTqJW0skD7s+xhoJejWqUg3eNtItLZeIlTHMNtpAL9xA3tAgjglz7CxoK+07eopBJ3761jHV7kTelqS6O2HCP2HCPttWSze8d\\u002fHrXRYLBskxtRdvCNWMBoCS\\u002fwdGFR9C6OfOq6juT4vzwNdlNEh\\u002fpJbCzOX+9dNKYgEs3K8bG+nBvx7VZk90arKlB17Z0h3o74QlDvJyqGruMDVZjh8jfXUBCEEDA101A0OrhcVg9MiWNSqFRySqNTq3RfrHnikKlplEpvXIliqFkozkealuEjR3iJ788hO\\u002fKDBZbscwIGCwB3bFF0Xh7aohWvRwZLCp1gmDa8ZaDCALzXZcBgDPDjU8TPO3\\u002fsn5CiTpZNWmwQkYCnVpTRhDRqH3RDD9kfDRQ0w9CQhY+mbzwyWSiAgAAnvn+kWe+f8RQEhjnq5+8IWZK+KATOQBA3LSIuGl9ne7lrZ3XK+q0NBIAFDW1x82LNVmCSKb4+UrWqrFRbDrN8hkS0Oh0VFPrXBWLW11ZXHe2nVqnKxa3NsvEUrVqlIN3tbQrT9g02sHbmWnLp7NDuAO+kHQSxYbFuNBSluwWcrm1Ei8hwNbRQq74CE8mg3rgVM70pGC8pTNUtDrUhkXXW6seqSKroF6vxbABi+w6O9hGBLtdSCP+9BZAUbTwVn1wpAcAXE8pCQhxxXt+9UwZG3jpRvmhs7kEd77hoUkIMmN8yOGzuXklTe1dks0PT9Insya7NeADWbrFMgtOdz93waVblXittqS2LSnKhNV2c+BVNXWhGAYA9W1Covo2hHPLKm2UyJSfbJorkSnPZ5i+vT6ufGc+5\\u002fvnl5BIiFaHmvMnGjOcx4KAM8M1xDbi8\\u002fJ3BDRHB7ojUQ0AACim+6N+R5uyRalTCNVdc1wWOzFcXJnualSFIIDXyxzoTkmCKd9WfYwACQNsk98LxFLuDhimlsmPAqAK5WUGPQnD5Bptg7PDCM9\\u002fcId098p\\u002fuZa9amw0l8VUqDUKtYZGJXdKZQq1RodiPg52Gp2usLHNhWfrwuPQKGSFWsOmD+iglKs1CrWmUdgT4e5c0dbJZ7OcuDYAUNHaRaOSbRn0n69mJ0cERHkSP7PTXINudNSwKXQBw6ZRJsIf34qedgSgSSbG02gx3f7anBU+\\u002fT0qVDLFkWFzs6MmjOdKRkh4CZ1KqYVcNmz65ocmf\\u002frT+Ydf2j1rYqgDn9Ml6s0ubNjy6FT3gSuSmWN0lFducePXv1waG+vb3iXZezzbnscW3w74Lq1q\\u002fWT7+cQ4P3dnLoVCLq9uP3etZEbSEJzVaRdL06+WtTYLEQQxtlYAMGl04OQxgdv+vFrd0BUd4oaiWFOb+Fpm1bdvL3O07x9bO2ti6J7jWdv3pNraMBJj+y2FldkHJSrEjU6jfPPr5VXz4mk0srRXuWRmDCFNhJ9LbJD7xk8PAGCJET4Rfi5ylebj3y9UN3dptbq61u5NS5J8XPhjwr3Xf7jP1cHW3YFHKMEcYb7Ov5zMePabvwVcdoCHAwAYl+zuyFs8KWrTF4dIJARDsa+fXcQwupkm+X++CIX1E\\u002fjJlecRoDEZkwBA2POune1rCDICnoKRokPS++35G2sTYwKcBToU\\u002feFi+rJREYcyC4NdHdIq6l+cM0Gu0tR3iY5kF3+yYuYf13NmRwUJOAPcyfkNrcdzSmdEBHRIekkIcqu2+ekZiaUtHdUdwiBngZfA7ruUG2sTY4JdHQxz4egwDIG+gGPd7Rq+UqdlGDToLPvd9SUMmiszv27P8eySyla1RsflMMODXF56bLq+0fT6l8dzi5tO\\u002f7LJMIserQ79ac\\u002f1lOulYonC2cF25bx4Xw\\u002f7J97ch4dNdotlX++6VFTRIuyRUylkF0fb5AmhK+fE6dtfxoOfy6rb17+y+5XHZ8yb2lfPrSxpCQi11GJFMezwmdyTl4samkVUCsnJwXZMtPf65YkEx9NDL\\u002f5ZWdexaEYUwWNlObs1Z4iTnlu7Y19afbMQQcDH3X6nQbTHf5r7BqsPjaZCJPnYhrUcxWQy+REnwR6DtPcEX525\\u002fvysJHx7z428iSG++9MLnpuZdCCzICnQu7y1s1kkuVnV8MODC8wZrE6JbFq4\\u002f6cnr7rwOFoUnRMdbMdi7kvPV2m0GyaPMiz\\u002fPuaoq2y\\u002feaVMp0NJJGT1Y5OI6vvcZf4BD9F\\u002fAyo10I77ulbXCpjGgf8jUX0vUdspzK1vOZNfDgbugyZhjw5FAaCqvTuvvvVkXplCTfQ3477YqWH+PXIlhoHAhl3Z1oUgSJNIAgBaFD2Y2d+hdh+THNub7uRmFxDqig42hvw+d4P7Nax+UFSi1hQx6IkYpkaQAQ6g4lt1YXHeAFCSUx8a62WounfQoSh5sJgAHB2KIbfHlCo1WsZtD7c5v\\u002ft99PyyNWXuilHXzhW1NAo3vTybcmfdefcZKqafbw2qzRTmtyk7iYr\\u002fv+h0HZ3CTaKeDwCgW\\u002fwyPr8zDqpDC7NqdDpUp0OvnyvUqLX92e4lrLRWAEAmIfqxaXprBQD3rdWgLF6X6OjM5QtsohJ8\\u002fh9YK7lWItF0o1j\\u002f036PY9ozTyGRSQgpW1Q412UKUfcP0itV5t2qLcxtaKzvam4U9kqVCrkKQRAWm27DYbh7CTy9BZExXtFx3nQzseBk80GGBFTqLC7ncZniNAAgCBPDVAjS5+hNO1+UfrG0rVGIAOIf7mbcNzQMFAr1rYzqyrLW+prOpobu3l6lQqZWqTR0BpXFonO4TBdXO1d3O29fx7BID3cve2L+e5X8zjYfrp3t7eW2cttbvbg8\\u002fp0tEXpPweOzAWDKnKhbN6qIuqGAolh1RVthbn1tTUdrk6i9rUcuUykVahTF6Awqk0kTOHKcXXhevo6hEe4h4e7M4Q5KtYBE032l4yCf5owBluSw8M6HkfwDT7Xpdw8BxJ5mh9ewejTSEy0Xe7WyWS6Tm+St7cquLrUw0T4unBuo0CmPNJ\\u002fTorqJDqO82f0hoxJN79Hm8xjAdKdxtbImfRY\\u002fG0+T6QlgGGTdrDx55FbmjUqTnoIesbxHLG9uFGZcrzi4+waVSk6cGLxgaYLxai4sq39mGi1a1PMhivb0yg+ium69tQKA8bMinT3sA8KJwz5nJr5vfHpb3pifPDeaIDQk80blicPZOZk1JodTKORqhVzd3SWtq+7QC7k8VnS8z\\u002fjJIaMSAxhM06bZSr7\\u002f4szxQ1lE6VCYOC3s9Q+WAEBZdyedQvHh2mlQXV5Hm5sNh8dgKjQa+u1qWl5Ha25Hix2DaaXBeu6xX43nh8B5YMOktesnEKUjilSiWDHnK\\u002f30igQ+3rrG1dUu\\u002fVq5XpKXUROX6G+QxFoKcusvnCm4frm0V2o6EFouU8llqu4uaXlJC1wsAQAKlRw3ynfS9PCJU0NHsFon0\\u002fZ4s0N7tT0KnVQ\\u002fYOsef6pNGyxDuFTOTOcJJZKqLGE+lUT153jN5UzZWbMvnBt4qeMmn8ZzZjgcbj63JXC9PsvptivJzhMEdP5PNXvcmM76LPXyZpPpDSnKb\\u002fhpa0p5STNRYR6NRnf1QvHVC8XR8T6bnkv29uuPBbN+bkwK2c3O9lW58jxgOgF\\u002fK0FrbK3M0dUhIYpuk5ddu+PblKqKNqJiMHrEcvwCaTTK7mObeUbD3\\u002f55UpvqKkXCEHuBD9euSyHXorrPM69\\u002fOjH5WlOdO8fWncPdV1bIZzALO9unePoRM5th5rwYcwbrwun8u22wrqQUm7NWAkfb2ATfm1fLXD34AicuLuxoEQ9INBgYBjevlf2561r10B8ArUaXkVaZkVb58\\u002fcXlqwes3DZqBExWy5M3w5VY4+mK8R2lPGCg4bcO0\\u002f14AbrWmemSCNxYzrplCgVgEe1pZGoKGAAINcpHOj2NISy2G1ALLVKp2aSGWSEpEV1YJDFXHocrRb95YeLh\\u002ffeHHY3QF527ePrfnpo4+QVDyThLhoKhcRi0+UyFTGpKUgkLo0agjvdCar89OpLx3LwL8+WT5cTtIZ0mvpp1Wrtrm0Xjx7IGPal4Ti78qz8Xe82o108KkXdue2tY109izrbG6USiVpFI5OdWH3jfmrFwpVjJnbIewfms8SEaaHbvjqjVBA7NwGgpVlUXNAYFulBVIwcKafziaLbzJgdhZCQhHEBFAoZuR2TTV+WMDCVJVqahF99eKIgt56oGCLdXdId36acPprz9IuzYxJ8iOqhE8RJ8GINHjp77zzVpg1Wi6L9Skd6m7LTnelMRshtik6VTmW8GNwkhzEHGk85MQR+NgM6zqY6Je5pOM4g0xLtY5uV7Xq5ufQA0CtVvrllr7kPrPWgKPbLj5dKCpte\\u002f3ApnU4BAC6PZY3B0uk6ukTPo6jQxfF0t\\u002fhlgd2Xhj0SqWcKnnx7oTWzJnR1SAkSqUTxxvN7TS7zN1RGG03A9G9RJuwEQBqlPQDQIOnBP2DVYmF2e7Ncq14XGpPo5vlF1vVasSjJzZuQ1xxMJm38lNCUU6YNR8qp\\u002fLtnsJoausuKzVbqZ8yNgtvzLxrOh+Vu3SLYZ47n\\u002fvDlGZVqxPpqmhq6X9m8e9WDSQ9smGT9oBZjulTN1zuPurH8ASCBb6IOoefeeapNGyxXptPDPkv1u6PtoyjIgJRP+z8AAA50\\u002fpP+63SYjowMqKC6M503+q3CMIyEkOKhLwAXz2IyfY9Y\\u002fvJTf5qbk3cYpF+veHPL3ve\\u002fWElnUHl27NZmETGFERac7gDg4MorzqmzE3AAwDvQ0rByQuW5u0v6yjO762tGpr91dFIgUfQvEengHGgnYFAoALA+Mk6Lohsi4wHgu6lz8QQTPXzGuXlRrO64xJk5N9qcwbp6sXjTlpm0kejxMObC6QKi6DYRMV6u7nz9ruX5sAigKLb9m3NHD2QSFXcMhmJ7fk2tq+l8\\u002fYMlwx4pTUJI\\u002fpyYMO7YQd3t985TbdXzRLBWBAjWBwcBxFyrmJBerda+\\u002feL+EbRWOHnZtR+8fghDMTu+VbVNGi1aKtuj1dYbO90BwMWDX1Paeiu14lZqhaHcGMPKs0Kufv3ZPSP1u9pwGHevijEMcGuFY9IwmRRaJjzay9XNjigFAABZr+qmgc97BMEwuHjWrMEiOJvx+bA6WntMzodlCIZiX7x\\u002f7G5YKz03rpa9uWWvOdfboFBJjDZlbUrb7vNtfxJ1A7l3nmpLluifYesnJ0sKB28JUqjk4FA3L18HOz6bzqCqVVqZTNXWIq6uaGtvFRNTAwBARlrlrh8vWtkBYdnp7u7rmHa+aMXGycJOYt2YgFSiUKm0dDoFRbEPXjs0qCFmMKlsNoPOoKpVmt5epUkPDk78GL9Bp\\u002foflMefTV71UFKPWC4Ry3t6FBKxXNIj7+n7r7i9IVcpzZ7GXQVBYMbc6N9+ukxUAABAyumCidPCiNI7piC3rr2thygFgL5W6gAXj+X5sAzZ+umpC2fM2kE9dAY1OMzN0cmWY8u05bJ0OlTao5BI5E0NwqryVuMOOwI5mTVffnj85XcWERWDodTJmGSbeLvpAFDQc52oHsi981QPzWA1Kdrcmc4aVCPSSGwpNkWSCi+WmwO9v8I8VFIvl6aYr43jePs5Lls9NmlKCPP2tCEE2lvFl1OKjx\\u002fKMu7LOPDnDYLEAmSyA50WjSAsBGEQVJeO5kxfHN8rUVYWNUUn+lv+tHZ1SNw8+L\\u002f8eDEr3USoDoVKjor1ThjrHxTq6uElsOUOqMopFZq6mo6aqvbigsZb6dXC7l69avS4IdSczUGhkOwFHHvBIEP\\u002fUy+Xvv\\u002fqQaLUDI3yAg9WpKGkWnqzS90w2n6FodBKps+O+n3nFZOLJ2anV4mEMiurzNZjoT04YWoo4aljsen4fFiWOXog8\\u002fSxHKLUACaTNmth7IQpoYEhrhSK6TdWLlMV5tYfO5SVnV5N1Blw8WyhX6Dz0tWDn5Uh3epWNaqskubZUvltylqi2oh75KkemsG60J72oPfibFERj8qRaxVOdMGBxlNP+q8jprOOXqny209PEaUGUCikRzZNXbxyjGXPopMLb+UD45asGnPor5t\\u002f\\u002fnxFqx1O2K5O19YpfJpBT0BRmQ5td+BvN9RSqOTWRiGLTWtrHKQhAABdHZLuLunBv24S5DYcxtI1Y+csjOPyWASVHgaTGhzmFhzmNntBLIZBdWXb5fNFF88UiEWyhLH+xNR3DbLFG07geufvq7y+NJT4ccb6wdDeHz0OTraxCb63Mky8oiiKXTpXuGTVGKLiDlCptKmXS4nS2yTPiyZIrl8oSZoWShASKCls2vHteaL0NggJWbFu3PK1iTYc4neRAItNH50UODopsLykeesnpyyEDvz646X40X6GAT2D4sb0V6NKD1YQBaH62kQR1UbcI0\\u002f1AIMlkiskqv4ONS87Xr8OAABCbQPKpTWV0tp13osQQI40nXNnuRDSWM++P673iOVE6W3oDOr7X6yMjre275ZKJa96KCkqzvuN5\\u002feYC8mzgEqdx+U8xWRMBACh+C3CcMLlGyedP5zd3ixe89S0\\u002fjxmKCtpPnE4m1BHmDEnauPmGRzzk5EbgyDgH+jsH+i8ftPUsuImwlfrXqBLVZfRta9dUXmk8U0AWOTxHgJIruh4sTjFkx09wXE9AFxp36HFVM2KkgDOuApJ6hSnJzzZ0QXi02U9V1BAPViR4xweIBSbPDfapMECgJTT+SNrsNKulJnrRHZ154dHEUORSwsaLRssrUb31YfHzX01uTzWq+8tjh3lS1RYJCjU7aufHv703b\\u002fTrpieD0+j0X3zyalvdj5MVFiERuqzmFyq\\u002fUCNCe6Rp3pAXbReJL5cWfN9anpabf1XV9IMVTgxvNB8cRmVRMW7FRa4TReqxCqUGLVkDcLu3mPm\\u002fZEIAm98uNR6a6UnNML9461rhtGXRKNF9Mr2yORHpLI\\u002ftNp6hTJFruz\\u002fSJ746yaCIHPXjHU04xI2ZNe2ix0GPhEymbTljfkvvLlgSL+rISQSEhphwjGp0ejSrle0tAzeB3qXENC9Z7m+yKJwF3u8v9jjffypiLGbH8tfaJjMix0bzp0BGDbZ6fE62S2xurW05\\u002fJyr09Xen3RrChpUxD7MRInBpmrfdRUttdUDuJAGRIXzpjulAQjdzsO24b+2WuHftma8svWFKIOAAAO7L7RUNdFlAIAANuG\\u002ftVPDw3VWuEwmNS3Pl5uIW9JYeONq6bNmUlUqKJXK8b\\u002fsoTniGoj\\u002frGn2jIDXuxoNxcEQdx53GmBfu1SmfHofyqJ0qORJAkSAOCWqKhD1a3DdDSSVV5tAqf+vmUhMmXp6rFDis4wJCjUbeOzM7777DRRMQgkJmMKhmkQoLOYs1G0F0H6BsQBwIqNk6uKmw\\u002fvuqqUa554c75BrkFASMjrHy5JmhRCVJji6pWy8ROCLLd\\u002fDaFQSGQy6eaNqiVLhxDE+M\\u002fDJHOVOgkFoVEQqhbTdKvrReqWAw2v4lo1Sqxl02iUydPDTxzJJshxUk7nb9w8gygdFsKu3pzMGqIUAAAQEjJ99gDHHM68FaMt9MpJJYr9f5r40gMAQkJefW+xh5eAqLAaBIGX3lq4ce12c+2Sv35NTZwYTJSaIaP7DL5aAgA0yYnfDMsM6akeWYg1kShX54N5RZ9dSh3j7WFy9P9jvqvwjTi7cDWqGZ610unQ00fNuiTtBZx1j04iSofC3EXx507kVZS2EBXm0ek6SCQOnRbb0f0YleonsPvaUKuQqapLWsRdvS5DHLH56KapVv6uFRVtBw9kNDcLIyM9a2o6\\u002fPwdGxuEPB7LP8Dp8KEsDIM5c6M8PAYcHUEQgQOnuVkEAGKx\\u002fOCBDKlUuWhRfH19V0uLuKOjZ9KkkMAg5717bmo06PQZ4X5D8XFYCYKQtJgaA8xCLA9BYU\\u002fzsqU6LPP8CAESimkRU2ExyfOizRmsS+cKNzw93XqzboGL5wrNdcPFj\\u002fITONoSpQAci02Yw3vTFXLTDY55i+NHJQ7zG6yHL7B54LFJ5j7GlWWtZcXNwWFuRIUp4u2msSh9F9ihHNyHZYj1T\\u002fWIY8IkTQv0ezAhZpyPF1FhxPCsFQDk59R1d5mND1jxwDgrYxHMgSCw+qGhTZ6p1daQSFxJ7y57u8\\u002fIJD6GDfiIpfx9y9XL\\u002ftmPlq58fIqh3DIRMV5L1yQSpWYIDHT29nFYsXJMeIR7R4ekuKi5q0taWtpy8kTe\\u002fAWxGx6btH9fBjGPATwea8HCuKgoz7S0io4OSXCwyxObpqWkFJ09UyAQcOLivPfsHkKHqfUggARxJuyuffp40\\u002fsAgGK60y2f3RL+XSlNO9n8iVDdRMwAwKO5RNnNOVD\\u002f8oGGVw41vKY15VIIDHE150IWCWXZpjqqhsFF82EHM4zc7YOi0ejMDSyn0SirHxpPlA6LabMiLSy+ff5UHlFkBr21AgBHBtFVZ4EhPdUjDrGG1Sbt\\u002fTEtw4PHxQDWj44zHo4zIpjzHQIAk0kz7poZBmPHB9kLOBbMIgEaLUoofp1MEtCowVJUAtBvMS+fyJu\\u002fNjHvZhWCIJdP5E227vQQBJ7cMnNI90+fmMGgYhhGIiEUCkkmUzOZNDKZpNWabYkAwIWUIqFQ5ulpr9OhAGDHZ+NRMzKZ2snZlkajrLKiM354THV+Ur9NQsizXV8yUMIkp8cAwI0Vju\\u002fiG2Hc6WHc6YbJjEmeG\\u002f3TVtN9bSmn8u+8tlJd0WYunsiGw0icEESUDsaNa+XmentmL4zlC\\u002fpGWd4hTCZt6syIE4dNVz\\u002fTUyueeWkOUTpyDOOpHlmINSyhXB7v4UYhkWQqtem68kiQkVZJFN1m7IQgc\\u002fFWQwIhIROmWurNIUCl+DrwfxLwvwUALudpwxUoKgoaASD7arl+2xrGTgj29XciSi0SGOiy46fLxcVNLq48AHBz4+t02OzZUb\\u002fuuvrjDxcnGrknGhu7z58rzMmpy8qqoVDIzc2iioo2wkKkM5LD8\\u002fMaCgsbRUKZofzeZ+rMCHMBSjdTK8yZButJMV+9mpIcMYzxLhfMD5+ePKPPXo8I0XHeRNFtujql5qzwiDCMp3pkIdawQp0cKzu7WyXSaYH+5LtjSDvbJYbdDQTGTRzyl80c8WP8\\u002ft5vqRlFgETqqyRTKANqyA1V7SlHsuur2lOOZDdY\\u002fTTMXhBDFA3GvPkxGo2OSiWHhbnjkomTggHg2edn4RWuAakBPDzsNz3ZH2aRND6QECP28itzAeDFl+Zotai5l\\u002f+ehWfHHjUu0GTPl1qtvXaxZPbCWKLCalAUu3yukCi9zUzzlWhzcVhqtTY3y3T4Jc+OHRxqlV\\u002fJSoIsllZe0mK9TZFrJVpMY0OxMzeQjsAwnuqRhXiWec2tgQ6CV6dNrOkWanSW2iDDxsKUDAgCkbHeROlwCY\\u002f21E8Gcics3TBR4Mxd\\u002fMh4gTN36YaJRLUp2Db0uNF+RKkVmPywIwgYWytjLES0\\u002fuesFU7yXLPOYAuzwVhD1s0qc1VOX38n\\u002fyAXovQ2pWae3sLcerWZubMTxvqNyHOox9GZayFK0\\u002fq+Jomm+0L7ngJxalrXMXwhZMsM+6m2TKfwcaXKbIQTgQE1LI1Od6asUqJQ2rNZGhS9SzN8V5a3EkW3cfO0t\\u002fBLDBUmk+bmzm9q6CYqhkjM0N0l0fE+Qxoh9f8Vqab3YkdasvNEJnlAXFWdrClPXDLZcSyXytFiWnOj60clBtjx2SYtS3FBY0uzyNxI6UGx0HybYSr8Sg8eh4VP4\\u002ffI5n43XH5OfX+igfhYXd+xHh6fbS64oblRSBSZweSMoxa4F57qAQ8KlUx+bEy8BkWdOSPjIDRJo5mwOgCwviprJT7+TndusIaB5Ur7\\u002fw4cqg2VRFWhatxg1coabak2FIR8vCVlpvNEDoXdo5EebT4\\u002f1j42kONDzAxAJpOmzow8tIc4HATnwun8BzZMIkqtQNarumlm1g0KhTR1ZgRRaoC5OKzqcrPjZtw97YmiO8bGxnRgLQBY8LcQsH7GURxzT3V71zomPUmpztTpOpwc9pIQG7HkG6XqKgAwGTO4nCcAoFv0kkZbg2FyJmMSz\\u002fYlABBLvlQoL1PILjq0k1CgBYhnyWMxne6mtQIACxbE07oZ0azH2ZVHFP0j+Pqb7pIHgL9\\u002fvPDkxPf2fnGKqAD4eP2OYvMd9hnnCp6Z+uGri74qvGH6ZbMGC0e\\u002f21zrzGxWtB1pOotiGAUh00g0ANBiWplWZiE+xkKX8YUzBcOb6\\u002fLqxWJzzbcxSUGW6\\u002fid7T0\\u002ffnZa2N1bPbChUF1l3mANjJ4bEcyNBAAAkcHQ4kEJ4iSMtZ\\u002fDozoQFaaw8FQjCN3RfpeL4wkSYqNSZ6nUmc4OR5wdjihVqSp1DgDweR86OxxycTzRK9sHgGm01QpliovjcQf77Tqd2RqMMcSqeEFLm0qjHeM95JB56zE53SqOkzOPKLoznJy5RJEJdGoN0bNLo97RNCYOt2f+NmbRE9NoDKpkKE8VzoW9NzZ+tCJstD9RMRSGffQ7p1bWIKDznegCDDAulYOvQmJPs9Nvm8TLxyEo1M3kHP9tLeKivPqImMEDBglYmJ7BgtcMx+QEfnKZSthl9pY+smIbUXQ3UarMzuVCwPoZR3EsPNV0+ij9tlpTQadF4fHCNFqkWlNKo4YJxW+gmBxB6CgqwUCn1dZSqSF4hYlKHUI\\u002fG9FgefC436emS1QqEoJMCxx5B5tCobYw15LAkUMU3RlcK+aKxjC1TH4UAFUoLzPoSRgm12gbnB0OENMNBb59Xy117xencq6UAMDomZFLnzL9TOz+9ET2xSIHVztRp2lT3lrb+ftHR4tuVgo7eti2rJd+Wm\\u002fDZZ3+\\u002fdrVI1moDo0YF\\u002fjAqwt2vnlQpVCXZFYnzom5fvzW4x+vjJ4Q\\u002fO3zu5ur25UyVdzUsAdeXUAsFwAACOUQ1XdGo7y1XFKNYugMp\\u002fEJ\\u002fKgCcSmTzOBRB0SQazH0Qvv1aU5JhkJDkudGmzRYAJBypmCoBqutRVxc0ECUAgAA394mfrDJA\\u002fAJ\\u002fJgsuuEEfl2DzZL2T4KhGN7XTFQYYf2Mozj6p9oU\\u002fW01GjVYrDgJgAGAWp3Hsp2mVF1HUbGD\\u002fU4UFcvkRwGAQvbSaErx1T81WtMD3U1CNFgsKnVhRAhmNJxipOgRmfYU4gx7IKU5OOZrznoQhGnHfV2uPM+gj2cyJgGAsOddDNMYhmLpuZVaETc+kCg1gsGkAUBxRlVxRtVnJ14AgDeXfxs+NjA4juipaapqyzxfsDXlNQzDHhv7NkGL4+Lj8MrODe+u3bZqy5zAGG8AaK3tvHwo47PjLyAI8sqirypy6wAgZmKIm5+TTCLf+NGKnMvF0ROCN32ykkKjoDr0gahX1r0y39iralwOXv5I4cFyeT5oA74dahsQzPFDEAQBZI3XIn2aB72XaDHTDTScyTPCf9p6zuTI02sXS57cMgufvN9KUk7nm2tITp0VOahT2eQEfl1mvjT\\u002fFlqtVQYLn3G0RVENADOc1xHVRuBP9aDQaXEM+ti2zsUAGJMxhU6L16FCrWRre9daMtmJRg0FACo1gEGf1Noxj0LxpFK8iUWYh\\u002fhLyzTqs2WVMW4umQ1N0W5mO3eHjVyuIooMsNAyHx5sG7ODGAhQyd4iyccYpkAxmUZTrrdWrQ3dGZdK9cnyblZZY7Dw6SIaylsDYrxwMxEQ7VVb3GRssFpqOrxD3RASggDiFeJK0Jqjvrylpabj1cVf47tyqRIAuAKOVCyjMag0OlWt0qhVmu2v7FPIVDQ6VdYjR3UYmUI0WCbLuXuY8+ya6yXEYdvQx00KvnSuiKgAkMtUN66WDSks8+JZs+FXJqdnIHBiXwaDSZu7YpQtt9\\u002fVJRErDJL8Z0AxrVjdqdSZ6IQ1iclJUMoaOoI9\\u002f1RrdW1CqTO\\u002fr4XE5TzD5TyjT0Mm8V0cj+t3cey4rwC8gm8TsluAeAbtkt4xXh6T\\u002fH1qhSLj2RruHI3aRA+LHpN35E6wEJpEgEoNtOO+rlBeIyFMB\\u002f6PenldRZuLl73DbV9Yu3VzueBVGZ9Qt+vHb2EYBgAVOXWjZpjofnL2dqgracanGWqqNOu4JeAV5Orobv\\u002fRoWdJZJJWoyOTSVkphYQKVP61MqlI9vpvj0tFsitHTMe5GJdDTHFvMGNutEmDBQApp\\u002fOtN1jFBY0tTaZ7\\u002fUPC3a3p81mxfkJVaevh39OUCs0TL8\\u002fGhSqr3Ub3FGJ1Vxx\\u002fapuiHgUdiulIpkahG2JUQQcA2Hsx992Hk5VqTXePzBqLo+fCrYopMQF4gKH12YkGItzFaV9uweeXUsd4e464tQIAjcZExV6P9SvLW4n1BgsAyCRHGjWYsC5h\\u002fIQgw9Xo5lix0pee4HjfyHGBL837AsOwhGnhIQl+Cpnqu+f\\u002frCtt0aq1DRWtD7+5yDPQJXZy6HPJHzt5CVx8rOqsAQAXH4fZD014ZdFXJDIJQ7H39j1NTAEQFOuz98tTb674lu\\u002fE9QlzBwDjoxuXQ7eu2v8PExPv6+jMNdlhn5NZI+zqtXKknoVwU2uqV4DP21HWKurudTXo+zPX53iP484KkGl7lDqFSN02qLUi0CNTbj9+w5FngyCIRqvbezHXy8kuzMdZIlMeuJIvlasi\\u002fVymxgbIlOpfz2RptLo5Y0P83QTbj91UabRxge4OPPaf5281tItjA93CvJ312cW9it\\u002fOZmEYLB4f4eVsdz6rvLmrp00onR4fGB\\u002fkAcYGCwDmhga3SqTefB5RMSKYcR\\u002fgWBPPPSRIZGsLNLcuIb4aXV1F280LxTodSiKRVj85lZjZPCuem73iub7vMAAw2fSXfnrUQA8A8NAbi+CNfp+OOd7e3T\\u002fGGACmr0qcvipRv7vh\\u002fWUAEDbGH9\\u002fFN74621flxmFSTBydUM69CYLA9NlRf\\u002f1yjagAQFHs4rnCZVYM7dZodNculhClAABAp1MmTbeqXzjlRJ5PgNOMhTGG9Vl8wPl\\u002fDjJCsaXa21Lt1eiQXQEnb5bMSwzzdOR9tPsilUJOTgi6klcNABqdrq5N+MH6WXiy42nFjjwbd0fuL6cz544N9XTizR0biqv83QQPzozHa0X67Psu5S2bGOVsz\\u002flo98U3H5jeLuoN9XZePS32kz2XTBusFol0582sMGen3zJzPpgz3drX3WqoNEuG3PKEBHcVy+sSHvsjLXKUL4vDqCoy3V11n7tN8tzoPb9eM+kvTzmdb43Buml+NoWkySEW5mwxZP7K0UQRAI1KfI\\u002f0sG3oqx+eQJTeZajmz0ePClVo0D6Hcr746jjB0DqIFSoNm05j0WnGlQwnu\\u002f6WnUypduZz6BTKw7NG1bYKeTaD9KopVBoWg0YmkTS3TYHAlk2nUvTTlhEvTK3VjvPxmuTv06NUohg24uOfLbfRTMYQ3wk6M1NrG0OjRYt6PkTRHpPrEnJ4rLB4n9QzBcJOiVajo1jRBXOfkcXZlRcZ452fU0dUANRVd1RVtPlbXOAWLE7PYGV70JDM1IpRt7tfaOa7KclkkjXG9J\\u002fnTmYcBYCpsQE7T6V7O\\u002fMZNGpjh\\u002fjo9aLq5m5\\u002fN0GI14Dg0jljQnacTHcTcEO9nZIivD\\u002f+61JhTWuot9PEKL8QL6eth1KnxgXwOSx99oVJ4T8cS2PSqNPiTHdtEW80m04raGnLa25FEOSbK2mTA3xj3a3turIGyz2jsl5LfYjDQGk+5ouA5XUJFz88nmdvYyfgOLhw71urf4sZc6NNGiwASDmVb9lgiUWy7JumRxE4ufCijHpvjelo6xF1SVsahR2tPQBQeKtOb7AsTIgkNzMB6b\\u002fOncw4CgA+Lvz3Hp6JIH0t42eX9tcin17cH1LnYm\\u002f79oMztDqUQiYBwIePztJvL50YqdbqaBQyDMz+2ppp+rlJ1s2Iw4XvPtwXw0h0qzuw2c9PGvfC5KQtk8ZtmZw0stYKAHgWIzmlkhHuHrYcRTEQjEJxt7VZb8NeabwuYXl+IwBMWRBDow\\u002fB6Q4Ai57eSRQNRlFl62tfE\\u002fuAh8FIlXPvMGFKCJNl2jRcPl9k2ZF06ZzZBDPmRFnTkLDjs9087ZvruxMnhyRODvE2GKdi72C2e0ur0Y34Uz0iDHvGUT0kknFgn2lwC2W8jVsrApbnJiEarDqhKL2+8acbmW+cvkBQjQhsG7qFkLZu8+MbhodYaClO1RC58oJS1TfIVtTzEYYNqJoV3F51qrKo6V90tP0\\u002foMpMlLk5uSF0BnWSmZWfLVSgcC6fNx0VgSAwY45V9QsqjWJjy1y5foKHj8DDRzDbYOEPR\\u002fNjVgCgs11CFN1nuBCbhCqtrryji06hBDjYK7VaBoWY4M6xd+C0tYiJUgAA6GgTE0V3hkholQXUahvU6jwEoaOYDDBUpc4jJKAxqBf+vmVrx25t6LbshjNJdWPXl79efOPxmYfP5ynVmsKKlokJAZcyKp57cDKVQt5zMptGJbd1SUZFeG1YNg4ARBLF69+c6BLLnOw57z41x\\u002fg79t3uqyNSzrBJPXZr3NwY0tDjto7tuLjl+4eJUvNyAsnzos8czyVKAQDgwpmC0UmmHR\\u002ftbT3mBvdExfk4ufCIUvOkXy3HJ\\u002fBz9eDrhXb2NjQaxVxwQ0Ndl2+AE1FqgEatzU6t8A5wcjGa1wHDsJy0SiabHjrEEUj\\u002fXyE+cEGOAj8Bf3qQf5y7692wVmBxtg0LEzkMj9Zmq+I8SSRbAJJO163TterQTj7vLcK4nAc2z7C1Y8t7lU++s8hQPihUCrm4qnX7vuufPL\\u002fA1ZELAAnhXnMmhmEY9uy6SZkF9QDQ1C5+9+k5P7276mZebX2LEADauiTvPDn7p3dWdgp7a5tN35ORKmcYVObXH\\u002f7h\\u002fIFvzxanV0m6e39++9DOtw42VbWd\\u002fPVqSWb1+T1pGecLulvFcqny1w\\u002f+3vnWwZriJolQtu3lvfu\\u002fOUOIPjSUX\\u002f07q62+CwD2bz0LAIbZ9elDIzzczSxcdDO1wpwP1Fw0Awzd3W5yAj8EAQsmqdr8is04FAqZRCZlmFroIP1SaVVJC5ff50hJPVuImmrYmpP\\u002f\\u002f4Nokmq7RWdKKsJdnAAA\\u002fz\\u002fieHgJstP7WlgERnaNTLB6MjMSiWdr8wiKKShk0z47hISMmhQMAJlXyvANK1GqNR\\u002ftOJ88LsT29gRGPFumRKakUSk0GkWt0QKAl4sdvtiHr4egqV3MtWEGejviDWc7W6ZcYdprO1LlDIOAKC+vELdlT88kU0h\\u002ffnp8ziMTHd3sv31hN8\\u002feRilXaZSa1vquUdMiju28JHDhuXg77vv6dHCc7\\u002fRViW6+jt9u2W1Y1IX9N\\u002fXyzhaRV7ArADRVtgLA+T1p+uyv\\u002ffyYPkvy3Ohd2y7qd\\u002fWo1drrV0pNGiBzBovFpidNDiFKLWJuAr\\u002fAENeyYtOVuMK8eqJoIAgJcXDmtjZ0A4C8V3Vg5xWtRjdlfgwAXDqey+WzO1t73LwElcXNh39Nba7vioj3CTOY1t1Q3t0hCYzwcHa3O7Dzyqzlo07sSZdLlcHRnkkzwq+dKWhtFHa2isfPjIwa7avP\\u002ft+CWMMikZAkX6+VsZGrYiMJqpHCwreorqZDqbC2X88aqszPbkpAo21Qq\\u002fN0urbWjvldouf08o4WcXlB4+UTeQd+unLgpysnhrhYFolE+uPjdYUVLSk3+r6fxo7K2uZuFMVQDKtu6PJw4gEA2bzTUc9IlTM89AdXylQsGwaZQtKqtQwWHUMxhITgYwPkUiXThkFjUFY8O0slV7Fs6Ew2nTD6x1BOQhCdDtVpUTy6xTC7YZbps6LMOWUvmxq+Y6E9OGl62JAGTgPAvBWjNzw\\u002fc9GasYsGBiuERXoY7hpSVtxsLv7LmPNHsu2dbKPH+u\\u002ffccU32CVufODkudHRY\\u002fwAICDMzTvAadmjEw2tFUHe2dqjVKgBoLGmU6vRNdV2PvrS7KQZ4QDQ2doTGOH+2MtzLh3PMcz+34L4a7Go1NL2zuK2DgB4YXJ\\u002f9+QIYuGn1WrRwvz6hNvh2ndIS7PI3DSyxmi1NWSyI74uoUx+EMPkCMICADuBDYtNz7paNnFONABIxNaOFMVBAMhk0geb5z7\\u002f6RE+13QPqa0N883vTrZ3SRNjfDxd+UWV1hpZAiNVjjUERHntevdQ0ry45LVJv394jMGmjV8Qp+hVdbeK3Hyd6sqaAWDaijG7Pz\\u002fp4iUIiPYeNy92zxen3P2dCUN\\u002fDOUhCX57vzzlGeTCYNEJ2cFgsCBfYBM\\u002fxj\\u002fzRmW\\u002f6DZ5t2pFQpnd7QYUTuol09UrGGJ7sKq09diem4AgarWWTELoDOrmt\\u002fqDLRPG+pPJJJMdkSiKWb9khlymcnLl0eiUFY9NIupMfaVw9HKEhKA6FLf7AIDXBPXYCTg0BtXc2rH\\u002fCYgGy8GG\\u002ffykcQThyOLuac+zY4tFpt\\u002f8m9fKR8pgWe42ImBuXUIqjUKlUVY+PgUPv5q9csyAbIPx93cbAIBOo2x7czkAxIV5AEBUkBuujQpyyy9vduTbvP\\u002fMXH2W8ACXj56bj2\\u002frNwg8vXYijEQ5w2bOQxM1ai0+bunpL9dgKGbogB+\\u002fIA4AHD3sn\\u002f\\u002f2QX2c7QvbHkZIt+N2buMZ6GIof2XHBjKlr7eckN2Q5LlRJg0WimKpl0rmG\\u002fTfAUCqwWQbhnh6C0LC3YlS8\\u002fiHuGx5f\\u002fH+XdfmrhjFZNH2\\u002fXzNcB50Gw4jPNoz\\u002f1bdgDy3OXM8x4LBaqrtTPn7VmtDt4evw7QFsX9tu+Dszg8Id\\u002fcNdiGk9A9z2\\u002fX5maTkcIIPXi8Pifbc++MlTz9H+p0tRWwZtVZ3M7\\u002fWz0Pg7sgjqDAMMorqWAxaZIBp74oxaq1O2CN3tu8PDSmr6wj27o8aMYRosFok0q+vpEmUKgD4afnQovWtJyHRP+VUPlEKAACpl0o3PTfT+BkdBtdNeTHNga9LSCJxwGhdQgAoL2jE6+Hi7l5XM07f\\u002fzVwawUACIIg5sds6n9Kc12KhnLj391YAgBjxwfZcpmSHhPxTVcvDjBYXZ3SMgOfvSFDql7p6eqQNjd0s1j0prouwsIN02dHmTNY5SUtOZk1saNMe47cfRw2vtr\\u002fpXnuw6VarQ7vjJ450PjOWTla\\u002f50wJ3\\u002f5y5Vkcp\\u002fRf2TLTH2aJY+Mxze2fLxMLxweFDKJTCal5tasSiZa4Wu51bUt3VMTAvDdi5kVk+P7pmQwh0ql6e6RGRqs\\u002fSk5b2\\u002foP3NDiM9Qa49kSVRYoo\\u002fnaC93HWqifjsiJJn3W\\u002feI5RZmLLKe1mZR\\u002fq1aotQyCFmhvKxUZVIoAxqtqA4tzKrR6VCdDr1+rlBjpvd6eEQFuRlWi4bNSJXzn4BCJU9JjiBKAQCgKL\\u002fBsPKedqXM5PBDEgmZOms4Xtp1myYX5dSnphSv3DCRsGbtpOlhtlyzY+W2bz2vtXqgmIXQGWNrhaOXUyhkcy3HkYKEII63p4KRKdQ\\u002fHLy+dd\\u002fVyobOiobOM2klbd2Stm4pAJTVtf919tbvpzLzKgb4EHsVqi92X\\u002f7qrytldR0arW7f+dymdjEA9PQqP\\u002f\\u002fz0m8nM\\u002fHzNyxZn5dosCJdnb35dm5cWwRB7sb0Mjhxo\\u002f0sTC6697dUzR0PKtz3+3WTT6o5UFTU1b1Jo61Wqq53ibYYqtLOF6VfLP3urSPfv\\u002fW3m7fA3BNzn38Sc\\u002fUjDMVuXCvX716\\u002fYro9mJAYYHHCX7PYclmL1yau2jDR02guIBqNMndxPEGop66644+dV4jS\\u002fz4nUosc7GxGhXn9eiIj0NNhbKT3zLEhCaGeABDs7eTrbv\\u002fA7ITowD7fBY6kV9krV62bEx\\u002fs7UilkKePCWrrlgDAqbTiuUlhS6dG4b3ehiXr8xJNUlFbR123aFqg38OjiJW9EYRGo1hYXLelWbTv9+tE6VCoKm89b6bJaQ6l6gbH5hFbmw082+dJiA2G9Uf0jJ8V+eTbC5\\u002f9cOnmD5fMWTU0H9Z9DEEHjh+wjFiVr9aZDaPzC3T2MzN4UL+El1SiKMytH6jsY9DFJobH8rWJFhbd2ff79RFpPdxTyBRqNpNGo5IfmjeaqAMwOVu8qwP3iaXj\\u002fjyVnVE04NdRqrQsBo1Fp+Ez05osmWiwPHjcs2WV58urLlSYDpWqKjTtERgq85cmWJjf8q9fruVlD7FBd5teqfKTt\\u002f822V9jARo1rFd+QKXOUigv6dAOBKEbagPCB3wi7jMM1DphpehrsarvQyJRFal0XSpdh1LXjmIala4TACTqUqW2HQB6VAU9qnwtKjUsgYC5FcDysmrwoPP01AqTPWJcHmtMUhBROhKw2PQHTPXu6fn8vaPmIvXvBKVCk3K6QPFPDbSubxWdul6cVdKQXlg3e1zordLG\\u002fIqW7h4T3Wgh3k7f7U8tqGwxFFY3dR2\\u002fVqTR6jAMa2wXH79alFPWlJZfMyU+YNex9F9PZDDoFAAwWTKxdYMvQkEQGnLs16tbvlpDlA4dJxferAUxJ4\\u002fcIioAAABFsbdf2v\\u002fh16vDozyJOotIehRvvbCvwfxareagULy5nE1yxTkSiWPP+4yovs1QA0dHluLWjjAX070n9z4optGgPWSEDgCN0gMMslOr7AyNbKdFe\\u002fmMUTJNLYVkiwDSpDxgSw+jke0kqmIH5iRiKQZMmRGx49sU4ymJVCptUV5D7Chfc6ulTp0ZQaGY\\u002fVjeIXMXxV+\\u002fXJqbZfpzi6LY1x+dKClo3Lh5xp2vYIBhUJRXf\\u002f5U\\u002frWLJQqFOm708+YGh48sXi52z62epN9989Fk\\u002fRwMCyYO8C0untI\\u002fJYMeP3eBp7MdCUHwKsszKyfoVe881j8DhIvA1rBkHCODRaOaXHtCIpLt\\u002fvKMvTMXAUTcJT2y84pULFvw8ISC9OrWuk5HN768V7nmuZmGKr4Td\\u002f93KQq5yivQefry0Qe2XdCqtVOWJPjeXj927fqJF88WmvssKOTql576c8NT0xYsG2W5l0FPdnr11k9OtpuaSNcaqBQ\\u002fJmMigrBIJFu9sKNFLOqSttR3d7aIAaAwq2Z4ButsScX0kIA7nF\\u002fsz8zcTxYkE6X\\u002fERgUJxrJnkMLBgCFptGDsxzFVEpdO4PC6ZBfdGLN6FBcYVJcmFQPuabOnbNUpe13tZrElsscOz7IZJhVblZtZIzXrUzTrQTLi9HfIQgCL7618PG12012YuKcO5l3M7V8yaqxcxbFWfDTm6OzXZKXXZubXZubVdvdZakS+o9haFMImJySgWpKCGBingZCyUSDldfcSqdQQpwc9ucWLo4MpZL7yr14KGv68lGuPg7fvXKAJ+DMfSCpOLP65vnCrtaeWasTy3LrFO0qADBU+Ya4hcR5M1g0ea\\u002fq\\u002fP4Me2eui5dg\\u002f\\u002fcpr\\u002f7wEF4m395m4+YZ33x88vbBiWg1uh+\\u002fPnf2eO6S1WPHTwkxN+uQXKZKv15x8u9bRXkNBBX+EbMmzlina+sUPs2gJ6CoTIe2O\\u002fC34\\u002fJhB45KVaqtl28gCLIwMhQA++XmrTqhOMHTzVfA35mWhQEsj43wsbc7XVze0iNt7ZGuiIuo6uzWb7tybXekZWl0ugWRIc62nG+v3HDi2Jj0CNwNLDSoURTDMMKIQGvBQNskPeDOWc5njq4R71DpOm1o\\u002fnSyQ7fupi09FEHI3YobZBKbzxxTKdoq19TZMxOJRQwkeW6USYNVmNdQXNBo8lvoH+Ti6+9ElI4oAgfOe1+sfOnJP80NhwYASY\\u002fi1+2X\\u002ftx1NSbOJ36sX1Com6e3wLjOpdWisl5lW4uosb67qb67saG7uqLN3Doa\\u002fwsMMFgane5MWaVEobRnszQoqrdWAKBUqJhsBpNFJ5FJl45kizol7n5OOi1Ko1MoNDKdSQUMAwBDVWRiwOeb\\u002fwyO8Z730Pi\\u002ff77i5Man0akrnpzefzyA2QtiM65X3kzt79Yxpra644v3j33z8YmgUDcvHwe+wIbBoGm1Orlc1dkuqa\\u002ftrKvuMOmqQBB49f3FV84XpZhf6VePSp3H5TzFZEwEAKH4LQxTIwgN7iBwtEehlCrVW6aOc+TYAECAo2BDYjyZRPr2ys1V8VGuXM5bpy5+OG96q6Q31sM1bJTje2cu+wr4+u0gR4ETx8bTjrs9NTPa3WVxVJiXPe\\u002ftkxeJh7ECrRYVi2SyXqVcppL1qmQylaxXKetV9UlwYa9S1quS9yplMpVMpjJuZ+lJvVQyc9z7LBaNxaazbRgsFp1tQ2ex6WwbOhuXsOlsdp+kLw2bjkefB\\u002fFfwv3uAmaSPWMscnvhA0fWFACwY8TzGDEACAIkQ60F4sf42ws4xrWM6opWc40yC709I0hohMer7y3+4PVDFkw\\u002fAGg1uqz0qqz0vghnJpPG5jBoNIpOhyrkKrlcbeGH+N9kgMGiksmPjYnXoKgzh9jjmzQ7es\\u002fWc+5+jnQmlUIlt9R1KeVq41BAQ5VSruoRyoQdkpLs2mlLEv76+qyzl31ApKcvDHBgv\\u002fLuouce+7Wmqt1QaIxWixYXNBabGitvjrXrJyaM8W+o7QIY3GDRaBEi8Xso2o1ivVptvUKZAgiVxZiBa9MvlSQlRwCA9VGj7jzus1MSd97Inujvk+Tnpa+RKDQaNp1GJpE0ur5nkU6h0CgUfFe\\u002f3atSu\\u002fI4dAplY9Koy5U1bDqNRTMxf7Y15GbXvP7sHqL0DsBQTNarkvWqrJ\\u002fp6UzaG7jDgnQ7ItekPUKgT2hSawyJhEyfHbnvjzSCXKXS7vvDREczlUq2fk2wO2TcpOB3P1\\u002fx\\u002fmuHLCx1TkChUCtGboz6vYNUpfoq7QYJQRaHhoY4OnyTdkOp1Y728Bjt4b49M1Oj0y0MDQ1xcDhVXt4g7mmRSuYEBYU7ORmq9EURm4T2bNOdsh7+Ti98vUY\\u002fhCJxZqRhCLJ3UJ\\u002fba8K8GL3qp3f\\u002ffnvXoxwe6+ste5\\u002f\\u002favVzX67Wx+8awmTRPvhq1Qub\\u002fhjZiu68JfHrHp0IAFbX\\u002f0lMxhQM0yBAZzFno2ivYUdhWW4DbrCsp7Kj+1xppeb2BzbM1emzC6kzQwKWxoR\\u002fcymNSaPODA3EVQdziyAXZgQH1IvE+u1QF4fvr6a723EjXJySQwK2XUv3teczqXdxvMV\\u002flBlzo40NFgCYrHQnTgiyEAA44oxKDPh82wPvv3bQesv+\\u002fxKxUilVqV4cn+RkY3O5psbbzm5xWCgA\\u002fJaT42Rj48Xj\\u002fZiR8e3cuS1SaaSz88NxsW9fvFjW2Wmo0hdFNFgWsDyEQo9eNW1Jwt87L5Mp5AWP9PUCGFsrHIGj7Vc\\u002fPfTas3+N1PQyi1eO3ri5zzltYXIIQyhkFxv2CqL0NiwO47MX9gmcuQDwyAuziGpTBDjae9vzSLfjb1fFRap1OhqZDADvzp1muMDHg6NjPO24ZBJp181b+m0A+Gj+DC2KUkgkAPh04UyrJ6T938Ld0z40wqOk0Kqq94x\\u002fpD1oSHCY2\\u002fY\\u002fN3754YkbV8uIuv8ZPLjcLUnjdmRlT\\u002fLx6VWr7Zh9rrpetdrN1pZOJj8+qi\\u002fSyoHNYlAoKIYZq3CGYLCGil+4u5\\u002fVg0v59jZbdz7y49fnTh\\u002fLIeqGAp1BfealOdNn94+64PJYJt0cQ2Le2rFai2tWm8TQCQgAuLUCfAqH28bHm89j0ai4hTLcxsGtFRikv48xM+dFW2OwBA6c+FF+ROndh2PLfOfT5TdTy3\\u002famjKyzQg9fIHNjNlR\\u002f2TlcUhUdHWdrazU6HQYYJN8fN66eDGvtS3C2WlRaOi3N296cnkRzk4AA0YOmFPdRYM1VOgM6rOvzh0\\u002fJWTndxcGdWmZJHFi8Kbnkh1vLyuvxzfAadgGC8OwX78488iLs\\u002fHdw7uuLVnfHzYybLSYloJQAGBqUP8rZLhtTK9Wer3r6iSHaQwysS\\u002fpf5yJ08J++PrsoDOpTZ8TpV\\u002fB2xwanY7wmbGG4oZ2Twcehzkg2JjA2PFBCWP8L54tPLIvvba6g6geFhQKaVRiwMz5MaMSBxlg\\u002fO8SKBD42NnpWxtfz57d33RITtZvb4jvG9j0+cyZBJWee8hg4cSN9otN8L1xrfzU0Vu3MmswU54IAnQGdcKUkEUrx5hb6MkvwClrKFPNGKLTooZ9+D0imUlP3JCQaiVnW0\\u002fF8Uf5sv2EaiGfxpdqJTQSTaVTdak6vdm+Uq1EhapQDHVm9MfE2VA4FISqRlUMMkOLaetkNXyaPRkhG6ZskNdzKBw7Gr\\u002f\\u002fYP8DMFm08ZNDBu0LnjEnmigCAICKli46lezlYCfslf9yIXtGdECkt0txY7sT10Zgy+6SyGQqNY1CcTFYH9Qwl0KtUag1eLRRfl0rAuDjxFdptHK1BkUxb0c7fRYKlZw8Lzp5XnRxQWPqpZLrV8o6hh4ziCDg4+cUk+ATk+ATGePNuJvTyIwghM+AoRkimCRDjFUINqQhwv8sPWJ5XnZtUX5DY113S7OwV6pUKtSAICwWjWPLdPe09\\u002fZ1jIz1iorzpt3BgGSJSrXi6P46sfiFMePWR8UR1QDfvH4ofkKQX4hrQ3XHlRN5L3+1iphiiIjUwuMtR6Y4zvBgeR5pOrDYffnVzkseLK+bXdfj+AlBnJBjzYc9WJ7FPUXLPFYxyf31\\u002fAvt50bxx9hSuSK1sEPVntZ1jU+z16fMF+eSEKRSWrHAbbENZcDbNSRQDPv5QJpKrY0J9Qjzd9l76pakV7lsZkxuSWNTu9hZYCtTqNcvHStTqP88mqnR6mZNCPX3Io4Evtcw9yvfLKuvaRcGugoSAjzaxb0\\u002fnLmxekJMYX2rE4+TWdm4ftqoP6\\u002fcCvN0Ti+v3zw3ic3oiwQ0zKXW6rafTV+aGJFb28KkUS8XVr2xbOqO8xlBbg43yupfWDjBhmG25tXaLCorbq4sa21tEbW39oi6e5VKtUqpwTBgMKlMFp3JpLHYNHsHWw9Pe3cvew8ve09vB+Nwrf8dhv+e\\u002fwNweayJ08ImmlnZaaQo6mwv7eoEgOOVZSYN1lPvLLp0PPfswUwnN\\u002f7mD5cQ1UPHjsbnUGw9WPioIwQAUAwFgBDbsGBOKL4ba5fQq+2Va2WGBktPvbyuW9Ul18p5VDt9ygZ5HZ9mL6A7aFGz8YrWkJ5b6+5sN3ti321fMiMqr6w5Nbuqo1u6YFpkcWVrl0gNAKcuFznwbdyceL\\u002f\\u002fnfH+s\\u002ff6zDbmfuU4f\\u002ffq9u6C+taEAA8nng3fhhXk5nAmp2xpYqRKo63vFKEoNiHURyJXtomlfs72xrloFLIDlw0Afk78k9llQW4O+NLq06MCe2RKiVxlwWC5uNm5uNlZGWlRkFP\\u002f1fsndu7fRJDj01IThDgFOfXffnJq5\\u002f5Nd+ICbazrenHTH5IeBY\\u002fH2nPqOaL6n4VY4\\u002fofJNzBKdjegU2lrQgxHbhAoZJnLIl\\u002feMus2StHW165elCu1NeWdXcCgA7TXeu8AgAeLI+jzYereisAAF9CAsc4qL1F0VzdW5UhvKFCVV2qThT6OgH0KWN4cTJtLwBmSyV68YaETKHmcfqs5LnU0os3K2xYdJ0OpdEoVAqZTqPilXKZQs1i0mg08gMLRxlmv9vo7+GQMPcrV7Z2IYA0d0vwXY0OPXSjcFSA588pmdnVTcFuDgiC\\u002fHU1N6+2xdOBZzJXbbswr7blbE65FkXFMgWFTFZrdQDDHAxgGQrFRO\\u002fLoyt\\u002ftBCeajLLkPDwFuw7\\u002ffyWN+YTFf8GJpqEz\\u002f12YkVi1JhATwC4UV5\\u002fOL3wyweJ38+C+tbfLt\\u002f66iGi\\u002fHJxdU2bcP3UBIJcz5\\u002fXco5nlUyLDNg4fUBv5f8C9T3iibt3rQiN+HTyDDDwu+swHdm6OElDUExHMsqFYiiCIMbGbkj0ylVf7Lro5sQN9nVWqbU5xQ32djYkBOnpVcyfElFa3V7T2PX0uoltnZJdh27iycZEexNLuTsQ7uGIoNJo6dT+pgbud9ehKO4h3nry+tOzx+lH5Ooh5AKAb06kPjFz7KlbZSHujiHu\\u002f9AY9a5O6eo5X5+52ReXe\\u002fe4eKZw1\\u002fcX\\u002fvUalokm4ayYoNTSWtxgpZbWJkcHEVOYZ3KY3+QwS11d6ybE0ikUkczsuND\\u002fx6Q1NRju4tYKAIZhrQDA2FoBAD6R0B1iw6K\\u002f8\\u002fRs\\u002fSj5iaP8DYeq+noI8A1nB9vXnyAOpr\\u002fbEO7hiECwO7h7WB9cMjHM12QHHCEXAKwYF3Uiq8SVzx3UWuXfqvv9pyuVpS0kMsnDS\\u002fDBN6t4duzqirb3Xzn4wTerv3z\\u002feEVpix3f5ttf1vMFNgDQ0dazef0vkh45jUb5+9LLeCFqtfbZR39trO0EgDnjPsSFp2+8gZ+tySw4Jo9eVtT8+09XKkpbtFqdX6Dzky\\u002fMNDfdmDUsnf755lfnHvjzRk1lm6e3w\\u002fNvzAsIdgEAC9d48M8bRw9kSiUK\\u002fyCXJ55PxtMbQ7zpADAx1PeXS9n49q2a5mfnJAHAoZuFZ3LLdRga7+f+1MxEAOjulW\\u002f5\\u002fWSnROZix\\u002flkzWwEgb3X849lFY8O8HxubhKePauqadu5GxQSSaHW7Hx8KYtuokfDuGRjfkrJuFlRDwCTQv0emhxXUN\\u002f684UsMhnplspd7Gw\\u002fWTMLQawq598ltbGOKLqH0ZshcwPrcf5JawX\\u002fxj2M9nEliszgwrddmtgfAGiOlkbhq8\\u002f8tfLBca+8t4hCIRcXNPLs2Liqq0O6Y2vKY5unu3vaV5a14m8yADg6c\\u002feeei7jesUnb\\u002f2tL4dGo\\u002fzwx4bSoqbNj\\u002fxyKu11Qg3LZBYwf3QOlzk5Ofz5N+ZRaeSd31746oMT2\\u002f7YYJhxqOzYev7VD5Y4u\\u002fL+2HHlvZcP\\u002fnbkKfwMTV7j2eO5507kvfvFSkdn7um\\u002fb7369O5dB580ORWiCYNFp1K8HHhN3T0ohvk48ulUSmO3+FRO2S+bliEIPPrj4aKGdgBoE0l3Pr6ERiE\\u002f+P2B6vZuf2f7VUlRHCatsrUbLwdFsTf2nvvzmRWO3L77boxxyeGexMD03NqWvNqWXzctB4BNO\\u002f+O9XUDgPKWzhOvPqQ\\u002fOp1KHrScfxeVTnuzuZEovc9Q+P9xDw\\u002f9dTM0wl0\\u002fz9\\u002f4KSF6lVqtXbxqNL6Wj7kVK+4Qc0d38+C7efDx7TmLYl94\\u002fPdhz8mBkzwvJjTCHQAee2b60pNf5GXXxo32AzPXeOCPG+s2TPQPcgaAlQ8lHdx9MzOtcvqcqP7ibmPCYAHAzOig1NJaFMNmxgQCQHWbsKFLvGH7YVwrU6mZNEqwuwMee2Jvw5KrTIzY7JD0ctkMC9YKTJU8UA8AUN3eHebhhN+7UHenitbOQBcB4ehN3YpByzFEi6L+P35NEK4Oi\\u002fxo0oDJJAx56vzJk5XlD0bEvDthSodc9nNe9qW6mpZeKYphLjacRHfPR6PifHh2hFw\\u002f593C+6eqxUItigLA\\u002fpLC\\u002fSWFhGTlj2+mk038Ft0K+W8FuZfraxskYoVWa89gRjm5LAkKne7rb\\u002fJZapJKkv7YCQA56zfxGcxuhfz3gtxzNVXNvRIMwxzZNnHOrg9FxoQ7mLDmN5sbD5UW5ba3tcmkKq2WRaW52nBCBA7jPbySfQNsaKZ7G4Z6hnqqRcJ9JYWZLU2N0h6JSsWh0flMpi+PP97Da7qPn4tNf1jGsO\\u002fhMH5lPYWd7XuKCtJbGttlvQDgzOYkeXg+HBlr\\u002fBPDEJ+N+tqusEj3gQX04xsw\\u002fIaYNZg7ulgk2\\u002fNLam5WrVymwlBMq0VRFL0Tv5jrbfPHYtPtBZyWJlHcba814Rq1Gl1zk\\u002fDjN498\\u002fOYRvbC9tccgST8mXhIAGBfs\\u002fe6BC1oUXZ4YCQB+znwXO86OjYtJJESrQ0kkpKihjTyYu0TAYYtlii6pTMBhA4BJg21cMjEFQKCL4EJBJd43UNTYNiHUBwAIR7emHEMQBAniC4RKhUipwN8BK6kSdee3tz108ohI2e+GqxWLasWiI2UlO2bPH+\\u002fh3Z8a4LP0VPXtWRmGytmayi0Xzso0\\u002fca3TdbbVlN5rqZygqf39zPm2tLN9pe39\\u002fa2SCWPnjraJuvVC\\u002fHzXBjY\\u002f0nHQTHspUvnDpUVGwqlalW5UFUu7DpaUZrb3vrBxGmGWpzhnaFGp3vv+pXdRXmG3T0ipUKkVFSLhCm1Ve62toYGa9j3cHi\\u002fsg7DPky78mt+juHp1YiFNWLhnuKCLaPHPRFrulfUymfD8nRiVPOjdEcEc0d\\u002f96UDbBvGx9+tFThwSgoan330V2KKIaLTGv5kA9ZDI1wjimGAYR9+szoq3lsvNGcrTRssGoXM5zC1ur4psTzsecvGRj66\\u002fRAJIWEYtu3RhcQMADoUfWPv+Zp2oUylahNLnpgx1tvR7p3l05\\u002f\\u002f7SSNQtbo0O\\u002fWz6eQyO8dulDV2q3R6WrahZvnjDMumWEUAhrp5RLv5\\u002f7wDwcwDMaHeEd5uxTUtxLSWFOOIWQEObfqQXy7UdIz\\u002fs+fB+rNUtTZ8diZYz0q5ZKg0Kk+fvZMVpOk51BZ8c3mRoVWsznldNoDG5iUflfdjQcewwADAIVWO+HPnwFgQWDIG+Mm6hPgGFevLtXXbDp7AsUwJ7bNgxHR\\u002fnb2LCq1QdJzsLQot731WkPd42eP\\u002fzl\\u002fqbku64KOti8z0oRKxbyA4AQXNy6d3iGX5be3ZbQ0jXYlfmD3Fhfg1irayWV5SLiHrS2dTOmUy0u6Oq431ud3tK0MNRHwMbwzxAA2nTuRUlsNAFQyOdnHP87F1Z7JEiuVDRLxtYY6qVo90dPHMMuw7+HwfuU3r17YU1wAAP52\\u002fLXh0YF8exTDyoVdfxTm1feIP72ZqkOxp+JN9HFb+Wx4egsqSlqImYcL\\u002fmKjOszKAUUmj65Wa0sKGnFrBQBNDSMw4LH5diGyXlV3p9TVzUTNFIdGo7i686sr2xMS\\u002fYk6I4i\\u002fsZ4t8waMmFuQELogIVS\\u002fG+nloo9p0G98vGamPgFOYpBXYpCXoeSTNcSpDgglm+TRqaMendr\\u002fWTN5dGvKMQnbTGPHJD0qpUQFP81aMMO37+aOdnVfEhz2+JnjZ2sqhQrF+ZqqBQZVGAGrz3Eo0\\u002fSNdGNQKA6sPierOWQazQsXzqIYFunotH\\u002fRCkMLuDoscsvFs4fLim80NRyrKF0cZPqS37x20Z7JOr3igUC+vaFcZzBLhJ6zNZUA4GLDObR4peFgiDn+gS+OSWrtlRrWd3CGfYZ\\u002fFeXj1srfjr9rziIvLs9QC+NAplETznB495CAlb9yamMdbq3Ge3jvmrNQP149ycNrdVjkgyeOZLY0fZWZNtHLO8KoWW3ls7F49ZjHV2\\u002ff++v1GXOjSGRSaWFTVJw328ZEVdQaXNzsKBTSlQvFSZOCZb1KgaMtMcVAzB3djm+Tf6suMsarpqp932\\u002fXidmGztkTufFj\\u002fVw9+H\\u002fsuCJwtI1OGPARIrBm\\u002fYQfvzrn7esQHu0p6VHkZtZMnRVpctSRWYN1H3PMDwzRP5F6nogdhb\\u002f2+R3thgZreBwsLRIqFQDw6eRkQ1uA81rihMNlxXgycwZLrdN9O2MOwVqBxVkfyAhiGLmqx9hawXDPEAP4MScTAKhk8s\\u002fG1goAANhUqyzLXWJHbjYAUEikz6cm660VDpNC\\u002fXxK8uS\\u002ffkEx7IdbGT\\u002fONBFIac2z4ekteO+rVb\\u002f\\u002fdGX3rqsUCtnH3yk82pOQhcAPX569cr64t1ep1egWTPqEzaZvfm3u6HEBAMCxZT7zypxff7j07Sen3Nz52\\u002fdstJzF3NFfeHvBts\\u002fPHNx909vP8fk35r381J\\u002f6o3\\u002f5\\u002fvH06xUyqVKrRRdM+oRtw3j53YVRcd76BCZZsGzUT9+cr65s9\\u002fJxeOuTZZa9NNNmR6qUmh1bU9paRBxbZni05zRTHne4xw1WU8\\u002f3at2AmGZf\\u002fruGu\\u002f8KCwKDiSIAP7s+F6NIKR+oGQ4X66oBwIHFDhE4EHUA9kyWA4vdKZfltbcRdbcJFTgmuLgRpWYY7eqR2ljfJJU8ff7ka4kT3TiDfKVhuGdYIexqlkoAINnH39uUtfp3UWg1eC\\u002fkWDcPZ7aJziIvLi\\u002fO2TWrtflqQ53xRAJg9bMRP8YvfoxffwoAAPALdD6f+RZBiLNpy8xNBovOE5g5P2bm\\u002fBiC0EIWk0ePH+P36+Gn9Lunrr+u397ypgnTPCjunvzvfnuUILRwjXMWx81ZHEeUGnFPG6xO2VGFpsZQci8YrBB7E6+ovhNNY7Vz1wKFne0A0CmXeW\\u002f7kqgzQKHVKLQa4woOAEQ5DqGzaX107OnqipKujlNVFWerKyd6+iwKCpnu48+gmH08hneGRZ0d+MZoN6If7V6gUtjXCxnmYDbyM1TgmNXaLNdoqkTdwUZPwj\\u002fwbPyPY\\u002faJNAmKqaWqW0ptg0bXrUVFCEKlkLhMijeLFsKgDFKt\\u002fX+DHcPEaOQRRIdhPUolUWoGjQ5lmvoNTXbPmYNJoR5Zsmp7Tuau\\u002fBypWnWpvuZSfQ2HRl8cFPpodJyHLZeQfthnKFL0dZ85sUzUX\\u002f51xLd79yz8xHxmn0p4+1oMsZDxPiOCqYfdFCLFlTbpHz3KDBQz8TsBAJ3iYsec6sBewKEPXq\\u002f7T2PcEBhZsNvDO8MdnN6fOJWgJWDOl2zSG2UBBoXy7KjEDTHxR8tLD5QV5be3SdWq3wtz95QUbI4fS+gUG\\u002fYZorcHrg7x7P5pjMbX9qMfe0scWwgAd\\u002f\\u002fZ+K9wKOVFomiEGNxgydQltcJ3JKpsomIgKm1rm3R3m3Q3ixbsylnvaLPo\\u002flQQw4NCInFodKlapdRqYpxMj6i6S7CptDXhUWvCo6pEwt1FeXuLC1U67RcZ11lU6iNRsfpkwz5DHqNvIqdO+Qh4+kac\\u002ftqTeUck3tUA9ytTIwf+CTBl\\u002f00wiMESys9XdD1nrlZlErm6rKr7xRbpLj\\u002f+Bxx6\\u002f1N+H+sJc3BMb26sEYtESsW\\u002f8mL42\\u002fHfGT9lTVjUkiN7JSrVzrxsQ4MFwz1DvdPnVmvz6rDIgcp\\u002fnyC+gEGhKLXa4tu+NmNwNxyDQvG\\u002f7Ur\\u002fB9hevS1blEUQPun\\u002fTAxvJN+vX2p33uhOIwiXua9IdiaGIg2JdmX7D9XfNSuaDIU74nbpR++nltSw6LR4f3cA2Hry+qZZYy3MUm2pEtQpO1LWuWlI1kqPXF1W2La8XvQJig0ySuZ\\u002fBPrt30CiGtz7M83bDwBQDNtdlE\\u002fU\\u002fYME8O0ne\\u002fkCQFuvVN+awxneGYY5OOJBVaeqK1p7pUT1YAzpHg4DKpmMx6xmtDSZPL26HnFuWwsAjPfw+idbfwn8\\u002fiBEPTmiQRo9QwLFdPk9eUQpQJxdAlE0FHLFOR+UvkOwVoY0d\\u002fcUNbTn17VcK665UlRd3NBOTDEQszddrimv7n4D4E76NdBmyY7CtqVKbQNR878HhURyZLEBILu1xXAsi0lWhUXaM1kA8G12Oh5maUxRZ7vYas+3BXQYltli+nmSazQFHW0A4GzDITjFhneGZATBZ\\u002ftUarUbTh\\u002fDh+kRkGs05u7PkO7h8NgUO4qEIFoUfeHiWcJ4IIVW89KlcxgAArAx5o5e46ESwY2ik4i9KPnifBQbzoglk5RJy2RaGUHow\\u002fYV0AUEoZWgGHq46eAPVd8pdJZqPDZMOglBRL2KdnFvl0T+\\u002fPzxFqpXYK5JiIGuvPNJFLP0PpAQJgAMWv+SqYvyW+cFCr6xY04m6u4BdBgmVanwbZVOZy5KYESY7uv\\u002fV1F+p1y2+ujB9dFxLjYclVYrUipESuUDEdGGKdlU6vfJc9cdP6TR6TacPprk4TXJ08eRzdaiaLdCXinsTm9ubJD0nFv1oN4rNGy0qG753\\u002fvdOLYTPb0jHZ3dObZ0CqVHpawUdh8uK64ViwDAuPk27DPcEB1\\u002fpb42o6WpqLN90u5f5vgHxji52NLpYpWyXdaLDx76Zc6iJI8BAyT0WH8PDbH+V45ycn4mYew3mTfSmhpm7vtjXURUEF8AAKXdnfjQHADYEBMfb3WM24hAI9EieVFZwkxDoVwnK5OWhdqGGQqHTa44hygyU7OzBqlW+lP1D2XSUqLCCC6LsXJ8tFKjkSnVFvo69Jg2WF2y44QAKBwa2dGZs5bHGM+iBZMQOgBgmFala5Gpi3qUGd3y0xpdNzEPgA6VlnZs8ORtcec+QdT9G3Qr5KuOHpCq1RKVyvBDfbis+HBZMe5RtqHRxrp5fDYl2SDfnfL8qMTrjfX1PeL8jrZnzp\\u002fSyzk0uvHLNtbNY8+CZU+fP9Uu673eWH+9sZ6QAAAog40\\u002ftwZ8etJmqWRPcQE+KoXAspBwk8N9h3eGFBLpl7mLX7l8\\u002fkRlmUKrOVRWTBh0bRnr7+Gwf+VnE8ZSSaSvM2\\u002fUiIXvpl42VJEQZGNMwktjxxsK\\u002fxni7RIIBgsAckTZI2KwMMDyTBms+GG1B2tk1T9Wfy9Si4gKM4hlivcPXIj06uu92TwvaaB+ACYNFtbc8xNRBuBq+4gn7yUSMqAfHUEoDIong+Jpz5rtw39LKD\\u002ffKP5Grqk0TAMAAGiD+HOltsaP\\u002fxGCmP643Q3yO1rdOVy88aJHrdNVCE0YVhwtiuIzB7iaGpJyJ9gzWSeWrd2Rl3WhtqZBIlbrdBwa3Y1ja66jbZSr+7V16w+VFl+oqy7p6sT7p+wZTD87\\u002fmg3jzl+gfoQ6juBRiYfW7rmRFVZbltro6SnR6XSoDomheLKsY11dl0aHGYhYn54Z8imUr+bMefRqLiDZUVZLc0tvVKFVsOlM+yZzBCB4zRv3zgXV2Ke21h\\u002fD+\\u002fkV34ybnSyb8BvBTlpTQ2tvVISgjixbRLdPdeFRwfbD7OJdIfgrUIV2ldPxMkV56zxeuAOZ8QGgFpZjbF98WX78Wn2BOGgXO64uK9xj24obdWatu7HZoweHWhVIKeJOd1l6pL81r4RxXq87F5xs32MIDQD2i7dVyf+RIea8FBwGWOCHLZTSIMP\\u002fgCA3JZphIpeoteA3XZZLwZgz2SKlEpHFrugs82FzWFRaQqtpkEijnJ0+bu8OMbZ1ZZGZ1NpLOo\\u002fZyj\\u002fc7x2NWVPSX7dEy8QFfe5Z\\u002fip5gfjStarwW\\u002f42fgThEPlUNOBs22nCcLlHitnOJke3GMSNar+o\\u002f7X9O6bBDmNRNOgGny+DT2GvYStIun7By4kxwTSKRQAmBkbZJiSALHGDgASFfGm8JnTrLZWAEBy4qyOdjltMqahR5le1LZcrWsjKobFkYqSP4ty01uaTleX7ynJ75LLd+RlVQi7vsm6odRqcT9xXY\\u002foUHkR8761us9\\u002fnHg7Ew3zWyPRV5grvkWQIICYPJw52pXtH5W+Z2ytqCTqk\\u002f5PE4QESAiSHBMIACqtVqXVEtUDMdEklCiJER+edkP+8NIp7uHO++tFn7ZIfiao5JqKwrYloY5\\u002fMKl+BNVQoZMptjb087WVs3wDrzXWrQ6NUum0GGBJ7l6Jbn01zP2lhZvjE++00nwfK8hubl5+YD9RCrB5zNjNY8cSpfcxwJpbF8GNNNUqvLXcY6WhZKg0K5ralcRgAl8bP77V64fniXN21e407g1kkBlP+W\\u002f2twkgVK8IOPFspkb6lzV1xvu74yukWcBEDUupHeA9ZVL9WdRAQ4mVIED2tnstULCVhBB7slTa1sK25b2qIYTwmIROJjuy2F1yeYSDU6Kb57acjIyWJgQGTJPy8pgJp6rLO+TELtv7\\u002fKepF4un\\u002ffErUfrfIbetddWhA0SpRWgkWiQvmiDsVHU2yhsJwiFhsn\\u002fQSnc7HruwzVTsApfKfTHo1WBOiAbtm8XMHF0S2St\\u002fnPn6eCoAfHjwIiHij4CJGpYWFRvu2tLjDXeHioA9j0H1KevYoNYNsOJaVFTcvibY8WcuY4yhfEisCYsCgOk+\\u002fgAwwcM70c2TEM63NDgcAF4aPUi3zstXztWIhR9OmP7O9Us57S0sKm2eX9ArYycYdn4\\u002fce44gsAXk2d9dPPq6ZoKmVrtzeVtn7nAh2uHJ9Cgum23Mo5UFLf0Sh1Y7Dl+QVtGjTPXfW6SjJbG7blZue0tvRq1HYMZ4eD03vip7hwurrV8AloUPVxefLyqtELYLVIqHFjsGd7+L42ZwDZoCwuVim+yblyqr+mQ93JodF+e3dKgcP3ComQEqRJ1v5d2ObutmUGmxDq7vp44SX9197kXiLdLyBJmEIQ54mwPlgdBaD05IpPtQasM1vaabcbZAcCV6bY54Hl7mj0AaLFBDFZebcsDk+Mu5lcCAINKUWt0FqYLNqHQ6sSGu1Syg+HuMLChhUe6\\u002fF3asV6mHhCXocPkpR0PBzlss2NOMZQPmzsJPi7t7nzw1OF5\\u002fsGLA0Nz2lt+L8rtkMt+TJ5vmKatt\\u002fexs0dt6fQXRyVpUTS1qc7Npq\\u002f3AAN44tzxtKaGhyJi\\u002fO3sK0XdvxbkFHW275m\\u002f3MpxyEcqSl64dMaDw300Kt6VY9sk7UlvbnQaOCuThROgkEh7SvLdOdwnYkZx6Yz0lsbfi3JRwN4fP02f\\u002ffFzx6pFwg1R8S42nA657GZzQ7u8v2OEhJDWnDiY6Ob5btLUll7pjrysR04dSVn58J3cVQJtvb3vXLlU3yPW6HQrwiM2xMYDwNtXLoUKHI5XlIsUirmBgZsSRm\\u002fNuHm6soJKInHpjO9nz7VjMgEgpab6u4ybOhTTougXM2ba0ukoBi+lnCvr6tSh2NczZwWa77+7Ulf71c0bOhT14\\u002fM\\u002fmDLNlk43eSYE4Qxf\\u002f\\u002fXH\\u002f77wwMMAkN3S\\u002fE36zd2Ll4KpEzYpzG1r\\u002ffR6qlyj5tAZH0+d5snlAcD56qrP065zGfRIJ2fDM7QS061C0a0FrosMJdbTpepqkBPjUXxt\\u002fOysaw+Osx+fK8ohtPiieTGP+mxkkPvaVYPWsMI8nbeeSJUqVCeySoQyhQVrBSYNFgoDYoiNG3TDgEZ2Dnc6UN61SaxINZSjmKqs8\\u002fEA+y8F7HmG8n+eXrX6hVFJD0XEAsDS4HAyifRnUV5xV0eYwFGfJqe95YmY0S+P6auvrQuP1qvO1VReqKv+MXn+LN++5rMT2+bd65cu1lXjFUDLyDTqt1MvenPtTi5dp+\\u002fNfCaO6PexcAIAcGzJWv32kqCwZqnkfG2V3mCpdNqslqan4sY8HtPnTN0QNaDurEF1c\\u002fwC3xrX9\\u002fHg0GjvpV3OaW8Z5TJic1e9nHJuiq\\u002fv9rnzAUBh4F49Xl7284JFTAoFn45qdUTkM6PHIgCfpV3\\u002fu6zkkZg4ANielfnR1Onhjk4KrZZKIjVLJHVi0WfTZ8S7uu0uyN9xK\\u002fuLGaa7tLrl8jcuXfh75WoHFvuX3JyPr1\\u002f7eOp0k2dCEHb09ltzAoQTNhaqdNq3L1\\u002fcvXiZLZ1+urLinSuXf1mwSK7RvH7pwuHlqzy53PeuDojwshK8VUioZDUpmjpU7Y50J0OhlRi72wEgwWp3exQveo7LvJOtx\\u002fWSOS7zFrotNoy00GKD+NFd7DjPzE26WlSjQ7EPVg8S+Wji40lG2Ia7OlRiuDtsyCR2iMMuB\\u002fZighzDtBVdz7X37iPI\\u002f3mSfQL024sCQwEgrYn48dkQFUeQ4JyuKWdRqTMMShjv7gUAN1sa+hOZJ62pQapWPRoVP2jshbkTMCbY3qFd1qu77RGgkym+PP7+0sIzNRV6IYFVoVH67UhHZwBoko7Mrw8AOhTNbG5eG9F3CKbB7IDT\\u002ffzxXbw2d7Wubu2RQxtPHLtcVyO6PbhnbVTUM2dOb824KVWp8GTONjbxrm4AECwQmBzlg5Pb1hrh5IzPAb8wOORqXa3JMzEpNAfhhI2FFV3dNSLRqkMH5vz157bMDDx4qFoodLHheHK5ADDFx1efcUiYbKyZbJdZg3FGBJAhjR9c4LYoghsJADQSbaPvE4vclhDiwrToIAarVShRqDWrJkQvHB1mvJ42ARNqKtlei\\u002fbod5Xaun7dnYEglADB51SyfYtk50ANWt39mg6VutpuGCj\\u002f50AAHA3aX642tgDQJhswApZNpfEHxqDqqe8RyzUav+1fEuRWDverl4gBYNAJACycAAAUdLbtLsrP72jtlMvkWg3eQ2y4rNP2mQteuHTmiXPHHVnspcHhD0XE4kPz9LgbTI6Mz2g+vPW1TNJnI001kA2tQ61I9HX6jfNrH2TTaN9lputPYFFw6GRv38MlxUsP7P1m5hx7JtPQuJuxwAADpwbDDYfJMzEWGur161\\u002fgmDRnhkIMMHdb7qk16wz0gAGmL3PYDe0IbiSdxFChA56rHNGtmc6zDSXWINVKqnqJMd5+Nv52NDuC0AIIIBt8H\\u002f+h6rvlHis9WV5ENcCgQaRUCvnI1dzGLvH4UJ+ZsUEcJp2YwgATd41B8Tbcld5xX95AEG+7V73sXiGKAepEHzeIvyZK\\u002fykwU082DPxWEFYlMATFMD6T9cOM+YS\\u002fByNiiUlNgR9u0KUeLZzAlYbaxUf2lAs7H48Z9fvcpedXPKz3pusJsLM\\u002ftmTt3vnLx7h57MzLnrTn54v11YYJhtRFMFQoJFKsi8v+okJ8V6Y2PXpZolLZ0ulsGk2l016s6Y8Tbpf18hiM9bFxswMCc1pbDHIMQoyLa0lHR4dMBgDHysuSPL1Mnomx0J7J6pTLe1RKAEipruorzjoC7QUSlSqzuQkAMIAuuRwA\\u002fPn2zRJJk0QCAKkNxMq7leDjCgnCWlmNWCMmCAclV5RrHHAwjPGDLDLrhaCXTVorsMJgCWzZLy6a+PX6eTQqeemnfxLVAzHxoWDTQkWKS\\u002fpdta5NrilnUS2Fnw4VN9vHqCR+dferGAy4mKae73So1If\\u002fJsFS\\u002fDO0Gaxn1dwrATMLxpjEy5ZX2t05zdvPgk2xAD4NcY1YGOdsdlSKZXblZ1NIpD3zl+tXnZFrTTs7x7p5jnXzbB4tWXPiwNupl6Z6+RFT3DU+m5H81uVLuwvyUQxbEhqKu7oJRDo7B9kL5u\\u002f9i0Onj\\u002fPsC6YDgNcvXmjs6aGSSY5sm41xCZLbg5kHxY7B+Gjq9A0njupQ1IPL+2jKNDBzJsbCTQmjFuz9S8BiTfP1qxOLCSVbgEGh\\u002fDR3\\u002fvvXrsjUahSwR6Jjl4WFs6jUD6ZMe+joYT6TNcXHh5jHahLsRhHcWBhguaJbkx2nGgoHxdiBNdT2oDUMarBUGu3FgqqLBVVcFuOTB2YR1QMxYbC4jNFNPd8bSjp7\\u002fzZZJ7oTHG2WUkjciq5nUGzAk9cq\\u002fU2H9frZf4yA2Tdfg+qoJLPaYXOmpuKRyD4P0dGKUgAYdzv6dFBm+wWdrC7\\u002fvSiX4MnGrDO949y82FTqroJb8wOCjRcEtQYNinJodL21EikV15sGuM8IZ+LGsY1zdjtRVWYgu+u4cWx3zV9IEL47aQq+odHqKGQygsA3M\\u002ftbN2qtrk0odeZzfh6Y0Y7JPL36AVwV7+qG998RUGt1Nwpr\\u002fd0E4zw9j3muMVSZPBNj4ca4hI1xfS\\u002fw4\\u002fF9VQ\\u002f9CRuCCxs7xPa2LBaDBgARTk4Hlq0gJJvpHzDTv8\\u002fRqS9wqIRzI0y0CsVDM1gKnaJUUkIQ+tsE8Kg8gvAOQQeboqq+U6zVoR+sSWbSBq\\u002fgm3g3OPQEMomjQ\\u002fvdN+29+9y5T5FJA7rY7xw+a3qo42+lnRsIow47eg\\u002fpUFmg4BtDoZ5upfzHwvQ53sExDq7t8l4MMPv\\u002fa+88w9s2soV9AHZSpEiKIqnee7Uky3Lvjm05sVOcxOn9ZvduymZbkr0l+23LbjabLdn07KY6dpw47r33omarS1YvpAp7Jwjg+wEZgkBQplzvvU\\u002feRz+EM4PBYAgczJw5c0Ystfg8RrczRiqPlowbZZipKpGkbtQQJ1PEyhQDTntchMKF+QMkESkcmwMV8\\u002flv15ztd9hyo7Q1Q4b1TRdWpGbmMqYIJ2dFWubKtMzfnTrSYhopj4knSLLHbt3b1b7+9nvD6aYpRKL\\u002fnL3olSN7V236\\u002fK6sPL0sYsjlPDnQ++u5S8LcDmteQvKZwb7XThxamJgy4HR8eOG8Vio1e9x0hgtDhpeP7l2UlJakUAp4vIYR45a2ptUZOYwybhlYAH\\u002f9i4PKCAkJ8Pw9c5lJXj9msrn06rE2PFDVtqgkg9rkjpUUDJ+H8lD02IXOB5ayB+ZbjtWfa+4VC\\u002fkVecnLyq\\u002fb6OFI7aVZ+clpcRp2wnVFiAqLlEXnJnayWh2troBLxp9glJyEetuF4Pm7MsZ4sLV5MC5BHRExNSeBAIbz2TvRT6awTA53ZqwmM3asxUwOd5Q8pJUWOBUWigijpMuHnZtoSYCwD9g\\u002fTFT+mJHr+qAQz8jXfdU0\\u002fBgrLo3Jvbt52BFgKE0aP4Hb\\u002fF6qG7K5s8Hp91foEztspkS5cntX8w8LKlQiCZWTmXpbUmaAIN6oOfbW3FU7e1rWpOZu6Wxak5pLF4siyJe3r\\u002f3ViUPrmy5K+YJH8qe9MnMenXpFEIC3l97+aUzt1831Oy61CnhoXIRiaXJ6+PGq7s8piIuQv193\\u002fh81Z3wBXC2RlOhiI8Pe\\u002fOaZ4uk2n3dre\\u002fOXjRfiFYqni8oyVJq1W76iM8TJFalK9ebWxlGPW8jjxcsjfzpjzpOFHIOym8\\u002fuMy2zC1IWlWYAgN3l\\u002ffpQndPjK0yLnVuUumF\\u002fbaJelZeiB4DmnqEv9lb3DllKMuPzUvR0EkGS72055fMHSrMT8lP0X+6rtru89y2elh6v0ankfcNW1uUo7l1UXJwR9+r7OyvykugrzshN+mTXOQzHV87MTY\\u002fX0MXOK0plVqw4I46+ij5K\\u002fu53p1AEqZyVCwDbTzZ6fNjS6Vll2QnsS14\\u002fylTlLIVFkHidtXa2Zg5TOAmc84NlqrHnwevFvF5MKOQDgNnkdDi8BE5EKqVut18o5Gl1kQEMb2ke1OoUWl3kYL\\u002fZbvdmZcfYbO6NX5yatyg3Jy9uQtGheXPL0d89vOJIQwdOkIsL06lDdiYGHAoLAGLkDzMVFgAM2j\\u002fQRtx9I\\u002fbykgnzCvSbGoce9gUGmHKr9wTzkCZGKteIZblqLVBrCWWifb1tMoHwsZxSHx7osptV0WONRaeuSMpqMBn7nDa73wsA96Tlf9vRYPa6tZLxPqMvEMiJit6wmt2Hp2E5kQaDIsjjBSWPh2dl52RuQvLchGS29DKTV4CPoq\\u002fMnP\\u002fKzPlMITP6QrRU9s6ykCX8bv7S381fypQURuuve\\u002fCGVvvJLMVsthSgd8iyZm4+9T8WwHuMll8\\u002fPfbULivPOlI7ZvDOSdKlxUU9umI6D0WZSafquxJ1qlWzxj4\\u002faxcV17b1H63rSI+frKez41TTzlNNi0szmFf86kBNtCoiXqv8186zlbNymcWyKkZfZUVFttPj+9Hdc6KVEedbemflpxSlx\\u002f7hy4M3VGFxzhXWWqvDVFgYgdXbxqYXaDLkmZGXx4M8Hlp9tlOvj9TFKNd\\u002fepIgCH2s0unwZuXE1pzvevLZhS6XLxDA\\u002f\\u002fn+keWVRa0thswsPSAIhuEOh5dSc1OCIMd3VJoc7mkpmTBfKZnQMydIX\\u002fvoSywb+fVCzE8u0H8jFYw7MU0ORuDr2+oAQITytNKIUa97VkzSP+pPnx3qy1WPu8\\u002fRqQVR+l6HFb\\u002fs46cWS4c9rmLNVZq3v+eqGfBMWOpAE6tRdBnM9KFWFdL4wLm5lsvjV14euew63bz\\u002ffGuERITjk41EAGBRacYrjyxZXJYJjCu6PH6ZWCgS8B5fOYNZLAWdjXmVWE3kD++c\\u002fdnuqjONPQAQFSkVCfkEEdbrd9UIUEFR0Fxho72B5QQfiiZ7I0vZwUR\\u002fUYGAFxU9drMqtSw+Iap0eipBkDNmZWRk6YeH7e2txq5Lwy6nt7XFsHR5wbSyFASBaK1CqZKlZUzBhdXjx37\\u002fzaHNp+u\\u002fO9Pw+28ONVwppntIXZikfNnqOcmM6e7w1fRY\\u002fpiseoWR67oh5Ony9Rubh590+GrZaUH8x\\u002fRFGIEDwINZ0wBgaUIGAMyKSWLFt2SmPpk7PUAST+eVA0CAJPgouiQhnZn5e24CIlS2beANuUADAAu1j9PyFRU5v\\u002fl0f137gFIuWVkxblbrG7ZuPd7QMTCaHq+ZU5gKADlJur9uOr6kNEOlkNJJswtTXv\\u002f8YEOnMTdZx+ehfUNWjw9DUaTHaNlxqrFv2JoSo56Zn0wXSyEVCYJXTVXOyv1w2+m4aGVOso5Z7LziCXOpzKt0DIwerG7HcDw4tNx1xOrwYBgepZRR9jvgGhViBFZvu8jpWcqihmt+sOTyeBAA+npMjRf7PW7sDsb28SiCfPf1ub6e0SW3FVSf6yQJEgBmzEzf8PlJrS5yzdpyPh\\u002fFA\\u002fjOrTWVq8MdZ7x89yK3b9zB5f65xeNpXHAE8KPpNP+30THBLULEjymO2Xvdre80BOlpGfmB1XOMnXAZVgC\\u002fq+PIQGeSXJWiUNGSXxzZ+01LQ8ezP2Hk+p4pc8UYKR7cQVw29Mr44+1PEcAJPo+7y8\\u002fEH8CFfI45Yvp0LIALuDKED7MmoWrFvAoWwFEUoQaqV8fkTTdscny6+WysLpIk4YE7yig9ixHYi3XPsTpK5eqKZ1KfZUqCIUjipQsvOAMTDMRZ8uyfZV3BE+Dj9w49\\u002fsxCBBnr5uI4weOhAECSQJIkrUlZdvdWR8sbra\\u002fThxTMAH6hcAUcARKT85UooyMyWRMnq16WCFLoQ5kwv0C\\u002f+cZpKwBAEUlO9Ec3el3hgrhUprYCgD8suO17bXUTkPDkMr6K+mOnAXDqhWA4tRUwTr9GbQUTaxKqVsyrCPi8a9FWV8Rq9xTlxPF5qNvrpz09BaigKCjaDOfcH4s2ZytLW0GIFT8sKmZloOj4oJzSVgCAIEBrKwBgzRJeHVbMvMv4dbXl5JGRHUzv1slaGUUkmZq\\u002fUbvjqCWL8\\u002fUbhLwpjE6vDgThZ2re0ssfZid8z\\u002f9+TP7+XYN\\u002f3T7wp3bHhLHM90xOZoqWJGFo1FFemMRUDcFahtO7ikUt1\\u002fxgOP6ieYU3cBqBhStgT5Nl8xCeD\\u002fcyR4EhbVgUMmFehuYNu7c6Rf3LybXbdQVNVf9KgKr6bH9jp1wtBEm2jo5eHDI2DA8P2OwDDrvZ4\\u002fFgmC8Q4KGoVCCQCAQRQmGsXB4fGZmgiMyIUhfHxKrCdkq4CkZcrnMDA9WDA10WS4\\u002fVZvN6XBiGk6RUIIgUiRKVylSVanpc\\u002fMyEBGrz0ZvGJbP5WHd3rWGww2wZcjldfj8JECEQqKXSNJU6T6edl5RcoNMFG4Cu2Mu4YNmzPOZHKMI7OPRhunw6cg1PVLfVcqavv85o6LZY++w2p9\\u002fvxjA+ikr4\\u002fCipNCEyMlujKY2Nq4iPlwnHnGlvAjei6dwev8eHlRUklhUk7jveXJwTTycVRBaKeWIvPtGD1FJNrUYORXDEvix5tkKgYAlvLXGSZIO3z+IfLYiczhwSXkFhAUCUdGWUdMrrKq+dBOWLMmGuL9DPTpgip\\u002fv6trW0HOzsoNZzBYPjuB\\u002fHqVXK7aYJ7mApKlVpbOyilNT5KSmc612vAgzHt7Q0b25qOtffz2k+dPh8Dp+v324\\u002f1dv7xYULKIJUJCTcl5+\\u002fMjOLF\\u002fSgX0cIktzS3PxpXW39EMdMjQXHLV5vh9m8r+PSW6dOJURGPlhY9FBREXMFcsSVVQNix0aEPImf8F7dXi92n29jQ\\u002f3mpqbW0VF2GgBOEL5AwOr1dpjNR7q6AM6L+PyFKSmPFBdXxN\\u002fA3sENbbpv99YJLw+yGi8ZmEkCVFAYWXzOfIYprLPWPAKPhWrebleX2T8+IUtRpr5y9+qG4vL6jVYH1ZFKj4mihPmK0lRZNjMbhKOwwqTfaFVHSqWS8XbHMNxsc+k0EzQ3huFnartSEzVxeiVTzolauowtChsSYGtz87vnz7F00JTosli6LJZvGhvFfP6i1NS\\u002frawM\\u002fjaGD0GSX9XX\\u002f+PsGWPoQEvBECR5qrf3VG\\u002fvX06f\\u002ftnsOcszwnX+mBJ1RsMvDxxoHhlhJ4Sgz2Z7\\u002ffixT2prXp03f1XWmKd45JU6pDM1a6vM2zDCW65eE96apXH8OP5BVdVH1VXhryIEAF8gsKe9fU97e2ls7GsLF+Vpw126ED43uuluX1yglEuo\\u002f8sLkyekAZSpprMUliPgaHe0Zcq53feD5wdRBC1lzA\\u002fefLqHLcH7Eg77Bg8Pb0+QpALALM24h2DIjmgoXntrB+Vj8p9\\u002f2sZ0Njl+tt04Yh\\u002fPB+D1YSaLiykBAB4f5aHIyaoOljyYw6dar9qZpWFoaM36L1\\u002fas\\u002ftatBUTbyAQwIlr0VZdFsu9Gzf858EDU9JWTLoslh\\u002fu2P6D7dvMHg877dr4Z031vRs3hv\\u002fK0Ridzud37Xz1wH4qCIxaMvZehcLk65sb\\u002fdAi3VMefGqNcHHIuOqLz\\u002f986uSUtBWT6sHBNeu\\u002ffPPUyTAdFMPkJjSdUi4hyDEzTkIse7KCGhWyhMFaiSbYwT0zIkvOv5XjQWpfwhdun0P9UUIU0Cx50UzNEqa2gqvoYZUWJNU29qUladRKmceHfbH5LBbAl8\\u002fPA4Bdhxu8PmzRrKyS\\u002fEQsgG\\u002faWZMQq8rNiCFI8uOvTvr8gWn5CbPL0qKj5P1GKwC4PH769PTkaOZVWjuGvtpW1WewFufGd\\u002fSOpCdrewfMKoUkI1X39Y5qkiTvWFqYGKtmnkLzUXX1GydPYNcvkBPF\\u002fQUFbFHYHOnuemHXLnq39Gth76VLDcPDH65ena2Z0GJXzevHj31QVcWWToUN9fUGh+O92+8Q8fkKkSiUTiGB6HU3xEqyAaDVcSJGks4Lb0vd75qbXtk\\u002f9mJfCzhJ\\u002fuPs2Xrj0Du33x4qUGLtyKCIx89Va9e31a1NL5h8jf1Na7qWjiG3x19WkLhpV819laVMuzvnqLDGUn1\\u002fwgNMCYXBO2j0ThhUwlXFk7leYDh+8MIltx\\u002f7+tRFo9XB3JdQiIoHPN397k4AWBU7fi9T7mHNr8g4WdVx4nzH\\u002fIqMXYcaotUR0wuTPvv2DABUTEt57rGFe482AYCAz1s8J3to1A4AZ2q64mNVP3psweyyNGZRrNOZZKXpUhM1D66ZXpgTNzTiaGgZGDU7G9sN2\\u002fZduPO24h88NG\\u002f9lvOsUwAAJ8mX9+\\u002f73bGj111bxcjl85KT2dLw2N7a+vSWLddFW1EM2O1rN26sGRxkJzBwenyDZvugeUKfN5i3z569xleO4mh394927iBIcpL5gVb7yXbHmT2Gt\\u002fcY3lYL48LUVh9XV\\u002f9kz55r11Y0x3q6H\\u002ftuM2c0LozAd3a3\\u002fKu56g\\u002fVRztt5sm11c1sOkWEuKVzyGR1GUcdwb7+wRrH7Df1uLtZQuDqXqEIyvQXvcmQJPgCAR6KrCjJgon7EuJkwOo3Gb39Ru8EK\\u002faUe1iKCLHb429qN6xcmH+xeUAfrRAK+Q\\u002ffNaPqYo9aKeNclOD2+CMvD8KZuNw++nR2GozHe5SIBQRJIgjC5\\u002fNcbp9UIuDxUCxo\\u002fzIS4Me7d+1obWXJOUERRCOVaqRSMZ9PAjh8PpvPN+JiD2Bp1ublXd14cH9Hx0t7docKSQwACEB2dHSeVhunUEQIhADgxPwDdnvLyEjTyEio8YvL73\\u002fsu81f3rO2QMfhaNI9bPnkYFVeog4A1s4OOWG0v6PjrVMn2VIGPAQpionJi9bGyCNEfL4vEBhxubsslmrDYLD+PdjZ+btjxybZsDZbMVcpjNGL09kJodlQX\\u002f\\u002fbY0fZUgYiPr80NjZFqVRJJEqx2OnHLB7PgMN+tr8\\u002fuIY0VQMDz+\\u002fa9eHq1azfVIDyflBQ4cfxGJmcKQ\\u002fmJjddvF6Zk6bbfbRp9ZKC4McwX1HAOVeYJE1mSiDE\\u002fKCcf4WbvXEI+bzV5XlsKQAAWLDRGVELBj29BIkTJE57mU5ZYQFAflZsR88IiiLLF+T96+tTsTpldhr7tek3WnccrO\\u002fsHU1NjK4oSXnzgwNN7YbsNH1inGr3kcZ+ozUpPmqS0wEgK1X\\u002fzudHF8zMitVFjpidCTGRnb2jqxYXfLD+hEQsWDSLbVN8\\u002fdixK2qr0tjYZenp0+PicqO1wZH2XH5\\u002fu9nUPDJyuq\\u002fvVG8vbSpCEWRtXv7EvGFxyWx+ac9ueg0jC41U+lRp2V25uaE+rRaP57vm5o+qq41OtpsfADj9\\u002fn\\u002fbtnXbgw8Fn46iyKycpKVFmcEPN43Z43n1wH5udQgg4fOfLit7uKg4KqhwAMBJ8nhP94dV1af7epnyf9awP+AsHJiJUljtjjMZ8gp28kRqBgf\\u002f69BBtvQy5XHxz5ZPnxmfQI0jWOAkWTs4+M65c0e6u9hpAABwuKvzr6dP\\u002f3jWLKbQ5HUrL4f6mIRb0nSl+Yml+YkuD0fHUIAKiiKLz04cFdZaq++Mu5spsfjN3S52a3DuN1FnHkiSqVSi8frXWwaTItQKwQRjGafwKmjsGzJaHAVJ+p99sjNZq\\u002frVumUAkCRNdwTsXtxj8g8zfeK5l+ZUD8zTSFdGySojhFc23IRau8AiVLZQcgDAMFwQ5DXLWgdAsa\\u002fj0rPbtjElLG7Pyv7RjBkZUWMzpleEBGgcGtre2rqjrTVDHfXJXXexc1yJAEGs+uKLNhPH7DsAPFBY+Oq8+aEsKUx8gcCbp05+XF3N8TsBVMQnfLl2LbMtjjV21naOjxZpKyaLn+\\u002fb+01jI1sKAACZUZoP7rgjUalkJwSxq63tlQP7g7sMLOilOQeHPlysexoAjo18PkezDkU4dA2F3edb\\u002ftmnnBMUKonkrRUr5iUlsxO4ONXb+6OdOzgj6\\u002fNQ9Lv71+Uzuqjv1p+ZoU8Q8wRULJBQ3OSme7KwRCjkmaxuADhwsuXhNRwqptZa849Lf2MJf1\\u002fwx2jR+I0cHj74Ze\\u002fnjHRAEfTPRX+NmNjDumAeqDX3L9RnRIsj3DjW57RkR+ouWgaL1XEiHn\\u002fAbYuTRroC\\u002fgBBtNiGKOGI1+kO+AmSTJFHAUCtqR9BkFR5FKXLrrg0Z3dNq0YuPdXSU1mWs\\u002f1807PLK5iR\\u002fPyET4iK6EPuJ8YX6B+wfzBg\\u002f0DMT4ySrdRIK2VC7p4bhF67wCJUtlByAAjWVgCAIOwl+xav9z8OHGBKmGik0r+sWDmLEWw3HBCAfJ0uX6d7ed48G9fjfkXeO3+eU1shAP+9cNEjxcXshBCI+PxX583P02p\\u002fundvcGftTH\\u002ff+osXHywcH\\u002ffNy0udl5fKyMJBu8m0uamJLQUAgCyNZuO99ynCi8O1MjMzSal86NtvwmwiPiKqtx6Q8BVWv2ESbQUAb5w4zqmt0tTqj9fcSW08Ew6zEhM33nvfo5u\\u002fDS4NJ4j\\u002fPnzo2\\u002fvX0ZIFcanvN5wV8HgasQwAflE6fzz3ZW5+0\\u002fUZLR4vdu5ij0Yla+\\u002fhno7kHBXWWeuW6sYdg+qstYxEAIBseQ5LW23qqlWJpI0Ww0J9Rpt9ZEvPheXxuTwUPTHUES+LjJMqd\\u002fQ1rE2e9l3PhTsSC2jh5x3ncyJ1J4Y6Xy5cetjQJuUL9w+2\\u002fmpauM6buQna1789rJZL02OinF6fgMfzER4\\u002fPqbKqy0nFmhX0Zkne2gAwBvoHbC9N2B7T8xP1sgqNdJKqTCbnelW8\\u002faZM6GcQpOUyq\\u002fWrtWHEfMzFAhA+EH4aEZcrnfOnWVLAQDg+YqZ4WsrmtXZOQ6fn3OI9McTxyszM5mVvNhtEAn4WXHR35yqXz0jVxA0+H3v\\u002fHlO65hMKHz\\u002fjtVhvnIUeVrtX1asfPy7zewELuZpH+5wnvfj7tti\\u002fp2dxqB5ZOSr+nq2FCBCKPx49ZrwtRVFRlTUm8tXPPTNpuAbrjUYDnV2Lkod0+85au1\\u002fTF+Ek6ROGjEx4zg3v+ly0vQeH5aZonW4fNMLktjJABBiVNhgq6cVlo\\u002fwtjpamKkAUBY0HuxymtamTBv2jin3WdrUiuhkANBJxt6gVQn5u\\u002fubTD5XtDiCFhIksTw+1+r32DFPuiJ6a299jlInDjvSd1K06o+PVkaIRQDwxOLpfB56dGgfHx3rZPW4LzEzh+zdsPAGuvtt\\u002f6gzrKwdXNJn\\u002fYsba2fnmDpnTdz9appWR68VY38YWRidji8vXmBLAQBALZGsv+eatNVV835VlZexUShNSWzscxVXsN2E4qGiomVpHBZrh8\\u002f3UfW4BQTD8X117euP1v51+4nuYUuwtrJ4PDvbuI19P545c6rqAADmJyevzs5hS7kY9fUMeTtM\\u002foEq02Tj97fPnuVUCr9fuiyc0VYwMxMS7i\\u002fgnnz4uGaCHVojkQlQntHt4JwnuVVNN2Jy\\u002fP2zo2fqumqa+thplwmeK2x3ttK7Ljfbm1iLolGEV6IqZUoAoEKb8lbj4bMj3dQhNSnR6RitNvXt6Gt0Yr44aWSTzViojmMKgeEEHCAIi98tQHh+gj0tNglyiYgkSZwg9Co5AFRELZofvZL6u01\\u002fDzNnuFqQxoN19tn+1mf7m1SQQY0WJQKOtygcmuxdcVKtEOVrRSoP7vPifqPXlCFPaLH3aMUqrUjV4zJKeSKSJCQ8cb9nWCmI0IiUrEI+v3Ah1Jz3n1esiJHfEG3lDNhxMhApULMTAADAjWEb6i+ypQAA8NrCRVc320jxXwsXHu7uCnba+LSu9ofl5ZRFTMDjPbG4zItxqEuKHW2tnC2mlckeLipmS8PjuYqKbS3NHK\\u002f4RKrM2xKlBSJUZvSG\\u002fOD1Wq17L3GkFuh0lZmZbGnYPF1W+hXXj3Kmr7fXaqX1oMHlePvi6UR5JAnwTF4568e6VU2HouiMouSFFZPNouQHrSv0E\\u002f52Z1uuIg8AguOL5shzIvjsjuQ8XdpsbQq13Rzdl0uVa94qH7fh\\u002frZkbIBGC3+avxgA1qWWAsD6jupfT6vc1lvfbh\\u002fOU8ZcPmkyhqzOl\\u002f653en1aSMjPH7six+vkzEGqnpxPCNv2D2sYNxYe5\\u002f1r7WDy+oGV\\u002fTb3vZgXewcYdDrMn7de9CNe7tdhi969vgIzOp34CT+SddOKsOgZ3Sf8dzJ0Yt97qH1vftt2ATPA5wkQxkUVmZmhmmXBYA665nJQ+WzGPD0dDjZHWyaXW1t7on7blIsSEnJn3RpSADjeBmYxMrld+XksqUALr9\\u002fe+uE+pxs7j7W2PWXbSeYQoq9lyb0sWnWFRQGd8fCJFWlqki4spVQzJMnSPNN\\u002fj5nwIKHiILybVMTZ\\u002ffqh+XsHsSUSFaqyuMmPPoUJMC+jg760Oxzl+viBSjPhfmDK3FLmu5kdef2Q\\u002fWtXUPvrj\\u002f+zpfH2cmX4SP8oshilpAeBjba2QqLud8Ekytujjk5D6SVfddzUS9VhKmtAKCua+DZ5RUrSrI\\u002f\\u002fPd7StPiOX99min3sIJxY6291tZe659lwtwoaaVGVhlm6HeCJMujch0B94jXCgAlqqxiZcbp0Xqjz+wMjLkU7DGeeTDptoNDVVqRKkYchU3c9vpcf99QkDEVABCAFypmsqUMPLh7l+FrBNBy9VwS4NDwzhGfMU2Wo5fEHR\\u002fZ58HdKbLMImV5reV0oiwtSqg9MLR1se6OXYZNGOFPj8gVoAIA6Hd3GX0DZao5rMK3TdQdNPflF7BFAF0tg0KRIC4l2mpybnrv4NyVxdnTktrr+zR6pSpabhlxuJ1egYivvbwmY11hAb3ZJ5Nvm5ro8tVyKeV+NWp3sSZh3Rh2tr+fPmRye\\u002fY1WSdXZmaypuqDmaG+U8pXyvgqhSCaF8LovqWlmS0CUInFS7mGw1NiSVoatbMpi8NdXU+Vjg2O8tS6NsvogMt+W2IGa6n5rWq62aWps0tT6Yij7GQG09XlLDNWi6MZAEZ8I6O+CfM\\u002fKMKbpixhSq4XcdLI+1OnVrJOKe8cMseqFeuP1bYOjARwIlTIMwilsErjTpo9e0zuvQ5vdfhx3F3+Jpe\\u002fqdf6RoQwP0pWqZFWivgc3zQaEU+wpf9on3t4sa6s3dFHrS83eE3MeF1PpKw6MHR+lqagxtIq4YnUwglDvCNd3cxDmoqExMk9GNy404N7bo+9P1KgAoAYccJi7e0owrNj1mGv4eHkMZOwBTPpiXgAGPIONtnrokX6cvU8AGh11A96+gY9fZWx9zJKBQDwBgLnBwZYQgCQ8PkLgnzla4639l4aSsmOjUuJDmABp80jFPF3rT+t0Uce3V577w8Wb\\u002fv0eGZhQs2Jtsd\\u002fXimNEANAoU4fK5cPOhysomoHB21eL7WG1mh1bDx+AQCy4qJZk7AXjMbgqUYAoKLZsKVTYXZiAlvEoN\\u002fdLOVH2rHhZH5xfuQidvJlOi2WPpuNLQWYk5R0LUNpijxtNFsEAAAXjAaCJFEEcWF+dwCbE5s8JzZ5S2djuW7CHd2qpgMAzoijweRHFkp4Eg8+9rEHgG5Xl5\\u002fwB0fIypHnBo8HbxVFybE58VoBn7ftXNPTy2awtFVYbg0ifkyM\\u002fPEY+eMYbjZ79pnd+6zekyTJMczhxOlvcPobeix\\u002fiBAWamSroqQrRfxYdiaAh5KWEySBIAgCSI4imRLeFb8AJ4m74xcCwDJ9OQA8nrIKAPIUKVTO8fMBToTY7\\u002fuObLZbKYsooXZVzL0Hh7bnRhZnywuZhSqF45YpBBCCJAgSx0nci3uYQ2tHwMYZwaN6cJBeXsBkBpeLY8GMtN72oZbanqKZ6Rq9MjIqIjU37si22pUPzPT7AgNdIzhOTF+U67C6RwatSZl66qz5ySnB5hicJE\\u002f29q7MzAQAvVL+6KKynhFLdhz7Fa0Nscn7DK7h0pRIVqpUEoklxMJsP+Hus9fbsGH+5RU58VIOR5mTIX7QuWGP7ichTzvucsXEjWFdFkuaWv1ZSw29i23diGFirlvWdHA54qjF5rY5vewdcRnwEX6RsviM6TQtwUm809XR6mB3WoMt9LcQnCAEPB6KIGtmjD0Sk7g1XGG8KuCpdRH352j\\u002fWR5fnan5S5R0OQ\\u002fh8N8NhdN\\u002fsdvyu+qBufXGuwft\\u002f\\u002fTjRlYGFEGDX3vOUXRwTj+Ot4UIxjA\\u002fqC\\u002fDwuDtO2M+gpMBqjOXIE3dOri+y9XGypYiy9xr3LzHuFmIinIVxdXmk7sMmxps1QCQJc+fqVm027CJdUr9EPseKYpixtQNk+5WAyBg7Bu7CzyA7\\u002f7qdNGs9A3\\u002fOFB\\u002ftiMtNw5Fka3\\u002fOtZU0x2XMq56irmKAoA649g7Nmp3fbz\\u002f3IjN+dY2tsmj3WRmSSjydZMZ18IkW6Nhiy6TGlGiESW6cZvJ30\\u002f9sXMAAEDNIFtNUOROavsLE4VIFCqoWY\\u002fVCgD3ZRQ9kVtG\\u002fb1QPJuV51Y1HQDEaBUkV8TRYIJjkLY72tqdEyYxeAivRDW1UdsN5URz1+6aCVaUEyP7aqynqD+WWwP37xcMD43QyO7QyO4gSK\\u002fVc9zk3mvxHAoQVnY+bkiHr9bhq+22\\u002fFYuKtHIKqOkK6492nLb6ChnFz1eobiiK0OMOEGri0EAodxtZ2uWBEiM+vjfHjvuSZgsy3g0+XlaVz6S\\u002fCOcxHmMhQJJ0jT6f4rmkQnGAprcaI7HOqMgISlDLxSPdTqe\\u002fuVqKoB\\u002f8exMOrz\\u002fnU\\u002fMR9DLcf8BIERRANB0OcjJkNVZnpEwNy+le9iCEwQzmmWnhfutm3wEHSbJStXpvj62dAwkQ16hEGh14lR2CoOW0bFbYJE0dY8BTuQikYer\\u002f2twOgFALZbQkkwlW4PcuqYDHoqarC4eD7XY3Oy0iQSPCs9bzpr9E77rOYpcKW8yW9hNJi4q8nz7hHuviFpEj2ay5BP8UcJVWDQoIlZLl6qlS0nAbd7TZvdes3u\\u002fHx9m5+OGdPiqHb7qLvNvFOIyjbQySrpcwGMPW8Kky2phiwAAIGvSjxUNy+hLD1VYMNVT8GEwoWqVHMKBiNZWFFQAfzq8\\u002f4xFuWjQSoCkEEVdutzfzEvUfXPy4l+2HS\\u002fPTGTF3h2w25mHNPGK66ARruhEMrm2Ikiyw8yhFKKk0usV5lguEg1zLXE3e66gCOCWNp1UIlw5P+\\u002fg6dazF7oXVmSykxkEjwoHPeyRLOf6wVvLppMXD168RFmv3n32LkpbUbvmaEUTrElTVlg0CPCU4jlK8ZxU9f9z+GpN7j1m9z5voJedjxvC7j1n957rNP8qUjwjSroySrpCwBs3HoVD8GILilTV1Mq5vnDOWsKVnshQ5JalsEUAEUIhZ+ykUbeb6k\\u002f1DFuStCqb23ugrn1WNu1PAzhJcq6qAwB9xHUwwUbLpmAuCGbU7Q5wdZlNbnfqW39mS68rnF6+TG5t0xlH7PtONC+bk3P3bcXstCDKVOVMhcWCh\\u002fCmqUowDD9T3ZmaHB1O4N8bTUaM5rtXHmUJrZh5\\u002f9B3UUItALlQezttDmJ\\u002fva8KRC4qSVa9WhJ3pChmR0Lk81LBZB+BiRA27+lO839W9c9oHHpoyLkhQHD3UIIJpRq0slvW3SVI0sS1SIiPopME7b4KlFwBKgmSpLoPvgDeNjgqEvDTYqJ8DA9Si8fD6eQiEwr5oTdBCJ8pLUwJZjjED3oT8F2OVmT3+7x4AAD8ON5gGqIz3Nqm00crHrlzhj5awU7gIj+yQMLjeDwochV5Up6Mx0d5PPTUuQ52ctgcPnn1AYFZ4ARBt63bNzazd5W75kwVmTBXJsxNUL7oDfSY3LtN7r1O30VguCmEggTc5j1l857qNP1npGSWRrpKLV3GRyfrbAd3MSg4w3rcHLyBAOdjLZ\\u002f0cbwKFCHUn9PvB4DMWI3J7ipNj28fHBUJxn9iTndWAJALr0\\u002f1rnHgZgvxg94E6Feiw2b6orU2T63jo6gQ5Zm97nlxKfA\\u002fvumY8BF+kXLaGdMpdgIAXF4\\u002fiCJIdJR8wGAFAIvN\\u002ffWWKrvTc\\u002feqku5e0+CQdWjEsWhO1rSCRJfb\\u002f+U3Z7EAftvC3PSUcctp66Whjd+d7x+0FOXFd\\u002faMpqdE9\\u002fablZHSjFTdpm1VJAm331aYGBfuQKemY2DAbKemCP+w+fBr9y9DEFAJozl3zbkOHwdOxPykOMWzhfrvyuJPpqhfixTPROAK1h8KEnCr5\\u002fgl0y\\u002fO909vHn5i2PlNgOC2HdBfRRbiEDNBN4FQI4vg2FvXSKgCaY+KmdlJQj6PiuFHw7msBACEQWayq0NwbX2NUK13MyGBvC0xc9BlvzBiuDMtr99po+T\\u002fw5uORfBcIQUf4U8Lmh9URUrXrCwuzks4cebS8KgjOyPmR08u3Hu4CQB2H6yPjoooK076fNMEf9SsdF1KkuaBu8oLc+OHRuwNzYOjJmdTm2H73gtrVhQ\\u002f++i8rzafY+afHIfHFyEe09cogvgDAQDgIagDs\\u002fEQnjMw4fW\\u002f4e+2kKePkT8SI38kQFjM7gMm916b9wRBcsQhY0GSAYvniMVzBDH\\u002fUimeq5FVqiVLmftOh3qGrnqRxLXDaYIBgOu+PRc\\u002fRPRef4gKUARCrEedZFO8KRGqVgBg9F6qMm9DAAkQfgRBBahoRczzrDyc\\u002fms3mZLouC2dTXem5QUI4q3aEw9nj73et7DprgJqVMicK6TIVeRJeezxx74jTWaLKzFeTc25RzHiBrvcfl20QijgP3RPBessetpaIhIQJImgCJ+Hutx+qUTI46GBwGTPIYvyzIQXP97eb7I5PL4AQVBjAhEqma6eV2c90+K4UKScQWe+4QqLho+qtBFrtRFrcdJt8Rw2u\\u002fdbPUdC9Z6YkCRm8RyyeA6hiLAicdxfgxfCISWU1rgJhNKV171KWIj3J1TPi4LTwQ0AOIexVwH37wEAAHpx+qrYl06Pfl2irhSiklMjGyHIAzLUD3qTWRyfZvN7AeDlsgW08BY2HQAQBIlM8Gy5AqFGhfT6wd4B855DDQMGa1K8ms9D+w0WjxdDg5TvbQvzPtlwKlYXmZXBdv3LStO9+8nRBbMzY\\u002fXKEZMjPlbV1TO6amnBR1+cEIsFC2ZnsfJPQoRY9Jcnb6\\u002fvMUqEgtyEsTGBxT9abTlRqpozR7OMmfnmKSwaHiLVSCs10koScLv3nMVzyOI54sGubP9j9ctop2QWfvyWfahFIfRFqM7gVROqwMmHw6GqFxz+4eogrmSsdARGzb5BIU9i8veTQLI8gUP9oFkazboQwWGuF7nRY741nTbz+w1nCzV6AHgwaxqd4dY2XdMlg88fKM1PZCeE5qmUZ55KeYYtvUxinPq5pxbRh3NnZggmLoh59cUVAKDXKl5+fjlnTODVK4qpgMD52XGUZOHsLAD4yQ+XsQICZ8mzPyr7hD4MZtTuem3DfovT8+VL6\\u002f77q33\\u002fff9SFEFUQs0S3Rp21luisGgQ4EWKZ0aKZyarfukLDFi9J23ekzbvGQzn9h5kIWWYk5nYvLfMdisRCAQ8XvBD7PD52N2JayNUmEp6LtLp8dk9PgCIVY9PLYlDRGQOZVGeKp7QMW0o5kY\\u002fVG89iJHe2Zr7g\\u002fepD7ULQ6RIfBUhD68OFEHmxaWsTM5m\\u002fVi3tulidcp\\u002fbjrlcPlQFJk3PZ2dfM2wtBWLYG1FEWZA4CtS1zX4yMLSgxfaAUAs4PsxXCzkW\\u002fyjuwwbPbgLAJ5K\\u002fTmdmfudv\\u002fmI+HG6iHt1EfcCkGbPoQHbuw5fDTvTRDRSbveFSTa\\u002fudEgAFqZLNjDECdJ++WVydcFC5fCQhEkWiaD0LvmhNqt036d9Kkbu4Jp0oGNDnpaZmrus2HDUaIEVmoof5Rgr04Mx0ONvpl0jJrTNGpfIDDqdOsUEae7elOiVPHKyaaepQJBk3mYcmhghki+tU0nEQlWzM8jSXKquuB\\u002fBXmJ+r9uP+7w+LafbzK7PGIhH0LvmsOtO286pBtrNTg+aRl59nxfWcvw01fUVgCgC+Gw12OzskVc+HF80O5gSwF6LFbOTevCJJSD6EBQfIWrxur1ctYwWiajrPvUrjn3zCpk7fHFR1FOfx+CJEevh5Y3udlWXhYNtkOFymU+3GnwthFB8bBCNZ1p4qpgk8v95qGTdQMGABh2OIccTgzHR5yuBsPQqHPCXWyqbSBI8kh7l8Hu6DZZomTSt49NmO0KRiuJ+FnJvF+Uzv\\u002f3wglm5lvbdG6v\\u002f9CZtlGLa9+JZnba\\u002f35iVPLnV82Zk5OCE+RvHriNEiZJ0zWiGLUwGkFQ5q45t7KHheGjVu9xi+eozXsCw83s5CuREGJ9WZjb03swbMTlilWwX5L9ly7NS0nOvLy+Z3dr27JMdmikSchQR1VxhZfpsVpoQ8k10htCI1Mu\\u002fvSuOc19wxC0a06sXGH3cYy4++12qnd2LQy7nGzRRFCEb\\u002fEbhDyJ1W8M3oRCzOfrIyKCFzBYPR67z0frCwwn7F4vZfDaWt\\u002fs9PnLk+I7TZYEZeTOhtZ\\u002fm1OulIz1ZKcnxtX1Gy4MGH66eC6KIDhJKibt5Nr9PhGPN+JxAcD2ruYfFEzQWbew6UZMztK8hFklqT2DZhwn6JVbN5l7lv\\u002fphZ9Xzl2Uw064NgI4oVfK180rZgr73J0YieVHljGFcEsUlgfrNLn3WjwHHL4LAFOYPmPFqMkJ8f63jY46\\u002ff5gz3Kr1\\u002ftFbZ3D55sWG7s4LfXT6tpktaooRm\\u002f1eN85c9aNYRmaqEdLpgHAtw2NHgxbmZUVIRJ+dL6622KZHh9fFj9mXJycUHEFGodHVmRksqVXRcMQ98pNqkHm5aUqZRJ6EwrW6ClVreJcYHzJbJoWE8OWThEq5sEkzNTce9G63+4fnhP9IDsNAADytNpghUUC1BgGFySnUId6RYRaKs3RRwOAiM+PEIkOtHZIhYKHpxf7AoEuk2Va\\u002fNiNzE1Pfv\\u002fEORGfTwWQev\\u002fEuUfLx+3owXTZzZ4AdmywSyeRN5nZjXwLmy47Tbdl\\u002f8V3vjxWmp94q7TVjeNEc5fL668sm6AHo0S6vcZvPLgLAaQgcjotv3kKy+VvNLn3mt17p7qBhZCnj5Kt1EhXykUTnja1RML5QcZJ8kxf35K0NJYcw\\u002fEus+XNyhXUYWVO1v72SwBQMzhYEhsjEQqcvrFx1ryUlNK42NcOHHx9+W2ZmqhnyqeH725TEuLZDRVN6Sqgw8iwKNTp4PImFA63Vy2XYjjBsvWkq9XMQ5rG4eG1eWzhVAkVgIVmxNs1Xb0aAPrdzZECDs1eqNcf7OxkSwGqB8cVFgAECGJjTf19JQUCHk8bITvd1bs4s+C9E+dGna6fLplLZxPyeCaXuzI\\u002fGwC+rqnvGDVtrW9+uLw41MKDIk2MO4DlqXU2v3dObBIr9dY23ZqlN3ae9BYSHK0BAISoaLpqHksIN15hkQ5frcm92+ze5w2w6zQ5Qp4uSrpCI6uUi0pCmTUrEhK2NHOM6ne2tQUrLADQyznMXhUJCT\\u002fZtbsoRv\\u002fItDGFGC2Tivl8ysVmqmbOnOhorUwWHBKganDQjWHh7Jx6RY739LBFAAjA7MREuLwJBYYTOiXHzRaH0Kehgv+GjycQaOfah5GGBKLX3RAryQaAVseJGEk6LyhCxvzk5LdOsb2HAOB4d89PZs2mD19eOo+ail1XWggAi7PSAGBmSkLwd+W1lYupf+4tKbi3pGBiIgdGl4N2a0iLjGIm3cKma2w3CAW8jGTt1gMXVy7Im3xSr6Pd+OtXvvnNn9e9+dvtbc2DKnXE3z56Qq2JCASIf7136MDuiy6nr3Ba0o9+tiI2TkWd0tI48OmHR9qaDYEAnpah+\\u002fefLE\\u002fL0ANAAMP\\u002f\\u002fqfdR\\u002fY3iiWCex+aJRSN\\u002fV53LX3jbx8\\u002fEZ8Y9eHbB3Zvrf12708RFPnZjz5ffc90TbQ8uKiLtT2vvrh+486XZBFjn4o\\u002f\\u002fGoLAPziv9dQh6xoDQAgQsVJsgwqlckNUVgk4HbvGZN7z1Qiz4wh5GmjpMujZJUKUVkoPUWzIDmFU2HtvdRu9iwINbMDAD0W66aLDW2jo1nRmnydzux2jzhd1QMDc1OSWTnzdbrXjx5fkZlREjdhQDoJC1JSvm5oYAkxHN\\u002ffcSmcPZ0mp2pggHPVd55WSy+iVMokofRsSUwsiiDB7o5to6MGhyOU2Tsczvb3cW6NRdNqP9nuOGP1GwEgRpIRrK0AoECnj5bJgud5Lw4ZLw4ZC3Xj7ovBs4TB2uoqCOXWALeu6bAAfuh0q93lUymkOI5Prq0oRkccH\\u002fz9wDPPLY1PVLe3GtWaCAD47MMj505d+u1bD6jUsk1fnH71xfUfrX+WCmckV0gWLs1\\u002f6ZXbBULeh28f\\u002fPPvdvzjX08BwNdfnKo+2\\u002fnmu48oVbJ3\\u002f7rPNDo2cZSWqevtHo1PjOpoM+YVJfT3mRKSNL1dI+mZehwngosqnJak1UcePdi4cnUJAGAYfvp422uv30uVxhmtocfVzkcFcZLk06aD5er5dDCo66mwCNJn9Z4wu\\u002feY3QfDju03hoCniZIu10grFeLp4c9dLkhJEfP5wWvQvIHAR9XVP58zwd4cLZP9bN7YeCFJpXx5wViH87eHjrx\\u002f55pIifjl3XvnpiQ\\u002fNX3MzvfHFcsB4IHiQj+OT+5BzuKu3NxghQUAG+rrr11hbeDagQIAmCXX9xp9\\u002fkB5JttvAAAUIlFxTEzNIHt8SgJsb219pmzs3q+C\\u002fZc62KKJZCvmKoUxenE6O4EBArA6O5u5zSLNv2pq31oxNpy\\u002fcYRya4Bb13QCPu+h1eVYAB82OTKSOcbRwfj9gbvum5GTHwcAJdNTACCA4d9tPPvqr+9Oz9QDwNPPLTlyoPHIgcYlKwoBIC5BHZcwNuCtXFPy0x9+SpKAILBnR91d62akZ8UAwL89v\\u002fT4obHOQVqmvrd7dObcLIfDO3t+9qU2o1Il8\\u002fsD+lglVVpwUbetKj6w+yKlsKrOdMgVkqLSZCpb68CIgM9L1Y2PuHEyUGc948Zdcr4CJ3Fm6LrroLBw0m1xHzK791o8h3GS7TIzOQJeVJR0eZR0ZaR4Rvh6ikYhEi3PyODsZH1cU31PXl44uwPcmZ\\u002f7cVW1AEUfLeW2yE5JWwFAeVx8qkrVabGw5Gf7+6sGBsriwjLec9Jns21taWFLAfgouiZnXGHFR0W+t+eMw+NDUGRRAXtovCwtPfitA4AvLtQ9WVoa\\u002fnwoEzeG7QixwygTHsI\\u002fOfoVQRIooLOj17GTAQDgwcKij6urg\\u002fsbO9paH51WXKznHpddL6LEsp+WzAvVBLeq6QiS3LirpiAzdv\\u002fJYz9+fBE7mYvUDB3z0Giw+nyB1PQxfcfjoUkp0d2dY3MIVotr\\u002fScnaqu63C4fSZCBAEEQBAAMG22JyRoqj1YXKRSOqYv0TH3t+S7DgDlaq0jL0F2s7YmOVlCqkLMoHg9dVln06QeHDQOWmDjVsUNNy1YWUq319ckLVZcGBHy0JDXu7pljY3Yewl+sWx0gA8qgrT+nrCNoAoRl2PlN8\\u002fBT5\\u002ftK2kafH3XvDF9bCXhqXcS6PN0XZfFnUtW\\u002fjhTPvOqaPFrMrWUwHP\\u002fRjh2c8XBZ5Gq1P5k7+\\u002fnZM0NN8F0Fz04vZ4sAAOC1w4c4wzqHya+OHOY8\\u002fe7cPGZQHYlQcPv0HE2kTCNnr3QFgDuyszlfrX67fQPXBvHh8FldnSOM4DBV5m1KgT5GnEGGnh1OUiqXc02n4gTx4q5dVPycG8eF0cHTBg4TIcWtajrKrWHBjAy1UobjIZuOiUA44StLmQiY407m2PZXL28a7Df\\u002f\\u002fi8Pfrnlhf\\u002f47T20nARgLp\\u002fi8cde0vQsfV+vqb3VmJEVk5ap72gf6u0eTcvUQ+iiVGpZ+ayMA3vqAxh+9kT70pVFlPxit\\u002fH1R1b89sHlNZ0TnIEi+IpgbQVXoSb8+LDR8UXj0EPn+8ovmX5u8RwKJ\\u002fQCBR9V6SLuy9V9VhZ\\u002fNi3qt5HiWWHGnJmEIr1+cWoqWwoAAC2jI89s3RI8YLwJ3Jmbm8LVuWsaGfn98eNsaXh8Vld3iGsGjY+irE1G3T7\\u002f\\u002frr2EZtzVzVHd0wfEbEkxB5\\u002ffzp5wjB1B9c+m+3ts2fYUi7C2UgVAH46ezanQarXZntu547rtRqGk8QI5a6e1j09bft6Oeayb1XTZafphk3Od748lp2quzq3Bn2sUiIRdl4aog5xnOjrHk1KjQYAvz\\u002fQVN931\\u002f0Vmmg5APT3jrkx8nioVhfZ2z02G2AxuzzusTc9MTnaYnZ1dw5nZMdoouV2q3ug35yeqQ9VFMWKO6Yd2d9Yc74rLUtPDR4BwIcFNp648NXxup5hy1fH6746Xsc8JZhwb94b6B20f1hvvKeqf2an+b9s3lPh71fIR5XaiLW52k+mJ5xLi\\u002fq9Ujzn2vUUk1\\u002fMnRdq1Hayt\\u002ffejRt6uba6mxIDdjtnKNFQ8BDk14sWs6UAAPDPmup3z51jS6\\u002fE1pbmXx89wpYCAMDTZWUsH1pqE4olRRkahYyzR\\u002faD8nHHFiY2r\\u002ffftm+bUi\\u002fG6HQ+vPnbMJXIDPWdCkG0jK9KkhWG2kgVAFJUqlAmoaPd3fdu3GB0Tlk1sLB5vV9cuLBm\\u002fXqWtVvCF9ydlq+TRkRLuH1Bb0nTEQSp08gzk7UOp5edFh48HnrvQ7P++e6hS21Gs8n54dsHBEL+giV5ACAU8lXqiAvV3QEMb2s2bPjsJH3W8lXFmzecudRqMI06Pnr7AL2kGUURpUra1jSYnqUHAKVK1nlpKD1LP0lRADB9ZrrH49++ueq2yrHuFQA8tKBEp5TrlPInlpRT\\u002fzDO4CDkQ0PhxtrN7j0m916Xv4mddiX4aKRaulQjrYwUz0ZCP53XTrpa\\u002feLMWX88wd1zaRgeXv7Zp8+UlT02rUQ5qaNzMB1m88HOzt3tbReMxs\\u002fvvofyGwiTWYmJ9+Vz79L8xskTl8zm1xYuDOUQxMSP438+dfLDqqpgsw4AJCtVz8+oYAkn2YSColCnX5GRubu9jZ0A0DA0dP\\u002fXG9+7Y3W8QsFOC6LWYPjJnj29kzo9MpHylQAwyUaqNC9WzDza1UXvA8SkaWRk+WefPVFS+vi0aeE0IBODw3Gqr+9wV+eBjo6xiBeUTfgyXjzQZB7CSfLcUP\\u002fb8++g5TS3pOm6+k2dfaPlhUnshKmw7rE5Ph\\u002f26ovr3S5fflHC7\\u002f\\u002fyAL16+af\\u002fecc\\u002f3tyzaf3p5FTtS6\\u002fe\\u002fovnPqfk9z48y2iwvvSDTyUS4brH5g70m+nS0jL1tee71FERAJCWqdvxXXVCkmaSogAARZHbVhV9t\\u002fHcf\\u002fzmblpYlDw1oyTCjJdM4\\u002fTXm9y7ze69HqyLnXYl+KhCLV0aJa1UiufcUD3FhCDJp7duPdzFMWKikfD5i1JTl6SlTYuJTeRa1oOT5IDd3m21XDKZqgYHzw8MMHtVU1VYAOALBNZu3NAwzO3YoRKLHy8pvScvL9QuBlavd1tLywdV54P3eaaQ8Pmb1z3AuUsQZ7QGJkan47bPPgtlPZHw+U+XlT1UVKzhijeNk+QFg+GzC3XbW1qYT4+Qx8MJIniG\\u002foWKmS\\u002fMnMmUdDqrUyNKmZJg+u32u75aPxq6YysXiZanZ8xMSKhISOBsQxLA4HB0ms2dFkvr6Ojpvr7uoD2N2l94kVOn\\u002f6nm2IvFczjjtd\\u002f8puvqN327pzYhRgUA91Veod3+J\\u002fPxOwcdDu+Lv6hkJ4QNt8I61cNtFZoEHipXS5ZoZJVK8VyEy8XmRuPGsPu\\u002f3hhKO7AQ8\\u002fkxcrlCJBLyeL4A7sL8Lj9m8riDI8PQXIXCAgCDw7F244ZQGgcAEICMKE2+TpugiIwQChEEcWH+QbujeWSkcXgo+Amm4SHI3ytXLc\\u002fgcK4LFa2BxdaW5h\\u002fv3s2WMuAhSJFen6fVxcjlIj7P7vWZPR6j03FuYIAzvs0Hd6x+9\\u002fy5WgPbEf+FipmPlCZfcp6lJd2uurUJr43nCEH90NBD334TSjUwkQoESrFYJZFIBAIPhjn9fjeG2X2+K0YxZSksg8v+WUstAOSqtbenjE+8sriZTffCzJkmq+vTzWfVkVIAeOxudof6fz4YhgcwvLVp8P+9sonyOGXnCJtr7QHx0Ai1ZHGUrFIpnociQnbyTUQqEHxxz9ont3xXzTXxzMIbCHQFuR3cCGLk8vX3rL1\\u002f09fBS4goSIA202jbpI7OwaAI8qflyzm1FVyO1rC0KJNrRmuc1dk5zSMjH1RVsRMug5NkjcFQE\\u002fQWcfKD8vIlaWlHu7uC3zoAGPF1qwSxcsFYZ9DmD+u7UqDTrb9n7WPfbb6iAdGNYW4Mm+TDECYxMgXL\\u002fYqTm9l0ABCllL30xJXH0f9jqavu\\u002ftUvvlYopT\\u002f62Ypr0VZw1QqLh0hV0sUaaaVSsuDW6ikmCpHos7vv+a+DB75tmrLF7caRqFRuXrfuma1bw+z9XZEIofCvK1cuTAnZC5YKBa39I5zRGlj8fM5cp9+\\u002f\\u002fuJFdsIUebCw8Gez5wBAdohdqdMiylCET8+R89UrJ6aHJE+r\\u002fW7dA8\\u002ft3HHBaGSn3QBqRwZFPH6uWru+rW5teoEgdKj1m9Z0UwInMQQAZQxxSCCCYyVS+HAbgvCFKPf0wnVkekXajqOvAIAPt\\u002fkJF\\u002fOKk1SPk6kpLB4iVUkXRUkrVZIFKDI1e+fNQcLnv3Hb8nnJyb85ejR4hcetQh8h33jf\\u002fb87dnT9hQshx3jhMS0m5o3blk\\u002fuEKtRyJ5bNb7ybhJQBPnN4iW6iIi\\u002fnj4dvOgkHBCA52fOfL5izEQVKoQOtRBn2Nt5anTjTM19roAlShjPzhSCeIVi0333v332zPtVVVcc4k0JCZ\\u002fPtLhjBL6zu8Xm92rEMozAJ9FWcBObbkp0OA7wEGGafDEtOTfy7ozof2dkGafDcaDTcXhVwtvshCB8uN1PuOSCqRnIgwm+4iTV4yQshYUiEpVkoUZWqZIsRJGpTbQxOT\\u002faPV2TzJZOJJw8FD+t+uaBlPKSKA670u1Z2YtSUj+oqvr8Ql2oDXvDh4eiC5KTQ+01HyYSPv\\u002fXixavysz6\\u002fbFjF4euprOgi4j49\\u002fIZDxQWUsFSriPPzagoj4v\\u002fj4MHOHeKn4QkpfLXixbPSUqiJdnR0ZwL7iiYAfySZUXBIbFCwUfRF2fOujs3763Tp3a0tl77ph5Fev29+fl3ZE1wBBWgvB8UVPhxPEZ2hcl1mpvWdDRmX0ekMIGHCD0BM076IwR6Z2BoxNuiFxdI+GoACBCebucxlTA5UpjY7zqXFDHexfYTrkF3tQiN0EmKUISXq7y713WKTmWVQ+PDbY3WzSQQ0eKcRNlMAMTs6wQABzaYIKtAEd6Qp9EVGNJJCmX86Mmrx7ois3quwAgJxKi3lao5AFh8nRZ\\u002fNx8Vq0VpEfwxx\\u002f3JOmMoIo6SLs+M\\u002fnt5QlVW9NtR0hXXoq0A4G8th9iiIMLJEw4yofDHs2adevqZPyxbNi8pOXit7BWRCgSzExN\\u002fOW\\u002f+6aef+XD1mrgw5qqvyIz4+C0PPPDPNXcuSUvjnJwKBgEoi4v7\\u002fdKlR5948qGiouuurShmxMfvefiR3y9dmq0J6zufrFT9atGivY88ynzlAEDC50+i2akAfrbACGcAvyuSEBn55+Urjj351IszZ2VETdkUIhUIFiSn\\u002fHLe\\u002fH2PPvbdugfWFRQGb18aJZaGr60obk7T0Zh8bd3OowBwwfIlRnocmPH8yHsiVH5s6HU\\u002f4QKATsdhISo7Mf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                       {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"word_cloud\"}, \"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('700d50b5-7f4b-4195-b9de-5ba560e716d5');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "text = df[\"SUMMARY_EN\"].str.cat(sep=\" \")\n",
+        "\n",
+        "# Create and generate a word cloud image:\n",
+        "word_cloud = WordCloud(max_words=100, background_color=\"white\").generate(text)\n",
+        "\n",
+        "# Display the generated image:\n",
+        "fig = px.imshow(word_cloud, width=640)\n",
+        "fig.update_layout(xaxis_showticklabels=False, yaxis_showticklabels=False)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"word_cloud\"}})"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.embeddings.word_embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zPHwjjcAWNvj"
-   },
-   "source": [
-    "To see the outputs of the transformer encoder, let's apply the transformer to the first record of the dataset,\n",
-    "more precisely to its columns `input_ids` and  `attention_mask`, the outputs of the tokenizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "S7E9tRVZWNvk",
-    "outputId": "d943aa09-e288-4b2a-a233-6627faf829a7",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BaseModelOutput(last_hidden_state=tensor([[[ 0.1148, -0.0254,  0.1447,  ...,  0.1937,  0.0804, -0.2158],\n",
-      "         [ 0.1216, -0.5199,  0.6924,  ...,  0.2711, -0.2492, -0.0172],\n",
-      "         [-0.4065, -0.0786,  0.3362,  ..., -0.2183,  0.0278,  0.1635],\n",
-      "         ...,\n",
-      "         [-0.1276, -0.4791, -0.1539,  ...,  0.0442, -0.2272,  0.1089],\n",
-      "         [-0.1577, -0.4097, -0.2176,  ...,  0.0154, -0.2008, -0.1374],\n",
-      "         [-0.1855, -0.4261, -0.1884,  ..., -0.0515, -0.0600, -0.3426]]],\n",
-      "       device='cuda:0'), hidden_states=None, attentions=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "example = dataset_en[\"train\"][:1]\n",
-    "\n",
-    "input_ids = torch.tensor(example[\"input_ids\"]).to(device)\n",
-    "attention_mask = torch.tensor(example[\"attention_mask\"]).to(device)\n",
-    "with torch.no_grad():\n",
-    "    output = model(input_ids, attention_mask)\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "U9cBNUetWNvk",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "This produces a `BaseModelOutput` object which has a named property `last_hidden_state`,\n",
-    "a tensor that represents the hidden state of the final transformer block, i.e. the encoded text sequence!\n",
-    "\n",
-    "The dimension of the last hidden state is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "hkenZ_7pWNvk",
-    "outputId": "eaa565c5-abc7-4b3d-8ddc-dc32f465cff1",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dimensions of last hidden state:  torch.Size([1, 512, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"dimensions of last hidden state: \", output.last_hidden_state.size())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ImR3xLwLWNvl"
-   },
-   "source": [
-    "i.e., \\[number of samples (1),  sequence length $T$ (maximum 512 tokens), embedding size $E$ (768)\\].\n",
-    "\n",
-    "In what follows, we will use the information contained in this tensor to make predictions.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "4D2hUjC1WNvl",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "<a id='feature_extraction'></a>\n",
-    "<a name='feature_extraction'></a>\n",
-    "## 3.&nbsp;Using Transformers to Extract Features for Classification or Regression Tasks \n",
-    "\n",
-    "In this section you will learn how transformers can be used to extract features from text data for a classification\n",
-    "or regression problem.\n",
-    "\n",
-    "The idea is simple: The tokenized raw text data is encoded by the transformer model,\n",
-    "and the features are extracted from the last hidden state.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yowxi5sPWNvl"
-   },
-   "source": [
-    "<a id='extract_encoding'></a>\n",
-    "<a name='extract_encoding'></a>\n",
-    "### 3.1. Extracting the Encoded Text\n",
-    "\n",
-    "Before we have seen that the DistilBERT model encodes *each token* of each input sample into a tensor\n",
-    "of length $E=768$.\n",
-    "As such, the output of the transformer model depends on the length of the input sequences.\n",
-    "To make predictions, we would prefer having a single vector per input sample, independent of the sequence length.\n",
-    "\n",
-    "Different approaches are available to achieve this goal:\n",
-    "* Use the tensor corresponding to the `CLS` token, which is the first token of the input sequence in BERT models.\n",
-    "* *Mean pooling*: Taking the average of the tensors over all elements of the sequence.\n",
-    "    Here, the tensors corresponding to a `PAD` token should be excluded because they don't carry any information.\n",
-    "\n",
-    "We will implement both techniques and compare results.\n",
-    "\n",
-    "In the following cell we display a short function which applies the NLP model to a batch of encoded input samples,\n",
-    "extracts the last hidden state, and returns two tensors of length 768 for each input sample,\n",
-    "corresponding to the two methods explained before.\n",
-    "\n",
-    "The cell is not executable, because the function is already defined in the module `tutorial_utils` we imported initially."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "id": "b31jpyUBWNvl",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "source": [
-    "```\n",
-    "def extract_sequence_encoding(batch, model):\n",
-    "    input_ids = torch.tensor(batch[\"input_ids\"]).to(model.device)\n",
-    "    attention_mask = torch.tensor(batch[\"attention_mask\"]).to(model.device)\n",
-    "    with torch.no_grad():\n",
-    "        # apply transformer model and extract last hidden state\n",
-    "        model_output = model(input_ids, attention_mask)\n",
-    "        last_hidden_state = model_output.last_hidden_state\n",
-    "\n",
-    "        # extract the tensor corresponding to the CLS token, i.e. the first element in the encoded sequence\n",
-    "        batch[\"cls_hidden_state\"] = last_hidden_state[:,0,:].cpu().numpy()\n",
-    "\n",
-    "        # mean pooling: take average over input sequence, but mask sequence elements corresponding to the PAD token\n",
-    "        last_hidden_state = last_hidden_state.cpu().numpy()\n",
-    "        lhs_shape = last_hidden_state.shape\n",
-    "        boolean_mask = ~np.array(batch[\"attention_mask\"]).astype(bool)\n",
-    "        boolean_mask = np.repeat(boolean_mask, lhs_shape[-1], axis=-1)\n",
-    "        boolean_mask = boolean_mask.reshape(lhs_shape)\n",
-    "        masked_mean = np.ma.array(last_hidden_state, mask=boolean_mask).mean(axis=1)\n",
-    "        batch[\"mean_hidden_state\"] = masked_mean.data\n",
-    "    return batch\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ikSaZs83WNvm"
-   },
-   "source": [
-    "Let's apply this function to the first sample of the training data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "n8tYVkL4WNvn",
-    "outputId": "ce8f5a48-c248-4b77-bda0-d80ac2ed829b",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary', 'input_ids', 'attention_mask', 'cls_hidden_state', 'mean_hidden_state'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "example = dataset_en[\"train\"][:1]\n",
-    "result = extract_sequence_encoding(example, model)\n",
-    "print(result.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Fly05NyXWNvo"
-   },
-   "source": [
-    "As desired, two additional columns `cls_hidden_state` and `mean_hidden_state` were appended.\n",
-    "\n",
-    "Therefore, the function can be supplied to the familiar `map` function\n",
-    "to add corresponding columns to the original dataset.\n",
-    "The following lines do this for the full datasets.\n",
-    "\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is amore than 10 minutes.\n",
-    "We save the resulting datasets to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 209,
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-      "b13d3704f3c6432a9408375be0556c97",
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-      "95ddaeea7f3b4865878ec58175504159",
-      "3e1551afeac1478f9099ffd6e61ac492",
-      "b8280e22b8b744ef9029cf61b4708227",
-      "f4338786226b43fa9a74e856ad80e100",
-      "c37f24466c254b0bbb33fff09d4ddb30",
-      "c90825a6812347f7bc1d17a917e95c2a",
-      "7ef7f55f8af64a23be8ca73bee56cf04",
-      "3c54d43506f24c9198f2900c01869613"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8NkwCGKWNvb"
+      },
+      "source": [
+        "<a id='huggingface'></a>\n",
+        "<a name='huggingface'></a>\n",
+        "## 2.&nbsp;A Brief Introduction to the HuggingFace Ecosystem\n",
+        "\n",
+        "This tutorial uses NLP models provided by [*HuggingFace*](https://huggingface.co/).\n",
+        "\n",
+        "HuggingFace is a community that builds, trains and deploys state-of-the-art models for natural language processing,\n",
+        "audio, computer vision etc. HuggingFace's model hub provides thousands of pre-trained models for these applications.\n",
+        "The [Transformers](https://huggingface.co/docs/transformers/index) library offers functionality to\n",
+        "quickly download and use those pre-trained models on a given input, fine-tune them on the own datasets\n",
+        "and then share them with the community.\n",
+        "The library is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow.\n",
+        "\n",
+        "In this notebook, the following elements of the HuggingFace ecosystem will be used:\n",
+        "\n",
+        "* datasets – a library to load and process inputs and outputs of the NLP model\n",
+        "* tokenizers – translating the raw input text into tokens, which are the vocabulary items of a given NLP model\n",
+        "* models – loading and saving models\n",
+        "* trainer - training of models, making predictions\n",
+        "\n",
+        "In the next sections we will briefly explore the first three components in turn.\n",
+        "The trainer functionality will be used in [Section 4](#finetuning) of this notebook."
+      ]
     },
-    "id": "B6vH7oZ8WNvo",
-    "outputId": "a7c06bb5-b8b0-41de-eeb3-e4b49cd56c23",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06561ab395c642038aa26010867a695f",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iBkzTtL6WNvb"
       },
-      "text/plain": [
-       "  0%|          | 0/348 [00:00<?, ?ba/s]"
+      "source": [
+        "<a id='dataset'></a>\n",
+        "<a name='dataset'></a>\n",
+        "### 2.1. Loading the Data into a Dataset\n",
+        "\n",
+        "[*Datasets*](https://huggingface.co/docs/datasets/) is a library for easily accessing and sharing datasets,\n",
+        "and evaluation of metrics for NLP, computer vision, and audio tasks.\n",
+        "\n",
+        "A dataset can be loaded in a single line of code, in our case directly from the pandas DataFrame.\n",
+        "At the same time, we split the dataset into a training (80%) and a test dataset (20%).\n",
+        "We fix the random seed for the sake of reproducibility."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5af5a48ac97c4376b105eabb22067904",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "g5EUHu8NWNvc",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
+      "outputs": [],
+      "source": [
+        "dataset = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=0)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6b919e945d04d6589676f82ae4a0026",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xJQZP6vFWNvc"
       },
-      "text/plain": [
-       "  0%|          | 0/348 [00:00<?, ?ba/s]"
+      "source": [
+        "Since the texts are relatively long, some parts of this notebook require computing resources. Uncomment the following line to reduce the size of the dataset."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa760e4acd954272981152ef1700203f",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nw7H4keYWNvc",
+        "outputId": "e29671db-2f5d-4369-b932-f7c2a5ba7247"
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "DatasetDict({\n",
+            "    train: Dataset({\n",
+            "        features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
+            "        num_rows: 5559\n",
+            "    })\n",
+            "    test: Dataset({\n",
+            "        features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
+            "        num_rows: 1390\n",
+            "    })\n",
+            "})\n"
+          ]
+        }
+      ],
+      "source": [
+        "# dataset = DatasetDict({\"train\": dataset[\"train\"].select(range(1000)), \"test\": dataset[\"train\"].select(range(250))})\n",
+        "print(dataset)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "31f53ae765664719bd921e7d187b5c2f",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GEYz-kTWWNvc"
       },
-      "text/plain": [
-       "  0%|          | 0/348 [00:00<?, ?ba/s]"
+      "source": [
+        "The resulting `DatasetDict` behaves like a Python dictionary.\n",
+        "Therefore, you can access the `Dataset` corresponding to each split by"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee4e6306627644098b8c58a48e9c7599",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XJZ211STWNvd",
+        "outputId": "c09b4312-9cf2-4d56-ff20-32f93d516163",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset_en = dataset_en.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_ge = dataset_ge.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_mx = dataset_mx.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_en.save_to_disk(\"./datasets/dataset_en\")\n",
-    "dataset_ge.save_to_disk(\"./datasets/dataset_ge\")\n",
-    "dataset_mx.save_to_disk(\"./datasets/dataset_mx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9aNozXyxWNvo",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "<a id='classification'></a>\n",
-    "<a name='classification'></a>\n",
-    "### 3.2. ... and Using It in a Classification Model\n",
-    "\n",
-    "We will now use the encoded texts as features to predict labels taken from certain tabular information available in the dataset.\n",
-    "\n",
-    "To this end, we use the following convenience functions implemented in `tutorial_utils.py`:\n",
-    "\n",
-    "* `x_train, y_train, x_test, y_test = get_xy(dataset, features, label)`<br>\n",
-    "    get numpy arrays corresponding features (x) and label (y) corresponding to the train and test split of the `dataset`where the encoded sentences are stored in the column `features` and the labels in the column `label`.<br><br>\n",
-    "    \n",
-    "* `clf = logistic_regression_classifier(x, y, c=1)`<br>\n",
-    "    fit and return a multinomial [Logistic Regression classifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) to features `x`, and labels `y`. L2-penalty is controlled by the hyper-parameter `c`.<br><br>\n",
-    "    \n",
-    "* `clf = dummy_classifier(x, y):`<br>\n",
-    "    fit and return a [Dummy classifier](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html) to features  `x`, and labels `y`. This classifier predicts always the most frequent class and `predict_proba` always returns the empirical class distribution of `y`.<br><br>\n",
-    "    \n",
-    "* `score_accuracy, score_log, score_brier, confusion_matrix, fig = evaluate_classifier(y_true, y_pred, p_pred, target_names, display_title_string, file_name)`<br>\n",
-    "    Calculate and display performance metrics of a classifier. The return value `fig` is a ploty figure representing the confusion matrix plot. The following inputs are expected:<br>\n",
-    "    * the true labels `y_true` (array-like);\n",
-    "    * either the predicted labels `y_pred` (array_like), in which case the log loss and Brier score are not evaluated;\n",
-    "    * or the predicted probabilities `p_pred`  (array_like);\n",
-    "    * a display title string;\n",
-    "    * a file name for exporting the figure, or `None`.\n",
-    "\n",
-    "Now the toolbox is ready!\n",
-    "\n",
-    "Next, we apply it to a simple classification task."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XKHtvwGHWNvp"
-   },
-   "source": [
-    "<a id='case_study_nvehicles'></a>\n",
-    "<a name='case_study_nvehicles'></a>\n",
-    "### 3.3. Case Study: Use Accident Descriptions to Predict the Number of Vehicles Involved\n",
-    "\n",
-    "In this case study, we will predict the number of vehicles involved in an accident from the verbal accident description.\n",
-    "\n",
-    "Since the data set contains the column `NUMTOTV`, we can adopt a supervised learning approach.\n",
-    "\n",
-    "We might consider framing the problem as a regression task, e.g. using Poisson regression. However, looking at the frequenca distribution of `NUMTOTV`, it apears unlikely that the Poisson distribution is a good reflection of reality. First, there are no accidents with zero vehicles involved - it takes at least one. So we might consider using a zero-truncated Poisson model. However, the empirical frequency distribution has low mass at high vehicle counts, so that this would not be a plausible model either.\n",
-    "\n",
-    "Therefore, we frame the prediction task as multinomial classification. Given that only a small fraction of cases involves four or more vehicles,\n",
-    "and to avoid a heavily imbalanced classification problem, we map these cases to an aggregated class \"3+\".\n",
-    "\n",
-    "To achieve this, we map the column `NUMTOTV` to a new column `labels`, with levels 0 (1 vehicle), 1 (2 vehicles) and 2 (3 or more vehicles).\n",
-    "We choose the column name `labels` because this is expected by the sequence classification model which we fit in Section [4.2](#task_finetuning)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 244,
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-      "1d17eacf5f8343cb82e645d31134d145"
-     ]
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset({\n",
+            "    features: ['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary'],\n",
+            "    num_rows: 5559\n",
+            "})\n"
+          ]
+        }
+      ],
+      "source": [
+        "ds_train = dataset[\"train\"]\n",
+        "print(ds_train)"
+      ]
     },
-    "id": "LFE7syyMWNvp",
-    "outputId": "54d674fb-de07-4428-d7b1-a76ba8b1bc61",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2d31bc0bade498daee30b90900905b3",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RL4dLtZ5WNvd"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "The `Dataset` object behaves like a normal Python container.\n",
+        "You can query its length, get rows or columns, etc. For instance, its length is:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d5f3aaf6417b4e4894cfb430ca6557e6",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V5KYuXD7WNvd",
+        "outputId": "0c277665-991a-4096-9dd3-2a372e64be53",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "5559"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "len(ds_train)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "97f279ba0d73458aa89e07e11d344f1c",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rYPGO6cBWNvd"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "To query a single row, you can use its index, like in a list: `ds_train[0]`.\n",
+        "This returns a dictionary representing the row.\n",
+        "Its elements can be accessed by the column names as keys,\n",
+        "e.g. `ds_train[0][\"SCASEID\"]`.\n",
+        "Multiple rows can be accessed by index slices, e.g. `dataset[\"train\"][:2]`,\n",
+        "or by a list of indices, e.g. `dataset[\"train\"][0, 2]`.\n",
+        "\n",
+        "You can list the column names and get their detailed types (called features):"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ad6245f15074c818c94e84a396218c5",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m6RObDO1WNve",
+        "outputId": "70a336b2-6edb-405c-a732-7212aa6a8a09",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'level_0': Value(dtype='int64', id=None),\n",
+              " 'index': Value(dtype='int64', id=None),\n",
+              " 'SCASEID': Value(dtype='int64', id=None),\n",
+              " 'SUMMARY_EN': Value(dtype='string', id=None),\n",
+              " 'SUMMARY_GE': Value(dtype='string', id=None),\n",
+              " 'INJSEVA': Value(dtype='int64', id=None),\n",
+              " 'NUMTOTV': Value(dtype='int64', id=None),\n",
+              " 'WEATHER1': Value(dtype='int64', id=None),\n",
+              " 'WEATHER2': Value(dtype='int64', id=None),\n",
+              " 'WEATHER3': Value(dtype='int64', id=None),\n",
+              " 'WEATHER4': Value(dtype='int64', id=None),\n",
+              " 'WEATHER5': Value(dtype='int64', id=None),\n",
+              " 'WEATHER6': Value(dtype='int64', id=None),\n",
+              " 'WEATHER7': Value(dtype='int64', id=None),\n",
+              " 'WEATHER8': Value(dtype='int64', id=None),\n",
+              " 'INJSEVB': Value(dtype='int64', id=None),\n",
+              " 'words per case summary': Value(dtype='int64', id=None)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "ds_train.features"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4088953449d74aa9bc31396a9015d1ef",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yWaiHGOCWNve"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "Later in this tutorial we will get to know methods to process datasets,\n",
+        "such as filtering the rows based on conditions, and processing the data in each row.\n",
+        "\n",
+        "\n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c74fb15f55e44a185fb83650243efe3",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZTm6W4KUWNve"
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "source": [
+        "<a id='tokenize'></a>\n",
+        "<a name='tokenize'></a>\n",
+        "### 2.2 Tokenization: Split Raw Text into Vocabulary Items\n",
+        "\n",
+        "Next, we convert the summary texts into tokens,\n",
+        "i.e., the text strings are split into elements of the vocabulary of the NLP model.\n",
+        "\n",
+        "As such, the tokenizer and the NLP model need to be aligned.\n",
+        "Changing the tokenizer after training the model would produce unpredictable results.\n",
+        "\n",
+        "Let's start with the model\n",
+        "[`distilbert-base-multilingual-cased`](https://huggingface.co/distilbert-base-multilingual-cased).\n",
+        "As the name implies, this model is cased: it does make a difference between \"english\" and \"English\".\n",
+        "\n",
+        "The model is trained on the concatenation of Wikipedia in 104 different languages listed\n",
+        "[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).\n",
+        "The model has 6 layers, 768 dimensions and 12 heads, totalizing 134 million parameters.\n",
+        "This model is a distilled version of the\n",
+        "[BERT base multilingual model](https://huggingface.co/bert-base-multilingual-cased)\n",
+        "which has 177 million parameters.\n",
+        "On average, the distilled model is twice as fast as the original model.\n",
+        "\n",
+        "**If you want to use another model throughout this notebook, please feel free to simply change the following line!**"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2, 1, 2, 2, 2, 2, 2, 1, 2, 3, 2, 3, 2, 1, 3, 4, 1, 3, 1, 2, 1, 2, 2, 4, 2, 2, 2, 4, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2]\n",
-      "[1, 0, 1, 1, 1, 1, 1, 0, 1, 2, 1, 2, 1, 0, 2, 2, 0, 2, 0, 1, 0, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
-    "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
-    "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
-    "\n",
-    "# map number of vehicles to a new column \"labels\"\n",
-    "labels = [\"1\", \"2\", \"3+\"]\n",
-    "d = {i: min(i-1, 2) for i in range(1,10)}\n",
-    "dataset_en = dataset_en.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]}) \n",
-    "dataset_ge = dataset_ge.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
-    "dataset_mx = dataset_mx.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
-    "print(dataset_en[\"train\"][\"NUMTOTV\"][:40])\n",
-    "print(dataset_en[\"train\"][\"labels\"][:40])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Tpr-a2TqWNvp",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "As explained in Section [3.1](#extract_encoding), we will explore two different ways to use encoded texts:\n",
-    "1. Use the hidden state corresponding to the `CLS` token, which is the first token of the input sequence in BERT models.\n",
-    "2. *Mean pooling*: Taking the average of the tensors over all elements of the sequence.\n",
-    "\n",
-    "Let's start with the first approach by using the feature `cls_hidden_state` produced in Section [3.1](#extract_encoding).\n",
-    "\n",
-    "Using the toolbox developed before we fit a dummy classifier and a logistic regression classifier to the features and\n",
-    "labels of the English dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 768
-    },
-    "id": "fnUHw7xVWNvp",
-    "outputId": "b0a346e9-3c0e-4f59-be6d-8b2fe5be0a72",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dummy classifier\n",
-      "accuracy score = 57.2%,  log loss = 0.961,  Brier loss = 0.574\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.00      0.00      0.00       389\n",
-      "           2       0.57      1.00      0.73       795\n",
-      "          3+       0.00      0.00      0.00       206\n",
-      "\n",
-      "    accuracy                           0.57      1390\n",
-      "   macro avg       0.19      0.33      0.24      1390\n",
-      "weighted avg       0.33      0.57      0.42      1390\n",
-      "\n"
-     ]
     },
     {
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-       "config": {
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-         "format": "svg"
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-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "Dummy classifier"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "scaleanchor": "y",
-         "title": {
-          "text": "<b>predicted class</b>"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "<b>actual class</b>"
-         }
+      ],
+      "source": [
+        "model_name = \"distilbert-base-multilingual-cased\"\n",
+        "\n",
+        "# load tokenizer\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "print(f\"Tokenizer vocab_size: {tokenizer.vocab_size}\")\n",
+        "print(f\"Tokenizer model_max_length (maximum context size): {tokenizer.model_max_length}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O0vvS4YIWNvf"
+      },
+      "source": [
+        "As we can see, the tokenizer has a vocabulary of size 119'547.\n",
+        "The maximum sequence length of the model is 512 tokens.\n",
+        "\n",
+        "To see the tokenizer in action, we tokenize the first sentence of an accident description:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "x8B8xjQMWNvf",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       }
       },
-      "text/html": [
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+      "outputs": [],
+      "source": [
+        "text = \"V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.\"\n",
+        "result = tokenizer(text)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extract the transformer encoding corresponding to the the CLS token\n",
-    "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"cls_hidden_state\", \"labels\")\n",
-    "\n",
-    "# fit dummy classifier\n",
-    "clf_dummy = dummy_classifier(x_train_en, y_train_en)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_dummy.predict_proba(x_test_en), labels, \"Dummy classifier\", \"cm_nv_dummy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 768
     },
-    "id": "lIzPeTfEWNvq",
-    "outputId": "97c2cefb-ad14-4732-9113-8d37f523a079",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logistic regression (a)\n",
-      "accuracy score = 90.9%,  log loss = 0.275,  Brier loss = 0.146\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.94      0.93      0.93       389\n",
-      "           2       0.89      0.96      0.92       795\n",
-      "          3+       0.92      0.68      0.78       206\n",
-      "\n",
-      "    accuracy                           0.91      1390\n",
-      "   macro avg       0.92      0.85      0.88      1390\n",
-      "weighted avg       0.91      0.91      0.91      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kIlaAMtDWNvf"
+      },
+      "source": [
+        "Calling the tokenizer returns a `BatchEncoding` object,\n",
+        "which behaves just like a standard Python dictionary that holds input items used by the NP model.\n",
+        "`input_ids` is the list of token IDs for each token.\n",
+        "`attention_mask` is a list containing 1 for all elements that corresponds to tokens of the input text,\n",
+        "and 0 for padding tokens that are appended to attain a specified sequence length.\n",
+        "\n",
+        "To illustrate the meaning of the input IDs, we convert them back to token strings:"
+      ]
     },
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+      "execution_count": 22,
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+      ],
+      "source": [
+        "print(result)\n",
+        "print(tokenizer.convert_ids_to_tokens(result[\"input_ids\"]))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fit a classifier to the encoded English texts\n",
-    "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression (a)\", \"cm_nv_lr_a\")"
-   ]
-  },
-  {
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-   "source": [
-    "We obtain an accuracy score of 91%, compared to 57% with the dummy classifier.\n",
-    "This is already a very good result!\n",
-    "\n",
-    "Remember, we have just used the DistilBERT transformer off the shelf, with no tuning whatsoever,\n",
-    "to extract a vector of length 768 representing the information contained in the accident descriptions.\n",
-    "During this entire text encoding, the transformer model was unaware that its output was going to be used to predict the number of vehicles.\n",
-    "\n",
-    "How about the second approach, which uses the feature `mean_hidden_state` that was extracted\n",
-    "by mean pooling over the entire encoded sequence?\n",
-    "\n",
-    "Let's see:"
-   ]
-  },
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-   "execution_count": 33,
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-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logistic regression (b), train EN, test EN\n",
-      "accuracy score = 96.0%,  log loss = 0.127,  Brier loss = 0.063\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.96      0.97      0.97       389\n",
-      "           2       0.95      0.98      0.97       795\n",
-      "          3+       0.99      0.86      0.92       206\n",
-      "\n",
-      "    accuracy                           0.96      1390\n",
-      "   macro avg       0.97      0.94      0.95      1390\n",
-      "weighted avg       0.96      0.96      0.96      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6iRVhlrDWNvf",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "We observe that words like \"V1\", \"Pontiac\", \"minivan\", \"driveway\" etc. are split into multiple tokens each.\n",
+        "This is typical for WordPiece tokenization adopted by BERT, an approach designed to reduce vocabulary size.\n",
+        "This tokenizer marks sub-words by the prefix `##`.\n",
+        "\n",
+        "It is interesting to note that `2000` is a separate element of the vocabulary.\n",
+        "\n",
+        "The first and last tokens of the tokenized sequence are `CLS` and `SEP`, respectively.\n",
+        "* `CLS` stands for \"classification\".\n",
+        "The output of the BERT encoder corresponding to this input token is sometimes interpreted to represent the meaning of\n",
+        "the entire sequence (we will check this in [Section 3.2](#classification) of this notebook).\n",
+        "* `SEP` stands for \"separation\".\n",
+        "In next-sequence prediction tasks, it is used to separate the first from the second sequence.\n",
+        "\n",
+        "Here is a list of other special tokens used by the BERT tokenizer:\n",
+        "* The `UNK` token is used to represent tokens that are not available in the dictionary.\n",
+        "* The `PAD` token is used to pad the length of the tokenized sequence to a fixed length.\n",
+        "A fixed length is required when multiple sequences of different length are tokenized and fed into a BERT model\n",
+        "at the same time.\n",
+        "* The `MASK` token is used for pre-training the BERT model by masked language modeling.\n",
+        "For this task, the model is used to predict the masked token."
+      ]
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+      ],
+      "source": [
+        "print(f\"Tokenizer special_tokens_map: {tokenizer.special_tokens_map}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
-    "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression (b), train EN, test EN\", \"cm_nv_EN_EN\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "SB0K-NinWNvq"
-   },
-   "source": [
-    "Again, we have used DistilBERT without any fine-tuning.\n",
-    "\n",
-    "For the present task, by any of the considered scores, mean pooling performs much better than using the encoding of the `CLS` token.\n",
-    "For this reason, we use mean pooling in what follows.\n",
-    "\n",
-    "What would you guess - will the classifier model exhibit a similar performance when trained on the encoded German dataset?\n",
-    "\n",
-    "Let's check:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 768
     },
-    "id": "oLaoeTQgWNvr",
-    "outputId": "578b6a8b-d8c8-4a90-c760-2268d7f985e4",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train GE, test GE\n",
-      "accuracy score = 96.0%,  log loss = 0.120,  Brier loss = 0.062\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.97      0.98      0.97       389\n",
-      "           2       0.95      0.98      0.97       795\n",
-      "          3+       0.96      0.86      0.91       206\n",
-      "\n",
-      "    accuracy                           0.96      1390\n",
-      "   macro avg       0.96      0.94      0.95      1390\n",
-      "weighted avg       0.96      0.96      0.96      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PwVPgPCJWNvg"
+      },
+      "source": [
+        "It is instructive to look at the tokenization of the German translation of the same text:"
+      ]
     },
     {
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-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "cm_nv_GE_GE",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6r2vF-hyWNvg",
+        "outputId": "9f7cb543-aaaa-4073-eb94-d0cd054bcbcf",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       },
-       "data": [
+      },
+      "outputs": [
         {
-         "coloraxis": "coloraxis",
-         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>",
-         "name": "0",
-         "texttemplate": "%{z}",
-         "type": "heatmap",
-         "x": [
-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
-         ],
-         "xaxis": "x",
-         "y": [
-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
-         ],
-         "yaxis": "y",
-         "z": [
-          [
-           380,
-           9,
-           0
-          ],
-          [
-           10,
-           778,
-           7
-          ],
-          [
-           1,
-           28,
-           177
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'input_ids': [101, 159, 10759, 117, 10290, 32930, 12955, 10118, 73879, 23986, 46917, 24408, 10441, 10268, 11218, 10180, 117, 66298, 10166, 10599, 73655, 12210, 25131, 10496, 23608, 10329, 10359, 11615, 54609, 13091, 10525, 117, 42169, 21181, 10112, 10882, 37590, 72847, 43968, 10221, 126, 44271, 16757, 54609, 30064, 10106, 28253, 10165, 20139, 10130, 10745, 144, 16822, 38064, 11357, 119, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
+            "['[CLS]', 'V', '##1', ',', 'ein', 'Mini', '##van', 'der', 'Marke', 'Pont', '##iac', 'Montana', 'aus', 'dem', 'Jahr', '2000', ',', 'bog', 'von', 'einer', 'privaten', 'Ein', '##fahrt', 'nach', 'links', 'auf', 'eine', 'zwei', '##sp', '##uri', '##ge', ',', 'tro', '##cken', '##e', 'As', '##pha', '##lts', '##traße', 'mit', '5', 'Fa', '##hr', '##sp', '##uren', 'in', 'nördlich', '##er', 'Richtung', 'und', 'einem', 'G', '##ef', '##älle', 'ab', '.', '[SEP]']\n"
           ]
-         ]
         }
-       ],
-       "layout": {
-        "coloraxis": {
-         "colorscale": [
-          [
-           0,
-           "rgb(247,251,255)"
-          ],
-          [
-           0.125,
-           "rgb(222,235,247)"
-          ],
-          [
-           0.25,
-           "rgb(198,219,239)"
-          ],
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-           0.375,
-           "rgb(158,202,225)"
-          ],
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-          ],
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-          ],
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-          ],
-          [
-           0.875,
-           "rgb(8,81,156)"
-          ],
-          [
-           1,
-           "rgb(8,48,107)"
+      ],
+      "source": [
+        "text = \"V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.\"\n",
+        "result = tokenizer(text)\n",
+        "print(result)\n",
+        "print(tokenizer.convert_ids_to_tokens(result[\"input_ids\"]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wGEujP4IWNvg"
+      },
+      "source": [
+        "Tokenizers of multi-lingual models use the same vocabulary for all languages.\n",
+        "Obviously, the tokenizer simply splits the input string into pieces and does not perform any translation:\n",
+        "the English pronoun \"a\" (169) is a different token than the equivalent German \"ein\" (10290).\n",
+        "\n",
+        "We observe that the tokenizer is case-sensitive:\n",
+        "It differentiates between the tokens `mini` (25103) and `Mini` (32930).\n",
+        "\n",
+        "So far, we have tokenized single sentences only.\n",
+        "Next, we want to tokenize the entire dataset.\n",
+        "This is easily achieved by applying the `map` function to the dataset.\n",
+        "\n",
+        "All we need to provide to the `map` function is a function that takes a record or a batch of records from the dataset,\n",
+        "applies an operation to it, and returns a `DataSet` or a `dict` which defines the columns to be added or updated.\n",
+        "\n",
+        "In our case, we supply a function that calls the `tokenizer` as shown before.\n",
+        "As we have seen, calling the tokenizer returns a dict with the keys `input_ids` and `attention_mask`.\n",
+        "Therefore, the `map` function will add columns with these names to the original dataset.\n",
+        "\n",
+        "Since we plan to feed the tokenized sequences into a transformer model,\n",
+        "we need to truncate their length to the maximum length accepted by the transformer.\n",
+        "Moreover, the shorter sequences need to be padded at the end, so that all tokenized sequences have the same length.\n",
+        "\n",
+        "Overall, only a few lines of code are required to complete the tokenization:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
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+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
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+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+              "model_id": "2ff3aa3cbc084b018e532313e3a62868"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary', 'input_ids', 'attention_mask']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# define a function to tokenize a batch\n",
+        "def tokenize(batch, column):\n",
+        "    return tokenizer(batch[column], truncation=True, padding=True)\n",
+        "\n",
+        "# encode the full dataset\n",
+        "dataset_en = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_EN\"})\n",
+        "print(dataset_en[\"train\"].column_names)"
       ]
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-    }
-   ],
-   "source": [
-    "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
-    "clf_ge = logistic_regression_classifier(x_train_ge, y_train_ge, c=10)\n",
-    "_, _, _, _, _ = evaluate_classifier(y_test_ge, None, clf_ge.predict_proba(x_test_ge), labels, \"train GE, test GE\", \"cm_nv_GE_GE\")"
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-    "Yes indeed, the performance on the English and German datasets are comparable.\n",
-    "This is what we would have expected - after all we are using a multilingual transformer model.\n",
-    "\n",
-    "\n",
-    "<a id='cross_lingual_transfer'></a>\n",
-    "<a name='cross_lingual_transfer'></a>\n",
-    "### 3.4. Cross-Lingual Transfer\n",
-    "\n",
-    "In practice, it might happen that training data is available (predominantly) in one language,\n",
-    "but we would like to apply the model to test data in another language.\n",
-    "Translating the test data to the language of the training data would be an option,\n",
-    "but let's see how the multilingual transformer model performs.\n",
-    "\n",
-    "In our small experiment, we simply switch the languages of the test sets.\n",
-    "This might be hard for the models, since in the entire training process each model has seen only encoded input\n",
-    "from text samples in one language!\n",
-    "\n",
-    "First, use the German test set for the model trained on English input:"
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     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN, test GE\n",
-      "accuracy score = 66.0%,  log loss = 1.083,  Brier loss = 0.527\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       1.00      0.16      0.27       389\n",
-      "           2       0.67      0.86      0.75       795\n",
-      "          3+       0.57      0.85      0.68       206\n",
-      "\n",
-      "    accuracy                           0.66      1390\n",
-      "   macro avg       0.75      0.62      0.57      1390\n",
-      "weighted avg       0.75      0.66      0.61      1390\n",
-      "\n"
-     ]
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+      "source": [
+        "The additional argument `column` is passed to `tokenize` via the the dictionary `fn_kwargs`.\n",
+        "As we can see from the progress bars, the map function gets called twice - once for each split.\n",
+        "As expected, new columns `input_ids` and `attention_mask` have been added to the dataset.\n",
+        "\n",
+        "We repeat the same procedure for the German texts."
+      ]
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+      ],
+      "source": [
+        "dataset_ge = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_GE\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mhb6ViiHWNvh"
+      },
+      "source": [
+        "Later on, we will also use a dataset which has 80% English texts and 20% German texts:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
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+        "id": "J_Mve0bUWNvh",
+        "outputId": "da7de7c3-5beb-49e9-a9c3-b7d4f9676c44"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "08ceb14dc17a424eb9bbf3b32de86c1b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ef037aa5769a4dd2af9eecdc43143f5d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a13e4156d0a6432caf4da59392e12323"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "591089b40457413186dc8c7880d1dfec"
+            }
+          },
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "def map_mixed(x, idx):\n",
+        "    return {\"SUMMARY_MX\" : x[\"SUMMARY_GE\"] if idx % 5 == 0 else x[\"SUMMARY_EN\"]}\n",
+        "dataset = dataset.map(map_mixed, batched=False, with_indices=True)\n",
+        "dataset_mx = dataset.map(tokenize, batched=True, fn_kwargs={\"column\": \"SUMMARY_MX\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ItDUbmVvWNvi"
       },
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+      "source": [
+        "Now we have created three datasets - with the tokenized English, German and mixed language texts, respectively.\n",
+        "\n",
+        "We could have stored the results in a single dataset (with different column names),\n",
+        "but keeping languages separately will make it easier to convince ourselves in the following examples\n",
+        "that the languages have not been mixed up!"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = evaluate_classifier(y_test_ge, None, clf_en.predict_proba(x_test_ge), labels, \"train EN, test GE\", \"cm_nv_EN_GE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rQQ0Ql2bWNvr"
-   },
-   "source": [
-    "From these rather poor results, we conclude that this approach to cross-language transferability does not work.\n",
-    "\n",
-    "Vice versa, use the English test set for the model based on German input:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
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-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train GE, test EN\n",
-      "accuracy score = 24.3%,  log loss = 8.053,  Brier loss = 1.361\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.00      0.00      0.00       389\n",
-      "           2       0.40      0.17      0.24       795\n",
-      "          3+       0.19      0.99      0.32       206\n",
-      "\n",
-      "    accuracy                           0.24      1390\n",
-      "   macro avg       0.20      0.39      0.19      1390\n",
-      "weighted avg       0.26      0.24      0.18      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CMoSQ04TWNvi"
+      },
+      "source": [
+        "<a id='transformer'></a>\n",
+        "<a name='transformer'></a>\n",
+        "### 2.3. Transformer model\n",
+        "\n",
+        "After completing the tokenization of the raw texts, we are ready to apply the transformer model,\n",
+        "in our case the multilingual DistilBERT model.\n",
+        "\n",
+        "First, we load the model.\n",
+        "To speed up the following calculations, we opt for GPU support if available.\n"
+      ]
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+      ],
+      "source": [
+        "# load model\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model = AutoModel.from_pretrained(model_name).to(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6SjLBsnHWNvi"
+      },
+      "source": [
+        "The warning message can be ignored for our application.\n",
+        "\n",
+        "Let's examine the model structure:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "G9_RcTV-WNvj",
+        "outputId": "4a1d3d16-649b-4343-ebf9-313de42eddce",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DistilBertModel(\n",
+              "  (embeddings): Embeddings(\n",
+              "    (word_embeddings): Embedding(119547, 768, padding_idx=0)\n",
+              "    (position_embeddings): Embedding(512, 768)\n",
+              "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "    (dropout): Dropout(p=0.1, inplace=False)\n",
+              "  )\n",
+              "  (transformer): Transformer(\n",
+              "    (layer): ModuleList(\n",
+              "      (0-5): 6 x TransformerBlock(\n",
+              "        (attention): MultiHeadSelfAttention(\n",
+              "          (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "        )\n",
+              "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "        (ffn): FFN(\n",
+              "          (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
+              "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
+              "          (activation): GELUActivation()\n",
+              "        )\n",
+              "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              ")"
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-           "#636efa",
-           "#EF553B",
-           "#00cc96",
-           "#ab63fa",
-           "#FFA15A",
-           "#19d3f3",
-           "#FF6692",
-           "#B6E880",
-           "#FF97FF",
-           "#FECB52"
-          ],
-          "font": {
-           "color": "#2a3f5f"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "#E5ECF6",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
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-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
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-          "scene": {
-           "xaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "yaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
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-           "zaxis": {
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-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
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-          "shapedefaults": {
-           "line": {
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-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "train GE, test EN"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "scaleanchor": "y",
-         "title": {
-          "text": "<b>predicted class</b>"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "<b>actual class</b>"
-         }
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X3lhtdAsWNvj",
+        "pycharm": {
+          "name": "#%% md\n"
         }
-       }
       },
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+      "source": [
+        "As we can see, the first block of the model deals with embeddings, with the word embedding as the first layer.\n",
+        "This is followed by the transformer which consists of 6 transformer blocks.\n",
+        "\n",
+        "Let's first explore the word embedding.\n",
+        "\n",
+        "The goal of the word embedding layer is to assign each element of the vocabulary a vector of length $E$.\n",
+        "\n",
+        "The multilingual DistilBERT model has a vocabulary of size $V=119'547$ and a word embedding size of $E=768$.\n",
+        "We can confirm this by looking at the dimension of the word embedding weight tensor:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k9yHSGD0WNvj",
+        "outputId": "261e9e7a-f773-4546-c22b-f5453aaf10da"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Embedding(119547, 768, padding_idx=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "model.embeddings.word_embeddings"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = evaluate_classifier(y_test_en, None, clf_ge.predict_proba(x_test_en), labels, \"train GE, test EN\", \"cm_nv_GE_EN\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5FFjmHzBWNvs"
-   },
-   "source": [
-    "Again, performance is unsatisfactory.\n",
-    "\n",
-    "To improve results, we need to change the approach.\n",
-    "\n",
-    "\n",
-    "<a id='multi_lingual_training'></a>\n",
-    "<a name='multi_lingual_training'></a>\n",
-    "### 3.5. Multi-Lingual Training\n",
-    "\n",
-    "In a multilingual situation, a possible approach is to train the classifier with a training set consisting\n",
-    "of encoded samples from both languages.\n",
-    "This can always be achieved by translating a fraction of the text data and then use it to train the model.\n",
-    "\n",
-    "This is exactly what we are going to do next.\n",
-    "In order to simulate a situation where one language is underrepresented, we create a mixed-language dataset\n",
-    "with about 80% English and 20% German samples, our dataset `dataset_mx` produced in [Section 2.2](#tokenize).\n",
-    "\n",
-    "Since we are already using a multilingual transformer model, no further changes are required."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
     },
-    "id": "BdCe6OIDWNvs",
-    "outputId": "32fe4681-c6e7-401a-91e7-fc0862159c3d",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN/GE, test EN\n",
-      "accuracy score = 95.7%,  log loss = 0.136,  Brier loss = 0.068\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.96      0.98      0.97       389\n",
-      "           2       0.95      0.97      0.96       795\n",
-      "          3+       0.97      0.85      0.90       206\n",
-      "\n",
-      "    accuracy                           0.96      1390\n",
-      "   macro avg       0.96      0.93      0.95      1390\n",
-      "weighted avg       0.96      0.96      0.96      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zPHwjjcAWNvj"
+      },
+      "source": [
+        "To see the outputs of the transformer encoder, let's apply the transformer to the first record of the dataset,\n",
+        "more precisely to its columns `input_ids` and  `attention_mask`, the outputs of the tokenizer:"
+      ]
     },
     {
-     "data": {
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-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "cm_nv_MX_EN",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S7E9tRVZWNvk",
+        "outputId": "a44caa27-b257-4f48-a0e7-97719e3a3ed2",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       },
-       "data": [
+      },
+      "outputs": [
         {
-         "coloraxis": "coloraxis",
-         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>",
-         "name": "0",
-         "texttemplate": "%{z}",
-         "type": "heatmap",
-         "x": [
-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
-         ],
-         "xaxis": "x",
-         "y": [
-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
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-         "yaxis": "y",
-         "z": [
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-           380,
-           9,
-           0
-          ],
-          [
-           14,
-           775,
-           6
-          ],
-          [
-           1,
-           30,
-           175
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "BaseModelOutput(last_hidden_state=tensor([[[ 0.1148, -0.0254,  0.1447,  ...,  0.1937,  0.0804, -0.2158],\n",
+            "         [ 0.1216, -0.5199,  0.6924,  ...,  0.2711, -0.2492, -0.0172],\n",
+            "         [-0.4065, -0.0786,  0.3362,  ..., -0.2183,  0.0278,  0.1635],\n",
+            "         ...,\n",
+            "         [-0.1276, -0.4791, -0.1539,  ...,  0.0442, -0.2272,  0.1089],\n",
+            "         [-0.1577, -0.4097, -0.2176,  ...,  0.0154, -0.2008, -0.1374],\n",
+            "         [-0.1855, -0.4261, -0.1884,  ..., -0.0515, -0.0600, -0.3426]]],\n",
+            "       device='cuda:0'), hidden_states=None, attentions=None)\n"
           ]
-         ]
         }
-       ],
-       "layout": {
-        "coloraxis": {
-         "colorscale": [
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-           0,
-           "rgb(247,251,255)"
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-          ],
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-          ],
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-          ],
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-           0.625,
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-          ],
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-          ],
-          [
-           0.875,
-           "rgb(8,81,156)"
-          ],
-          [
-           1,
-           "rgb(8,48,107)"
+      ],
+      "source": [
+        "example = dataset_en[\"train\"][:1]\n",
+        "\n",
+        "input_ids = torch.tensor(example[\"input_ids\"]).to(device)\n",
+        "attention_mask = torch.tensor(example[\"attention_mask\"]).to(device)\n",
+        "with torch.no_grad():\n",
+        "    output = model(input_ids, attention_mask)\n",
+        "print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U9cBNUetWNvk",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "This produces a `BaseModelOutput` object which has a named property `last_hidden_state`,\n",
+        "a tensor that represents the hidden state of the final transformer block, i.e. the encoded text sequence!\n",
+        "\n",
+        "The dimension of the last hidden state is:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hkenZ_7pWNvk",
+        "outputId": "e5520d4d-178a-4ba8-a65b-710ba68e39e7",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dimensions of last hidden state:  torch.Size([1, 512, 768])\n"
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+      ],
+      "source": [
+        "print(\"dimensions of last hidden state: \", output.last_hidden_state.size())"
       ]
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-     "metadata": {},
-     "output_type": "display_data"
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-     "text": [
-      "train EN/GE, test GE\n",
-      "accuracy score = 95.2%,  log loss = 0.160,  Brier loss = 0.080\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.96      0.97      0.97       389\n",
-      "           2       0.95      0.97      0.96       795\n",
-      "          3+       0.94      0.85      0.90       206\n",
-      "\n",
-      "    accuracy                           0.95      1390\n",
-      "   macro avg       0.95      0.93      0.94      1390\n",
-      "weighted avg       0.95      0.95      0.95      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ImR3xLwLWNvl"
+      },
+      "source": [
+        "i.e., \\[number of samples (1),  sequence length $T$ (maximum 512 tokens), embedding size $E$ (768)\\].\n",
+        "\n",
+        "In what follows, we will use the information contained in this tensor to make predictions.\n"
+      ]
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+      },
+      "source": [
+        "<a id='feature_extraction'></a>\n",
+        "<a name='feature_extraction'></a>\n",
+        "## 3.&nbsp;Using Transformers to Extract Features for Classification or Regression Tasks\n",
+        "\n",
+        "In this section you will learn how transformers can be used to extract features from text data for a classification\n",
+        "or regression problem.\n",
+        "\n",
+        "The idea is simple: The tokenized raw text data is encoded by the transformer model,\n",
+        "and the features are extracted from the last hidden state.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yowxi5sPWNvl"
+      },
+      "source": [
+        "<a id='extract_encoding'></a>\n",
+        "<a name='extract_encoding'></a>\n",
+        "### 3.1. Extracting the Encoded Text\n",
+        "\n",
+        "Before we have seen that the DistilBERT model encodes *each token* of each input sample into a tensor\n",
+        "of length $E=768$.\n",
+        "As such, the output of the transformer model depends on the length of the input sequences.\n",
+        "To make predictions, we would prefer having a single vector per input sample, independent of the sequence length.\n",
+        "\n",
+        "Different approaches are available to achieve this goal:\n",
+        "* Use the tensor corresponding to the `CLS` token, which is the first token of the input sequence in BERT models.\n",
+        "* *Mean pooling*: Taking the average of the tensors over all elements of the sequence.\n",
+        "    Here, the tensors corresponding to a `PAD` token should be excluded because they don't carry any information.\n",
+        "\n",
+        "We will implement both techniques and compare results.\n",
+        "\n",
+        "In the following cell we display a short function which applies the NLP model to a batch of encoded input samples,\n",
+        "extracts the last hidden state, and returns two tensors of length 768 for each input sample,\n",
+        "corresponding to the two methods explained before.\n",
+        "\n",
+        "The cell is not executable, because the function is already defined in the module `tutorial_utils` we imported initially."
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "id": "b31jpyUBWNvl",
+        "pycharm": {
+          "name": "#%%\n"
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+      "source": [
+        "```\n",
+        "def extract_sequence_encoding(batch, model):\n",
+        "    input_ids = torch.tensor(batch[\"input_ids\"]).to(model.device)\n",
+        "    attention_mask = torch.tensor(batch[\"attention_mask\"]).to(model.device)\n",
+        "    with torch.no_grad():\n",
+        "        # apply transformer model and extract last hidden state\n",
+        "        model_output = model(input_ids, attention_mask)\n",
+        "        last_hidden_state = model_output.last_hidden_state\n",
+        "\n",
+        "        # extract the tensor corresponding to the CLS token, i.e. the first element in the encoded sequence\n",
+        "        batch[\"cls_hidden_state\"] = last_hidden_state[:,0,:].cpu().numpy()\n",
+        "\n",
+        "        # mean pooling: take average over input sequence, but mask sequence elements corresponding to the PAD token\n",
+        "        last_hidden_state = last_hidden_state.cpu().numpy()\n",
+        "        lhs_shape = last_hidden_state.shape\n",
+        "        boolean_mask = ~np.array(batch[\"attention_mask\"]).astype(bool)\n",
+        "        boolean_mask = np.repeat(boolean_mask, lhs_shape[-1], axis=-1)\n",
+        "        boolean_mask = boolean_mask.reshape(lhs_shape)\n",
+        "        masked_mean = np.ma.array(last_hidden_state, mask=boolean_mask).mean(axis=1)\n",
+        "        batch[\"mean_hidden_state\"] = masked_mean.data\n",
+        "    return batch\n",
+        "```"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")\n",
-    "clf_mx = logistic_regression_classifier(x_train_mx, y_train_mx, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_mx.predict_proba(x_test_en), labels, \"train EN/GE, test EN\", \"cm_nv_MX_EN\")\n",
-    "_ = evaluate_classifier(y_test_ge, None, clf_mx.predict_proba(x_test_ge), labels, \"train EN/GE, test GE\", \"cm_nv_MX_GE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0Ew5aqySWNvs"
-   },
-   "source": [
-    "This is a very good outcome. The scores are close to those achieved in the situation with a single-language!\n",
-    "\n",
-    "To conclude, a multi-lingual situation can be handled by a multi-lingual transformer model. For the best performance, the classifier should be trained on the encoded sequences from all languages."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C_CEp4ShWNvs",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "<a id='finetuning'></a>\n",
-    "<a name='finetuning'></a>\n",
-    "## 4.&nbsp;Fine-Tuning – Improving the Model\n",
-    "\n",
-    "In the previous case study, we have used the DistilBERT model without any adaptation to the text data at hand,\n",
-    "simply by using the sequence encoding produced by the model.\n",
-    "As such, the language representation, which the model has learned from a large corpus of multilingual data, is transferred\n",
-    "to the text data at hand.\n",
-    "This approach is called transfer learning.\n",
-    "The advantage of transfer learning is that a powerful (but relatively complex) model can be trained on a large corpus\n",
-    "of data, using large-scale computing power, and then be applied to situations where availability of data or computing\n",
-    "power would not allow for such complex models.\n",
-    "\n",
-    "For the task at hand, the results are already very good.\n",
-    "However, in certain situations it might be required to further improve model performance.\n",
-    "\n",
-    "In the following sections you will learn how to fine-tune a transformer model.\n",
-    "We will explore two approaches to fine-tuning:\n",
-    "\n",
-    "* *Domain-specific fine-tuning* involves updating the parameters of the transformer model using text data which is\n",
-    "    relevant to the domain where the model will be applied.\n",
-    "    However, the model is not necessarily tuned for a specific downstream task of interest.\n",
-    "* *Task-specific fine-tuning* uses domain-specific text data and tunes the parameters of the transformer model\n",
-    "    while training it for a given downstream task of interest.\n",
-    "\n",
-    "The advantage of the first approach is that it can be performed in an unsupervised fashion,\n",
-    "i.e., it does not require labeled data.\n",
-    "\n",
-    "On the other hand, task-specific fine-tuning is expected to produce better performance on the particular task\n",
-    "which the model was tuned for, so it might be the method of choice if there is a single down-stream task\n",
-    "and sufficient labeled data.\n",
-    "\n",
-    "Let's explore these two fine-tuning approaches in turn."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "I1pU2x-JWNvs"
-   },
-   "source": [
-    "<a id='domain_finetuning'></a>\n",
-    "<a name='domain_finetuning'></a>\n",
-    "### 4.1. Domain-specific fine-tuning\n",
-    "\n",
-    "Domain-specific fine-tuning can be achieved by applying the model to a \"masked language modeling\" task.\n",
-    "This involves taking a sentence, randomly masking a certain percentage of the words in the input,\n",
-    "and then running the entire masked sentence through the model which has to predict the masked words.\n",
-    "This self-supervised approach is an automatic process to generate inputs and labels from the texts and does not require\n",
-    "any humans labelling in any way.\n",
-    "\n",
-    "This is very easy to implement using the Transformers library.\n",
-    "You will see three new elements of the Transformer library in action:\n",
-    "\n",
-    "* the `AutoModelForMaskedLM` class loads the DistilBERT model with a model head suitable for the masked language\n",
-    "    modeling task.\n",
-    "* The `DataCollatorForLanguageModeling` class forms training batches from the dataset and handles the masking.\n",
-    "* The `Trainer` class provides the interface to train the model.\n",
-    "\n",
-    "Depending on the hardware available, training might take a rather long time.\n",
-    "Therefore, if available, we use GPU support.\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 55 minutes.\n",
-    "We store the trained model for later use.\n",
-    "\n",
-    "If you do not have enough time to perform this step right now, you can skip this section and return later. The remainder of this notebook does not depend on it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 530
     },
-    "id": "R5-TPov4WNvt",
-    "outputId": "8e6828ea-0fbb-475d-abbe-e821528896a8",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, SUMMARY_MX, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5. If WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, SUMMARY_MX, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5 are not expected by `DistilBertForMaskedLM.forward`,  you can safely ignore this message.\n",
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/optimization.py:309: FutureWarning:\n",
-      "\n",
-      "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
-      "\n",
-      "***** Running training *****\n",
-      "  Num examples = 5559\n",
-      "  Num Epochs = 2\n",
-      "  Instantaneous batch size per device = 4\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 2780\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ikSaZs83WNvm"
+      },
+      "source": [
+        "Let's apply this function to the first sample of the training data:"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='2780' max='2780' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [2780/2780 08:20, Epoch 2/2]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>500</td>\n",
-       "      <td>1.414500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1000</td>\n",
-       "      <td>1.119100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1500</td>\n",
-       "      <td>1.020200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2000</td>\n",
-       "      <td>0.938100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2500</td>\n",
-       "      <td>0.877800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table><p>"
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n8tYVkL4WNvn",
+        "outputId": "9d67c353-eeb2-4558-f6a3-99de7c2d7b99",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_keys(['level_0', 'index', 'SCASEID', 'SUMMARY_EN', 'SUMMARY_GE', 'INJSEVA', 'NUMTOTV', 'WEATHER1', 'WEATHER2', 'WEATHER3', 'WEATHER4', 'WEATHER5', 'WEATHER6', 'WEATHER7', 'WEATHER8', 'INJSEVB', 'words per case summary', 'input_ids', 'attention_mask', 'cls_hidden_state', 'mean_hidden_state'])\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "example = dataset_en[\"train\"][:1]\n",
+        "result = extract_sequence_encoding(example, model)\n",
+        "print(result.keys())"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to models/distilbert-base-multilingual-cased_mlm\n",
-      "Configuration saved in models/distilbert-base-multilingual-cased_mlm/config.json\n",
-      "Model weights saved in models/distilbert-base-multilingual-cased_mlm/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load model and tokenizer and define the DataCollator\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model \n",
-    "model_mlm = AutoModelForMaskedLM.from_pretrained(model_name).to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\n",
-    "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
-    "\n",
-    "# define training arguments\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=\"models/\" + model_name + \"_mlm_epochs\",\n",
-    "    overwrite_output_dir=True,\n",
-    "    num_train_epochs=2,\n",
-    "    per_device_train_batch_size=4,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "trainer = Trainer(\n",
-    "    model=model_mlm,\n",
-    "    args=training_args,\n",
-    "    data_collator=data_collator,\n",
-    "    train_dataset=dataset_mx[\"train\"]\n",
-    ")\n",
-    "trainer.train()\n",
-    "trainer.save_model(\"models/\" + model_name + \"_mlm\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "n8if8dfPWNvt"
-   },
-   "source": [
-    "Now, `model_mlm` holds the DistilBERT model, fine-tuned to the mixed-language accident descriptions\n",
-    "using masked-language-modeling.\n",
-    "\n",
-    "Next, we apply this model to all input sequences and extract the last hidden state.\n",
-    "The procedure is the same as in section [3.1](#extract_encoding).\n",
-    "To avoid confusion, we create new datasets, and store them on disk for later use,\n",
-    "so that this step does not need to be repeated all over when this notebook is re-run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
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-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fly05NyXWNvo"
+      },
+      "source": [
+        "As desired, two additional columns `cls_hidden_state` and `mean_hidden_state` were appended.\n",
+        "\n",
+        "Therefore, the function can be supplied to the familiar `map` function\n",
+        "to add corresponding columns to the original dataset.\n",
+        "The following lines do this for the full datasets.\n",
+        "\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is more than 10 minutes.\n",
+        "We save the resulting datasets to disk."
+      ]
     },
-    "id": "ULV0Ffx4WNvt",
-    "outputId": "cc20315a-8f1b-4560-b8eb-55ae802e1c07",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file models/distilbert-base-multilingual-cased_mlm/config.json\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"models/distilbert-base-multilingual-cased_mlm\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"torch_dtype\": \"float32\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading weights file models/distilbert-base-multilingual-cased_mlm/pytorch_model.bin\n",
-      "Some weights of the model checkpoint at models/distilbert-base-multilingual-cased_mlm were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\n",
-      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "All the weights of DistilBertModel were initialized from the model checkpoint at models/distilbert-base-multilingual-cased_mlm.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertModel for predictions without further training.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
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+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
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+              "version_major": 2,
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+        {
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+          "data": {
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+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
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+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
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+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5976809ff3b649b4a501c31a5679ac4e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "94e9b0b82ef24d50bcd22a69d330b8e3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fe3b8ce1b4804ba69782b7a9266496fb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c0c0c6952f1d48f7be957066bac19ed5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6ddeec81eeab4e60b7426bf313960252"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6e0ff75463754489a5174cac1d9b5822"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d17de88295f24936bb07e02bdde1ef18"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9aba4419261f433babdbacf2cddbaaae"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c69bc5a9ef494ff1be688d3345b47951"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "dataset_en = dataset_en.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_ge = dataset_ge.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_mx = dataset_mx.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_en.save_to_disk(\"./datasets/dataset_en\")\n",
+        "dataset_ge.save_to_disk(\"./datasets/dataset_ge\")\n",
+        "dataset_mx.save_to_disk(\"./datasets/dataset_mx\")"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "37b1ba9b0990460b80459cd828a3056d",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9aNozXyxWNvo",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
       },
-      "text/plain": [
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+      "source": [
+        "<a id='classification'></a>\n",
+        "<a name='classification'></a>\n",
+        "### 3.2. ... and Using It in a Classification Model\n",
+        "\n",
+        "We will now use the encoded texts as features to predict labels taken from certain tabular information available in the dataset.\n",
+        "\n",
+        "To this end, we use the following convenience functions implemented in `tutorial_utils.py`:\n",
+        "\n",
+        "* `x_train, y_train, x_test, y_test = get_xy(dataset, features, label)`<br>\n",
+        "    get numpy arrays corresponding features (x) and label (y) corresponding to the train and test split of the `dataset`where the encoded sentences are stored in the column `features` and the labels in the column `label`.<br><br>\n",
+        "    \n",
+        "* `clf = logistic_regression_classifier(x, y, c=1)`<br>\n",
+        "    fit and return a multinomial [Logistic Regression classifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) to features `x`, and labels `y`. L2-penalty is controlled by the hyper-parameter `c`.<br><br>\n",
+        "    \n",
+        "* `clf = dummy_classifier(x, y):`<br>\n",
+        "    fit and return a [Dummy classifier](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html) to features  `x`, and labels `y`. This classifier predicts always the most frequent class and `predict_proba` always returns the empirical class distribution of `y`.<br><br>\n",
+        "    \n",
+        "* `score_accuracy, score_log, score_brier, confusion_matrix, fig = evaluate_classifier(y_true, y_pred, p_pred, target_names, display_title_string, file_name)`<br>\n",
+        "    Calculate and display performance metrics of a classifier. The return value `fig` is a ploty figure representing the confusion matrix plot. The following inputs are expected:<br>\n",
+        "    * the true labels `y_true` (array-like);\n",
+        "    * either the predicted labels `y_pred` (array_like), in which case the log loss and Brier score are not evaluated;\n",
+        "    * or the predicted probabilities `p_pred`  (array_like);\n",
+        "    * a display title string;\n",
+        "    * a file name for exporting the figure, or `None`.\n",
+        "\n",
+        "Now the toolbox is ready!\n",
+        "\n",
+        "Next, we apply it to a simple classification task."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
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+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XKHtvwGHWNvp"
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
+      "source": [
+        "<a id='case_study_nvehicles'></a>\n",
+        "<a name='case_study_nvehicles'></a>\n",
+        "### 3.3. Case Study: Use Accident Descriptions to Predict the Number of Vehicles Involved\n",
+        "\n",
+        "In this case study, we will predict the number of vehicles involved in an accident from the verbal accident description.\n",
+        "\n",
+        "Since the data set contains the column `NUMTOTV`, we can adopt a supervised learning approach.\n",
+        "\n",
+        "We might consider framing the problem as a regression task, e.g. using Poisson regression. However, looking at the frequenca distribution of `NUMTOTV`, it apears unlikely that the Poisson distribution is a good reflection of reality. First, there are no accidents with zero vehicles involved - it takes at least one. So we might consider using a zero-truncated Poisson model. However, the empirical frequency distribution has low mass at high vehicle counts, so that this would not be a plausible model either.\n",
+        "\n",
+        "Therefore, we frame the prediction task as multinomial classification. Given that only a small fraction of cases involves four or more vehicles,\n",
+        "and to avoid a heavily imbalanced classification problem, we map these cases to an aggregated class \"3+\".\n",
+        "\n",
+        "To achieve this, we map the column `NUMTOTV` to a new column `labels`, with levels 0 (1 vehicle), 1 (2 vehicles) and 2 (3 or more vehicles).\n",
+        "We choose the column name `labels` because this is expected by the sequence classification model which we fit in Section [4.2](#task_finetuning)."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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+        "id": "LFE7syyMWNvp",
+        "outputId": "8662795c-6dbe-4db1-9d43-0a651db6a02a",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/348 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8859929b31c548eaa0fceb367568af33"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "37bb3b9cd750470580d42d0792ea8e3b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a38d4960d8b347d5a27e3b6e4e1fb113"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0c3537ff57e04d7db9262aab594bcd5f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ac73a156c46644d7970e16e7781a5183"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "56ab07e2da3340d7a5548ad251b507fa"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 1, 2, 2, 2, 2, 2, 1, 2, 3, 2, 3, 2, 1, 3, 4, 1, 3, 1, 2, 1, 2, 2, 4, 2, 2, 2, 4, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2]\n",
+            "[1, 0, 1, 1, 1, 1, 1, 0, 1, 2, 1, 2, 1, 0, 2, 2, 0, 2, 0, 1, 0, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
+        "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
+        "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
+        "\n",
+        "# map number of vehicles to a new column \"labels\"\n",
+        "labels = [\"1\", \"2\", \"3+\"]\n",
+        "d = {i: min(i-1, 2) for i in range(1,10)}\n",
+        "dataset_en = dataset_en.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "dataset_ge = dataset_ge.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "dataset_mx = dataset_mx.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "print(dataset_en[\"train\"][\"NUMTOTV\"][:40])\n",
+        "print(dataset_en[\"train\"][\"labels\"][:40])"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb11b429e4bb458d89a068c83b97958a",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tpr-a2TqWNvp",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
+      "source": [
+        "As explained in Section [3.1](#extract_encoding), we will explore two different ways to use encoded texts:\n",
+        "1. Use the hidden state corresponding to the `CLS` token, which is the first token of the input sequence in BERT models.\n",
+        "2. *Mean pooling*: Taking the average of the tensors over all elements of the sequence.\n",
+        "\n",
+        "Let's start with the first approach by using the feature `cls_hidden_state` produced in Section [3.1](#extract_encoding).\n",
+        "\n",
+        "Using the toolbox developed before we fit a dummy classifier and a logistic regression classifier to the features and\n",
+        "labels of the English dataset."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "83fb52dd8abc430e9c80434449de43c7",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "fnUHw7xVWNvp",
+        "outputId": "ba7c7d40-83ca-4034-bb31-f2451433844f",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/348 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dummy classifier\n",
+            "accuracy score = 57.2%,  log loss = 0.961,  Brier loss = 0.574\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.00      0.00      0.00       389\n",
+            "           2       0.57      1.00      0.73       795\n",
+            "          3+       0.00      0.00      0.00       206\n",
+            "\n",
+            "    accuracy                           0.57      1390\n",
+            "   macro avg       0.19      0.33      0.24      1390\n",
+            "weighted avg       0.33      0.57      0.42      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# extract the transformer encoding corresponding to the the CLS token\n",
+        "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"cls_hidden_state\", \"labels\")\n",
+        "\n",
+        "# fit dummy classifier\n",
+        "clf_dummy = dummy_classifier(x_train_en, y_train_en)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_dummy.predict_proba(x_test_en), labels, \"Dummy classifier\", \"cm_nv_dummy\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-       "model_id": "c951b413644740a2b6548a1cb1732a22",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "lIzPeTfEWNvq",
+        "outputId": "091df1a2-7414-45ee-89da-d4ed4f4d8028",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/87 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Logistic regression (a)\n",
+            "accuracy score = 90.9%,  log loss = 0.275,  Brier loss = 0.146\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.94      0.93      0.93       389\n",
+            "           2       0.89      0.96      0.92       795\n",
+            "          3+       0.92      0.68      0.78       206\n",
+            "\n",
+            "    accuracy                           0.91      1390\n",
+            "   macro avg       0.92      0.85      0.88      1390\n",
+            "weighted avg       0.91      0.91      0.91      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# fit a classifier to the encoded English texts\n",
+        "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression (a)\", \"cm_nv_lr_a\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
-    "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
-    "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = AutoModel.from_pretrained(\"models/\" + model_name + \"_mlm\").to(device)\n",
-    "dataset_en_pretrained = dataset_en.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_ge_pretrained = dataset_ge.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_mx_pretrained = dataset_mx.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
-    "dataset_en_pretrained.save_to_disk(\"./datasets/dataset_en_pretrained\")\n",
-    "dataset_ge_pretrained.save_to_disk(\"./datasets/dataset_ge_pretrained\")\n",
-    "dataset_mx_pretrained.save_to_disk(\"./datasets/dataset_mx_pretrained\")"
-   ]
-  },
-  {
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-   "metadata": {
-    "id": "BKct-oVhWNvt",
-    "pycharm": {
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-   },
-   "source": [
-    "Now let's see to what extent domain-specific fine-tuning is able to improve the performance of the classification model.\n",
-    "\n",
-    "To this end, we perform the same steps as in Sections [3.3](#case_study_nvehicles)-[3.5](#multi_lingual_training):"
-   ]
-  },
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-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "91f9913d451c4690991760e2928c6c26",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qED73tnMWNvq"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "We obtain an accuracy score of 91%, compared to 57% with the dummy classifier.\n",
+        "This is already a very good result!\n",
+        "\n",
+        "Remember, we have just used the DistilBERT transformer off the shelf, with no tuning whatsoever,\n",
+        "to extract a vector of length 768 representing the information contained in the accident descriptions.\n",
+        "During this entire text encoding, the transformer model was unaware that its output was going to be used to predict the number of vehicles.\n",
+        "\n",
+        "How about the second approach, which uses the feature `mean_hidden_state` that was extracted\n",
+        "by mean pooling over the entire encoded sequence?\n",
+        "\n",
+        "Let's see:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cb1040e232114af59e874c5410e57cd8",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "Vx1LdIg5WNvq",
+        "outputId": "fae14e3c-6446-44e5-e042-5d19ae87acbf",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Logistic regression (b), train EN, test EN\n",
+            "accuracy score = 96.0%,  log loss = 0.127,  Brier loss = 0.063\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.96      0.97      0.97       389\n",
+            "           2       0.95      0.98      0.97       795\n",
+            "          3+       0.99      0.86      0.92       206\n",
+            "\n",
+            "    accuracy                           0.96      1390\n",
+            "   macro avg       0.97      0.94      0.95      1390\n",
+            "weighted avg       0.96      0.96      0.96      1390\n",
+            "\n"
+          ]
+        },
+        {
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+              "                            \n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
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+              "\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
+        "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression (b), train EN, test EN\", \"cm_nv_EN_EN\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab3d67551e20490fa7002af5b4427d98",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SB0K-NinWNvq"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "Again, we have used DistilBERT without any fine-tuning.\n",
+        "\n",
+        "For the present task, by any of the considered scores, mean pooling performs much better than using the encoding of the `CLS` token.\n",
+        "For this reason, we use mean pooling in what follows.\n",
+        "\n",
+        "What would you guess - will the classifier model exhibit a similar performance when trained on the encoded German dataset?\n",
+        "\n",
+        "Let's check:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a2ba01e272b42ba816f5b8b861dec9f",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "oLaoeTQgWNvr",
+        "outputId": "97da31d2-6524-4eff-a4a5-e2b6064c2e3a",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train GE, test GE\n",
+            "accuracy score = 96.0%,  log loss = 0.120,  Brier loss = 0.062\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.97      0.98      0.97       389\n",
+            "           2       0.95      0.98      0.97       795\n",
+            "          3+       0.96      0.86      0.91       206\n",
+            "\n",
+            "    accuracy                           0.96      1390\n",
+            "   macro avg       0.96      0.94      0.95      1390\n",
+            "weighted avg       0.96      0.96      0.96      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
+        "clf_ge = logistic_regression_classifier(x_train_ge, y_train_ge, c=10)\n",
+        "_, _, _, _, _ = evaluate_classifier(y_test_ge, None, clf_ge.predict_proba(x_test_ge), labels, \"train GE, test GE\", \"cm_nv_GE_GE\")"
       ]
-     },
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+        "id": "fYKoqK3eWNvr"
       },
-      "text/plain": [
-       "  0%|          | 0/5559 [00:00<?, ?ex/s]"
+      "source": [
+        "Yes indeed, the performance on the English and German datasets are comparable.\n",
+        "This is what we would have expected - after all we are using a multilingual transformer model.\n",
+        "\n",
+        "\n",
+        "<a id='cross_lingual_transfer'></a>\n",
+        "<a name='cross_lingual_transfer'></a>\n",
+        "### 3.4. Cross-Lingual Transfer\n",
+        "\n",
+        "In practice, it might happen that training data is available (predominantly) in one language,\n",
+        "but we would like to apply the model to test data in another language.\n",
+        "Translating the test data to the language of the training data would be an option,\n",
+        "but let's see how the multilingual transformer model performs.\n",
+        "\n",
+        "In our small experiment, we simply switch the languages of the test sets.\n",
+        "This might be hard for the models, since in the entire training process each model has seen only encoded input\n",
+        "from text samples in one language!\n",
+        "\n",
+        "First, use the German test set for the model trained on English input:"
       ]
-     },
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-     "output_type": "display_data"
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     {
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+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "247Aj4I5WNvr",
+        "outputId": "f111a82a-bf4b-4480-b7f2-c8c959b33efe",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train EN, test GE\n",
+            "accuracy score = 66.0%,  log loss = 1.083,  Brier loss = 0.527\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       1.00      0.16      0.27       389\n",
+            "           2       0.67      0.86      0.75       795\n",
+            "          3+       0.57      0.85      0.68       206\n",
+            "\n",
+            "    accuracy                           0.66      1390\n",
+            "   macro avg       0.75      0.62      0.57      1390\n",
+            "weighted avg       0.75      0.66      0.61      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
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+              "\n",
+              "// Listen for the clearing of the current output cell\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "_ = evaluate_classifier(y_test_ge, None, clf_en.predict_proba(x_test_ge), labels, \"train EN, test GE\", \"cm_nv_EN_GE\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset_en_pretrained = load_from_disk(\"./datasets/dataset_en_pretrained\")\n",
-    "dataset_ge_pretrained = load_from_disk(\"./datasets/dataset_ge_pretrained\")\n",
-    "dataset_mx_pretrained = load_from_disk(\"./datasets/dataset_mx_pretrained\")\n",
-    "\n",
-    "# map number of vehicles to a new column \"labels\"\n",
-    "labels = [\"1\", \"2\", \"3+\"]\n",
-    "d = {i: min(i-1, 2) for i in range(1,10)}\n",
-    "dataset_en = dataset_en_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
-    "dataset_ge = dataset_ge_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
-    "dataset_mx = dataset_mx_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
-    "\n",
-    "# extract features and labels and creade multi-lingual dataset\n",
-    "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
-    "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
-    "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
     },
-    "id": "jmGm2stNWNvu",
-    "outputId": "08a4ed7e-db6a-4af2-db14-3de971671ebd",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN, test EN\n",
-      "accuracy score = 97.1%,  log loss = 0.091,  Brier loss = 0.044\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.96      0.99      0.97       389\n",
-      "           2       0.97      0.98      0.97       795\n",
-      "          3+       0.98      0.91      0.94       206\n",
-      "\n",
-      "    accuracy                           0.97      1390\n",
-      "   macro avg       0.97      0.96      0.96      1390\n",
-      "weighted avg       0.97      0.97      0.97      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rQQ0Ql2bWNvr"
+      },
+      "source": [
+        "From these rather poor results, we conclude that this approach to cross-language transferability does not work.\n",
+        "\n",
+        "Vice versa, use the English test set for the model based on German input:"
+      ]
     },
     {
-     "data": {
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-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "cm_nv_pr_EN_EN",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
+        "id": "j90XLzGZWNvr",
+        "outputId": "d55a168a-c348-439b-f53f-f44755f48ff6",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       },
-       "data": [
+      },
+      "outputs": [
         {
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-         "texttemplate": "%{z}",
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-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
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-         "xaxis": "x",
-         "y": [
-          " <b>1</b> ",
-          " <b>2</b> ",
-          " <b>3+</b> "
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-           1,
-           17,
-           188
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train GE, test EN\n",
+            "accuracy score = 24.3%,  log loss = 8.052,  Brier loss = 1.360\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.00      0.00      0.00       389\n",
+            "           2       0.40      0.17      0.24       795\n",
+            "          3+       0.19      0.99      0.32       206\n",
+            "\n",
+            "    accuracy                           0.24      1390\n",
+            "   macro avg       0.20      0.39      0.19      1390\n",
+            "weighted avg       0.26      0.24      0.18      1390\n",
+            "\n"
           ]
-         ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+      ],
+      "source": [
+        "_ = evaluate_classifier(y_test_en, None, clf_ge.predict_proba(x_test_en), labels, \"train GE, test EN\", \"cm_nv_GE_EN\")"
       ]
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-     "metadata": {},
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-      "train EN, test GE\n",
-      "accuracy score = 41.4%,  log loss = 1.801,  Brier loss = 0.890\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       1.00      0.08      0.16       389\n",
-      "           2       0.58      0.43      0.49       795\n",
-      "          3+       0.26      0.99      0.42       206\n",
-      "\n",
-      "    accuracy                           0.41      1390\n",
-      "   macro avg       0.61      0.50      0.35      1390\n",
-      "weighted avg       0.65      0.41      0.39      1390\n",
-      "\n"
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+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5FFjmHzBWNvs"
+      },
+      "source": [
+        "Again, performance is unsatisfactory.\n",
+        "\n",
+        "To improve results, we need to change the approach.\n",
+        "\n",
+        "\n",
+        "<a id='multi_lingual_training'></a>\n",
+        "<a name='multi_lingual_training'></a>\n",
+        "### 3.5. Multi-Lingual Training\n",
+        "\n",
+        "In a multilingual situation, a possible approach is to train the classifier with a training set consisting\n",
+        "of encoded samples from both languages.\n",
+        "This can always be achieved by translating a fraction of the text data and then use it to train the model.\n",
+        "\n",
+        "This is exactly what we are going to do next.\n",
+        "In order to simulate a situation where one language is underrepresented, we create a mixed-language dataset\n",
+        "with about 80% English and 20% German samples, our dataset `dataset_mx` produced in [Section 2.2](#tokenize).\n",
+        "\n",
+        "Since we are already using a multilingual transformer model, no further changes are required."
+      ]
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+            "train EN/GE, test GE\n",
+            "accuracy score = 95.2%,  log loss = 0.160,  Brier loss = 0.080\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.96      0.97      0.97       389\n",
+            "           2       0.95      0.97      0.96       795\n",
+            "          3+       0.94      0.85      0.90       206\n",
+            "\n",
+            "    accuracy                           0.95      1390\n",
+            "   macro avg       0.95      0.93      0.94      1390\n",
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+      ],
+      "source": [
+        "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")\n",
+        "clf_mx = logistic_regression_classifier(x_train_mx, y_train_mx, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_mx.predict_proba(x_test_en), labels, \"train EN/GE, test EN\", \"cm_nv_MX_EN\")\n",
+        "_ = evaluate_classifier(y_test_ge, None, clf_mx.predict_proba(x_test_ge), labels, \"train EN/GE, test GE\", \"cm_nv_MX_GE\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fit logistic regression classifiers to each of the three datasets and (cross-) evaluate them\n",
-    "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"train EN, test EN\", \"cm_nv_pr_EN_EN\")\n",
-    "_ = evaluate_classifier(y_test_ge, None, clf_en.predict_proba(x_test_ge), labels, \"train EN, test GE\", \"cm_nv_pr_EN_GE\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
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-     "name": "#%%\n"
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train GE, test GE\n",
-      "accuracy score = 96.9%,  log loss = 0.104,  Brier loss = 0.051\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.97      0.98      0.98       389\n",
-      "           2       0.97      0.98      0.97       795\n",
-      "          3+       0.96      0.91      0.94       206\n",
-      "\n",
-      "    accuracy                           0.97      1390\n",
-      "   macro avg       0.97      0.96      0.96      1390\n",
-      "weighted avg       0.97      0.97      0.97      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Ew5aqySWNvs"
+      },
+      "source": [
+        "This is a very good outcome. The scores are close to those achieved in the situation with a single-language!\n",
+        "\n",
+        "To conclude, a multi-lingual situation can be handled by a multi-lingual transformer model. For the best performance, the classifier should be trained on the encoded sequences from all languages."
+      ]
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+      "source": [
+        "<a id='finetuning'></a>\n",
+        "<a name='finetuning'></a>\n",
+        "## 4.&nbsp;Fine-Tuning – Improving the Model\n",
+        "\n",
+        "In the previous case study, we have used the DistilBERT model without any adaptation to the text data at hand,\n",
+        "simply by using the sequence encoding produced by the model.\n",
+        "As such, the language representation, which the model has learned from a large corpus of multilingual data, is transferred\n",
+        "to the text data at hand.\n",
+        "This approach is called transfer learning.\n",
+        "The advantage of transfer learning is that a powerful (but relatively complex) model can be trained on a large corpus\n",
+        "of data, using large-scale computing power, and then be applied to situations where availability of data or computing\n",
+        "power would not allow for such complex models.\n",
+        "\n",
+        "For the task at hand, the results are already very good.\n",
+        "However, in certain situations it might be required to further improve model performance.\n",
+        "\n",
+        "In the following sections you will learn how to fine-tune a transformer model.\n",
+        "We will explore two approaches to fine-tuning:\n",
+        "\n",
+        "* *Domain-specific fine-tuning* involves updating the parameters of the transformer model using text data which is\n",
+        "    relevant to the domain where the model will be applied.\n",
+        "    However, the model is not necessarily tuned for a specific downstream task of interest.\n",
+        "* *Task-specific fine-tuning* uses domain-specific text data and tunes the parameters of the transformer model\n",
+        "    while training it for a given downstream task of interest.\n",
+        "\n",
+        "The advantage of the first approach is that it can be performed in an unsupervised fashion,\n",
+        "i.e., it does not require labeled data.\n",
+        "\n",
+        "On the other hand, task-specific fine-tuning is expected to produce better performance on the particular task\n",
+        "which the model was tuned for, so it might be the method of choice if there is a single down-stream task\n",
+        "and sufficient labeled data.\n",
+        "\n",
+        "Let's explore these two fine-tuning approaches in turn."
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-      "train GE, test EN\n",
-      "accuracy score = 64.3%,  log loss = 3.640,  Brier loss = 0.687\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.00      0.00      0.00       389\n",
-      "           2       0.62      1.00      0.76       795\n",
-      "          3+       0.99      0.49      0.65       206\n",
-      "\n",
-      "    accuracy                           0.64      1390\n",
-      "   macro avg       0.54      0.49      0.47      1390\n",
-      "weighted avg       0.50      0.64      0.53      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I1pU2x-JWNvs"
+      },
+      "source": [
+        "<a id='domain_finetuning'></a>\n",
+        "<a name='domain_finetuning'></a>\n",
+        "### 4.1. Domain-specific fine-tuning\n",
+        "\n",
+        "Domain-specific fine-tuning can be achieved by applying the model to a \"masked language modeling\" task.\n",
+        "This involves taking a sentence, randomly masking a certain percentage of the words in the input,\n",
+        "and then running the entire masked sentence through the model which has to predict the masked words.\n",
+        "This self-supervised approach is an automatic process to generate inputs and labels from the texts and does not require\n",
+        "any humans labelling in any way.\n",
+        "\n",
+        "This is very easy to implement using the Transformers library.\n",
+        "You will see three new elements of the Transformer library in action:\n",
+        "\n",
+        "* the `AutoModelForMaskedLM` class loads the DistilBERT model with a model head suitable for the masked language\n",
+        "    modeling task.\n",
+        "* The `DataCollatorForLanguageModeling` class forms training batches from the dataset and handles the masking.\n",
+        "* The `Trainer` class provides the interface to train the model.\n",
+        "\n",
+        "Depending on the hardware available, training might take a rather long time.\n",
+        "Therefore, if available, we use GPU support.\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 55 minutes.\n",
+        "We store the trained model for later use.\n",
+        "\n",
+        "If you do not have enough time to perform this step right now, you can skip this section and return later. The remainder of this notebook does not depend on it."
+      ]
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+      ],
+      "source": [
+        "# load model and tokenizer and define the DataCollator\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model_mlm = AutoModelForMaskedLM.from_pretrained(model_name).to(device)\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\n",
+        "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
+        "\n",
+        "# define training arguments\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"models/\" + model_name + \"_mlm_epochs\",\n",
+        "    overwrite_output_dir=True,\n",
+        "    num_train_epochs=2,\n",
+        "    per_device_train_batch_size=4,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "trainer = Trainer(\n",
+        "    model=model_mlm,\n",
+        "    args=training_args,\n",
+        "    data_collator=data_collator,\n",
+        "    train_dataset=dataset_mx[\"train\"]\n",
+        ")\n",
+        "trainer.train()\n",
+        "trainer.save_model(\"models/\" + model_name + \"_mlm\")"
       ]
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-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clf_ge = logistic_regression_classifier(x_train_ge, y_train_ge, c=10)\n",
-    "_ = evaluate_classifier(y_test_ge, None, clf_ge.predict_proba(x_test_ge), labels, \"train GE, test GE\", \"cm_nv_pr_GE_GE\")\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_ge.predict_proba(x_test_en), labels, \"train GE, test EN\", \"cm_nv_pr_GE_EN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
     },
-    "id": "ysYstLT2WNvu",
-    "outputId": "97bde4ef-7a27-4005-ce69-78f4899ee0b2",
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-     "name": "#%%\n"
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-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN/GE, test EN\n",
-      "accuracy score = 97.1%,  log loss = 0.095,  Brier loss = 0.046\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.97      0.99      0.98       389\n",
-      "           2       0.97      0.98      0.98       795\n",
-      "          3+       0.98      0.90      0.94       206\n",
-      "\n",
-      "    accuracy                           0.97      1390\n",
-      "   macro avg       0.97      0.96      0.96      1390\n",
-      "weighted avg       0.97      0.97      0.97      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8if8dfPWNvt"
+      },
+      "source": [
+        "Now, `model_mlm` holds the DistilBERT model, fine-tuned to the mixed-language accident descriptions\n",
+        "using masked-language-modeling.\n",
+        "\n",
+        "Next, we apply this model to all input sequences and extract the last hidden state.\n",
+        "The procedure is the same as in section [3.1](#extract_encoding).\n",
+        "To avoid confusion, we create new datasets, and store them on disk for later use,\n",
+        "so that this step does not need to be repeated all over when this notebook is re-run."
+      ]
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "84f23c987e614f0992dbdae071fb6314"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "25171061d90a40068fdd9cc385050aeb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d3b0ab351fe24f6c8c94db179bddfe82"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e27d3f29de024968b35a25d755a9abf6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "92d69515e6c246ffba8dbc9389875efb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7b7cfee0c19a418ca5a8892796d1764d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c17f3526e8cf4d4f909fb45e980c405e"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
+        "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
+        "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "model = AutoModel.from_pretrained(\"models/\" + model_name + \"_mlm\").to(device)\n",
+        "dataset_en_pretrained = dataset_en.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_ge_pretrained = dataset_ge.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_mx_pretrained = dataset_mx.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)\n",
+        "dataset_en_pretrained.save_to_disk(\"./datasets/dataset_en_pretrained\")\n",
+        "dataset_ge_pretrained.save_to_disk(\"./datasets/dataset_ge_pretrained\")\n",
+        "dataset_mx_pretrained.save_to_disk(\"./datasets/dataset_mx_pretrained\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BKct-oVhWNvt",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "Now let's see to what extent domain-specific fine-tuning is able to improve the performance of the classification model.\n",
+        "\n",
+        "To this end, we perform the same steps as in Sections [3.3](#case_study_nvehicles)-[3.5](#multi_lingual_training):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
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+          "height": 209,
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+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5034dad0e3fe47ceb3a20148f5792be6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0335aa51e5d34138bfc2b0a579a814d7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e773ba097a3f434eb44f2584e154f6f6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "53ecd38b4d364424966bf6fb209a5dec"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/5559 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bf9651c1a1e8405892e43f72b6aba784"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6ae930de9d8e4f428c840cf6f020dca0"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "dataset_en_pretrained = load_from_disk(\"./datasets/dataset_en_pretrained\")\n",
+        "dataset_ge_pretrained = load_from_disk(\"./datasets/dataset_ge_pretrained\")\n",
+        "dataset_mx_pretrained = load_from_disk(\"./datasets/dataset_mx_pretrained\")\n",
+        "\n",
+        "# map number of vehicles to a new column \"labels\"\n",
+        "labels = [\"1\", \"2\", \"3+\"]\n",
+        "d = {i: min(i-1, 2) for i in range(1,10)}\n",
+        "dataset_en = dataset_en_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "dataset_ge = dataset_ge_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "dataset_mx = dataset_mx_pretrained.map(lambda x: {\"labels\": d[x[\"NUMTOTV\"]]})\n",
+        "\n",
+        "# extract features and labels and create multi-lingual dataset\n",
+        "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
+        "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
+        "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "jmGm2stNWNvu",
+        "outputId": "a2ca1a88-fd87-4ecb-ef7d-719dbbfe3a30",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train EN, test EN\n",
+            "accuracy score = 97.1%,  log loss = 0.094,  Brier loss = 0.047\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.97      0.99      0.98       389\n",
+            "           2       0.97      0.98      0.98       795\n",
+            "          3+       0.98      0.88      0.93       206\n",
+            "\n",
+            "    accuracy                           0.97      1390\n",
+            "   macro avg       0.97      0.95      0.96      1390\n",
+            "weighted avg       0.97      0.97      0.97      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+            "train EN, test GE\n",
+            "accuracy score = 88.5%,  log loss = 0.308,  Brier loss = 0.174\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.96      0.78      0.86       389\n",
+            "           2       0.85      0.98      0.91       795\n",
+            "          3+       0.96      0.73      0.83       206\n",
+            "\n",
+            "    accuracy                           0.88      1390\n",
+            "   macro avg       0.92      0.83      0.87      1390\n",
+            "weighted avg       0.89      0.88      0.88      1390\n",
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+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+            ]
           },
-          "shapedefaults": {
-           "line": {
-            "color": "#2a3f5f"
-           }
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# fit logistic regression classifiers to each of the three datasets and (cross-) evaluate them\n",
+        "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"train EN, test EN\", \"cm_nv_pr_EN_EN\")\n",
+        "_ = evaluate_classifier(y_test_ge, None, clf_en.predict_proba(x_test_ge), labels, \"train EN, test GE\", \"cm_nv_pr_EN_GE\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "L5NPXt8VWNvu",
+        "outputId": "502a8f95-0800-4f8b-ed7c-caf6023b8af7",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train GE, test GE\n",
+            "accuracy score = 96.8%,  log loss = 0.115,  Brier loss = 0.055\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.98      0.98      0.98       389\n",
+            "           2       0.96      0.98      0.97       795\n",
+            "          3+       0.97      0.89      0.93       206\n",
+            "\n",
+            "    accuracy                           0.97      1390\n",
+            "   macro avg       0.97      0.95      0.96      1390\n",
+            "weighted avg       0.97      0.97      0.97      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+            "train GE, test EN\n",
+            "accuracy score = 65.1%,  log loss = 3.024,  Brier loss = 0.665\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       1.00      0.00      0.01       389\n",
+            "           2       0.62      1.00      0.77       795\n",
+            "          3+       0.99      0.53      0.69       206\n",
+            "\n",
+            "    accuracy                           0.65      1390\n",
+            "   macro avg       0.87      0.51      0.49      1390\n",
+            "weighted avg       0.78      0.65      0.54      1390\n",
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+              "                            \n",
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+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
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+              "</html>"
+            ]
           },
-          "title": {
-           "x": 0.05
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "clf_ge = logistic_regression_classifier(x_train_ge, y_train_ge, c=10)\n",
+        "_ = evaluate_classifier(y_test_ge, None, clf_ge.predict_proba(x_test_ge), labels, \"train GE, test GE\", \"cm_nv_pr_GE_GE\")\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_ge.predict_proba(x_test_en), labels, \"train GE, test EN\", \"cm_nv_pr_GE_EN\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ysYstLT2WNvu",
+        "outputId": "a86187be-a4b6-4be9-f81e-39902fcc6886",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train EN/GE, test EN\n",
+            "accuracy score = 96.9%,  log loss = 0.098,  Brier loss = 0.049\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.97      0.99      0.98       389\n",
+            "           2       0.97      0.98      0.97       795\n",
+            "          3+       0.98      0.87      0.92       206\n",
+            "\n",
+            "    accuracy                           0.97      1390\n",
+            "   macro avg       0.97      0.95      0.96      1390\n",
+            "weighted avg       0.97      0.97      0.97      1390\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+            "train EN/GE, test GE\n",
+            "accuracy score = 95.8%,  log loss = 0.146,  Brier loss = 0.070\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.97      0.97      0.97       389\n",
+            "           2       0.96      0.97      0.96       795\n",
+            "          3+       0.94      0.89      0.92       206\n",
+            "\n",
+            "    accuracy                           0.96      1390\n",
+            "   macro avg       0.96      0.94      0.95      1390\n",
+            "weighted avg       0.96      0.96      0.96      1390\n",
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+              "                            \n",
+              "var gd = document.getElementById('7724a95c-5ff3-45d5-a0ad-d771ac3948e7');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
           },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "train EN/GE, test EN"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "scaleanchor": "y",
-         "title": {
-          "text": "<b>predicted class</b>"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "<b>actual class</b>"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "clf_mx = logistic_regression_classifier(x_train_mx, y_train_mx, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_mx.predict_proba(x_test_en), labels, \"train EN/GE, test EN\", \"cm_nv_pr_MX_EN\")\n",
+        "_ = evaluate_classifier(y_test_ge, None, clf_mx.predict_proba(x_test_ge), labels, \"train EN/GE, test GE\", \"cm_nv_pr_MX_GE\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9ehbi38DWNvu"
       },
-      "text/html": [
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+      "source": [
+        "By comparing to the above results, we observe that the domain-specific fine-tuning on the English training set has improved the scores, but not to a satisfactory level for the cross-language transfer cases."
       ]
-     },
-     "metadata": {},
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-     "text": [
-      "train EN/GE, test GE\n",
-      "accuracy score = 96.3%,  log loss = 0.133,  Brier loss = 0.063\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.97      0.97      0.97       389\n",
-      "           2       0.96      0.97      0.97       795\n",
-      "          3+       0.94      0.90      0.92       206\n",
-      "\n",
-      "    accuracy                           0.96      1390\n",
-      "   macro avg       0.96      0.95      0.95      1390\n",
-      "weighted avg       0.96      0.96      0.96      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kSuSXjMDWNvv"
+      },
+      "source": [
+        "<a id='task_finetuning'></a>\n",
+        "<a name='task_finetuning'></a>\n",
+        "### 4.2. Task-specific fine-tuning\n",
+        "\n",
+        "An alternative to domain-specific fine-tuning is task-specific fine-tuning.\n",
+        "\n",
+        "The idea is to train a transformer model directly on the task at hand, in our case a sequence classification task.\n",
+        "The process is very similar to the masked language modeling used for domain-specific pre-training, except that\n",
+        "we load a sequence classification model using the class `AutoModelForSequenceClassification`.\n",
+        "\n",
+        "The following code tunes a sequence classification model that uses the English accident descriptions to predict\n",
+        "the number of vehicles involved.\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 20 minutes."
+      ]
     },
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+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 172
+        },
+        "id": "N-aQe_a1WNvz",
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+        "pycharm": {
+          "name": "#%%\n"
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-          " <b>2</b> ",
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+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
           ]
-         ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1390' max='1390' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1390/1390 09:49, Epoch 2/2]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>694</td>\n",
+              "      <td>0.154100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>1388</td>\n",
+              "      <td>0.052800</td>\n",
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+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model_cls = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
+        "\n",
+        "def compute_metrics(pred):\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(-1)\n",
+        "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
+        "    acc = accuracy_score(labels, preds)\n",
+        "    return {\"accuracy\": acc, \"f1\": f1}\n",
+        "\n",
+        "# train the model\n",
+        "batch_size = 8\n",
+        "logging_steps = len(dataset_en[\"train\"]) // batch_size\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"models/\" + model_name + \"nv_epochs\",\n",
+        "    num_train_epochs=2,\n",
+        "    per_device_train_batch_size=batch_size,\n",
+        "    per_device_eval_batch_size=batch_size,\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    logging_steps=logging_steps,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "trainer = Trainer(model=model_cls, args=training_args,\n",
+        "                  compute_metrics=compute_metrics, train_dataset=dataset_en[\"train\"],\n",
+        "                  eval_dataset=dataset_en[\"test\"])\n",
+        "trainer.train();\n",
+        "trainer.save_model(\"models/\" + model_name + \"_nv\")"
+      ]
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+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train EN, test EN\n",
+            "accuracy score = 99.6%,  log loss = 0.025,  Brier loss = 0.007\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.99      1.00      0.99       389\n",
+            "           2       1.00      1.00      1.00       795\n",
+            "          3+       1.00      0.99      1.00       206\n",
+            "\n",
+            "    accuracy                           1.00      1390\n",
+            "   macro avg       1.00      1.00      1.00      1390\n",
+            "weighted avg       1.00      1.00      1.00      1390\n",
+            "\n"
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+      ],
+      "source": [
+        "# evaluate model performance using predictions on the English test set\n",
+        "predictions_en = trainer.predict(dataset_en[\"test\"])\n",
+        "_ = evaluate_classifier(predictions_en.label_ids, None, softmax(predictions_en.predictions, axis=1), labels, \"train EN, test EN\", \"cm_nv_tsk_EN_EN\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clf_mx = logistic_regression_classifier(x_train_mx, y_train_mx, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_mx.predict_proba(x_test_en), labels, \"train EN/GE, test EN\", \"cm_nv_pr_MX_EN\")\n",
-    "_ = evaluate_classifier(y_test_ge, None, clf_mx.predict_proba(x_test_ge), labels, \"train EN/GE, test GE\", \"cm_nv_pr_MX_GE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9ehbi38DWNvu"
-   },
-   "source": [
-    "By comparing to the above results, we observe that the domain-specific fine-tuning on the English training set has improved the scores, but not to a satisfactory level for the cross-language transfer cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kSuSXjMDWNvv"
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-   "source": [
-    "<a id='task_finetuning'></a>\n",
-    "<a name='task_finetuning'></a>\n",
-    "### 4.2. Task-specific fine-tuning\n",
-    "\n",
-    "An alternative to domain-specific fine-tuning is task-specific fine-tuning.\n",
-    "\n",
-    "The idea is to train a transformer model directly on the task at hand, in our case a sequence classification task.\n",
-    "The process is very similar to the masked language modeling used for domain-specific pre-training, except that\n",
-    "we load a sequence classification model using the class `AutoModelForSequenceClassification`.\n",
-    "\n",
-    "The following code tunes a sequence classification model that uses the English accident descriptions to predict\n",
-    "the number of vehicles involved.\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 20 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "N-aQe_a1WNvz",
-    "outputId": "03d0c5d2-cfd7-410f-d7e7-ab0fb13ffd18",
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-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"LABEL_0\",\n",
-      "    \"1\": \"LABEL_1\",\n",
-      "    \"2\": \"LABEL_2\"\n",
-      "  },\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"label2id\": {\n",
-      "    \"LABEL_0\": 0,\n",
-      "    \"LABEL_1\": 1,\n",
-      "    \"LABEL_2\": 2\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/7b48683e2e7ba71cd1d7d6551ac325eceee01db5c2f3e81cfbfd1ee7bb7877f2.c24097b0cf91dbc66977325325fd03112f0f13d0e3579abbffc8d1e45f8d0619\n",
-      "Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5. If WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/optimization.py:309: FutureWarning:\n",
-      "\n",
-      "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
-      "\n",
-      "***** Running training *****\n",
-      "  Num examples = 5559\n",
-      "  Num Epochs = 2\n",
-      "  Instantaneous batch size per device = 8\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 1390\n"
-     ]
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-       "      \n",
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-       "      [1390/1390 03:17, Epoch 2/2]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>694</td>\n",
-       "      <td>0.319600</td>\n",
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-       "    <tr>\n",
-       "      <td>1388</td>\n",
-       "      <td>0.079000</td>\n",
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-       "  </tbody>\n",
-       "</table><p>"
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 768
+        },
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+          "name": "stdout",
+          "text": [
+            "train EN, test GE\n",
+            "accuracy score = 99.6%,  log loss = 0.025,  Brier loss = 0.007\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.99      1.00      0.99       389\n",
+            "           2       1.00      1.00      1.00       795\n",
+            "          3+       1.00      0.99      1.00       206\n",
+            "\n",
+            "    accuracy                           1.00      1390\n",
+            "   macro avg       1.00      1.00      1.00      1390\n",
+            "weighted avg       1.00      1.00      1.00      1390\n",
+            "\n"
+          ]
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+              "\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "# evaluate model performance using predictions on the German test set (cross-lingual test)\n",
+        "predictions_ge = trainer.predict(dataset_ge[\"test\"])\n",
+        "_ = evaluate_classifier(predictions_ge.label_ids, None, softmax(predictions_ge.predictions, axis=1), labels, \"train EN, test GE\", \"cm_nv_task_EN_GE\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to models/distilbert-base-multilingual-cased_nv\n",
-      "Configuration saved in models/distilbert-base-multilingual-cased_nv/config.json\n",
-      "Model weights saved in models/distilbert-base-multilingual-cased_nv/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
-    "model_cls = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
-    "\n",
-    "def compute_metrics(pred):\n",
-    "    labels = pred.label_ids\n",
-    "    preds = pred.predictions.argmax(-1)\n",
-    "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
-    "    acc = accuracy_score(labels, preds)\n",
-    "    return {\"accuracy\": acc, \"f1\": f1}\n",
-    "\n",
-    "# train the model\n",
-    "batch_size = 8\n",
-    "logging_steps = len(dataset_en[\"train\"]) // batch_size\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=\"models/\" + model_name + \"nv_epochs\",\n",
-    "    num_train_epochs=2,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    metric_for_best_model=\"f1\",\n",
-    "    logging_steps=logging_steps,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "trainer = Trainer(model=model_cls, args=training_args,\n",
-    "                  compute_metrics=compute_metrics, train_dataset=dataset_en[\"train\"],\n",
-    "                  eval_dataset=dataset_en[\"test\"])\n",
-    "trainer.train();\n",
-    "trainer.save_model(\"models/\" + model_name + \"_nv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 877
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "073wX0bfWNv0",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "The scores on the English test set have improved to fantastic levels.\n",
+        "\n",
+        "What is even more impressive is the performance on cross-lingual transfer:\n",
+        "Despite the fact that the model has been trained on English texts only,\n",
+        "its performance scores on the German test set are very good.\n",
+        "\n",
+        "This is an excellent result!"
+      ]
     },
-    "id": "xkUclLPlWNv0",
-    "outputId": "f87528cd-a5c1-422b-c509-4950b15c6128",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5. If WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1390\n",
-      "  Batch size = 8\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F0fTUwd7WNv0",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "<a id='understand'></a>\n",
+        "<a name='understand'></a>\n",
+        "## 5.&nbsp;Understand Predictions Errors and Interpret Predictions\n",
+        "\n",
+        "In this section you will learn how to analyze prediction errors and how to interpret predictions.\n",
+        "\n",
+        "We will study a more challenging example.\n"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
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-       "      [174/174 00:15]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "brifr977WNv0"
+      },
+      "source": [
+        "<a id='case_study_injuries'></a>\n",
+        "<a name='case_study_injuries'></a>\n",
+        "### 5.1 Case Study: Use Accident Descriptions to Identify Bodily Injury\n",
+        "\n",
+        "As seen in the previous section,\n",
+        "predicting the number of vehicles from the available accident descriptions is a\n",
+        "relatively easy task for the transformer model, even in a multi-lingual situation.\n",
+        "\n",
+        "Therefore, we will turn to a somewhat more difficult task: identifying cases which lead to bodily injuries. We cuse the column `INJSEVB` as label.\n",
+        "\n",
+        "The process is identical to the previous case study:\n",
+        "* Start from the original dataset, enrich it with hidden states produced by the original transformer model\n",
+        "    (before domain-specific fine-tuning).\n",
+        "    Given the experience from the previous task, we use the mean pooling output.\n",
+        "* For comparison, we also load the encodings produced by the transformer model after domain-specific fine-tuning.\n",
+        "* Define the labels.\n",
+        "* Fit a dummy classifier, which always predicts the most frequent class.\n",
+        "* Fit a regression classifier, and evaluate its performance.\n",
+        "\n",
+        "In case you have skipped Section [4.1 Domain-specific finetuning](#domain_finetuning), the dataset `../datasets/dataset_en_pretrained` will not be available.\n",
+        "In this case simply comment out the last lines of each block below."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN, test EN\n",
-      "accuracy score = 99.4%,  log loss = 0.032,  Brier loss = 0.012\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.99      1.00      0.99       389\n",
-      "           2       1.00      0.99      0.99       795\n",
-      "          3+       0.99      0.99      0.99       206\n",
-      "\n",
-      "    accuracy                           0.99      1390\n",
-      "   macro avg       0.99      0.99      0.99      1390\n",
-      "weighted avg       0.99      0.99      0.99      1390\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "k8StUs-DWNv1",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
+        "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
+        "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
+        "#dataset_pr = load_from_disk(\"./datasets/dataset_en_pretrained\")\n",
+        "\n",
+        "# map injuries\n",
+        "labels = [\"0\", \"1\"]\n",
+        "dataset_en = dataset_en.rename_column(\"INJSEVB\", \"labels\")\n",
+        "dataset_ge = dataset_ge.rename_column(\"INJSEVB\", \"labels\")\n",
+        "dataset_mx = dataset_mx.rename_column(\"INJSEVB\", \"labels\")\n",
+        "#dataset_pr = dataset_pr.rename_column(\"INJSEVB\", \"labels\")\n",
+        "\n",
+        "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
+        "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
+        "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")\n",
+        "#x_train_pr, y_train_pr, x_test_pr, y_test_pr = get_xy(dataset_pr, \"mean_hidden_state\", \"labels\")"
+      ]
     },
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+            "Dummy classifier\n",
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+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# fit dummy classifier\n",
+        "clf_dummy = dummy_classifier(x_train_en, y_train_en)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_dummy.predict_proba(x_test_en), labels, \"Dummy classifier\", \"cm_inj_dummy\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 750
+        },
+        "id": "1-OxWef7WNv1",
+        "outputId": "993423cc-8507-43cc-f684-ea58d9f76a0f",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Logistic regression, DistilBERT\n",
+            "accuracy score = 80.1%,  log loss = 0.400,  Brier loss = 0.259\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.83      0.83      0.83       816\n",
+            "           1       0.76      0.75      0.76       574\n",
+            "\n",
+            "    accuracy                           0.80      1390\n",
+            "   macro avg       0.79      0.79      0.79      1390\n",
+            "weighted avg       0.80      0.80      0.80      1390\n",
+            "\n"
+          ]
+        },
+        {
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+              "                            \n",
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+              "\n",
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+              "</body>\n",
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+            ]
           },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "train EN, test EN"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "scaleanchor": "y",
-         "title": {
-          "text": "<b>predicted class</b>"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "<b>actual class</b>"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "# fit logistic regression classifier to the encoded English texts (by the original DistilBERT model)\n",
+        "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
+        "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression, DistilBERT\", \"cm_inj_lr\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yXv_i1aWWNv1"
       },
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+      "source": [
+        "In case you have skipped Section [4.1 Domain-specific finetuning](#domain_finetuning), please also skip the following cell."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# evaluate model performance using predictions on the English test set\n",
-    "predictions_en = trainer.predict(dataset_en[\"test\"])\n",
-    "_ = evaluate_classifier(predictions_en.label_ids, None, softmax(predictions_en.predictions, axis=1), labels, \"train EN, test EN\", \"cm_nv_tsk_EN_EN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 877
     },
-    "id": "AOQnMgLKWNv0",
-    "outputId": "938df424-299f-4ee1-ab6e-537c0e1e754d",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5. If WEATHER7, WEATHER4, SUMMARY_GE, cls_hidden_state, WEATHER1, WEATHER6, INJSEVA, SCASEID, mean_hidden_state, NUMTOTV, WEATHER8, SUMMARY_EN, WEATHER3, index, words per case summary, WEATHER2, INJSEVB, level_0, WEATHER5 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1390\n",
-      "  Batch size = 8\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "dqrYOaCAWNv1",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# fit logistic regression classifier to the encoded English texts (by the fine-tuned DistilBERT model)\n",
+        "#clf_pr = logistic_regression_classifier(x_train_pr, y_train_pr, c=10)\n",
+        "#_ = evaluate_classifier(y_test_pr, None, clf_pr.predict_proba(x_test_pr), labels, \"Logistic regression - 2 epochs pre-training\", \"cm_inj_pr\")"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train EN, test GE\n",
-      "accuracy score = 98.9%,  log loss = 0.046,  Brier loss = 0.019\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           1       0.99      1.00      0.99       389\n",
-      "           2       0.99      0.99      0.99       795\n",
-      "          3+       0.97      0.97      0.97       206\n",
-      "\n",
-      "    accuracy                           0.99      1390\n",
-      "   macro avg       0.98      0.99      0.99      1390\n",
-      "weighted avg       0.99      0.99      0.99      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nzhkQaHsWNv2",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "We observe the following:\n",
+        "* The accuracy score of the dummy classifier is 59%.\n",
+        "* Using the logistic regression classifier on the outputs of the DistilBERT model with two epochs of domain-specific fine-tuning improves the scores compared to using the outputs of the plain DistilBERT model.\n",
+        "* The performance on the class `0` is better than on the class `1` because of a large number of false positives.\n",
+        "\n",
+        "Next, we perform task-specific fine-tuning.\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 20 minutes."
+      ]
     },
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+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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+      ],
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model_cls_inj = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
+        "batch_size = 8\n",
+        "logging_steps = len(dataset_en[\"train\"]) // batch_size\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"models/\" + model_name + \"inj_epochs\",\n",
+        "    num_train_epochs= 2,\n",
+        "    per_device_train_batch_size=batch_size,\n",
+        "    per_device_eval_batch_size=batch_size,\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    disable_tqdm=False,\n",
+        "    logging_steps=logging_steps,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "def compute_metrics(pred):\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(-1)\n",
+        "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
+        "    acc = accuracy_score(labels, preds)\n",
+        "    return {\"accuracy\": acc, \"f1\": f1}\n",
+        "trainer = Trainer(model=model_cls_inj, args=training_args,\n",
+        "                  compute_metrics=compute_metrics, train_dataset=dataset_en[\"train\"], eval_dataset=dataset_en[\"test\"])\n",
+        "trainer.train();\n",
+        "trainer.save_model(\"models/\" + model_name + \"_inj\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# evaluate model performance using predictions on the German test set (cross-lingual test)\n",
-    "predictions_ge = trainer.predict(dataset_ge[\"test\"])\n",
-    "_ = evaluate_classifier(predictions_ge.label_ids, None, softmax(predictions_ge.predictions, axis=1), labels, \"train EN, test GE\", \"cm_nv_task_EN_GE\")"
-   ]
-  },
-  {
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-    "id": "073wX0bfWNv0",
-    "pycharm": {
-     "name": "#%% md\n"
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-   "source": [
-    "The scores on the English test set have improved to fantastic levels.\n",
-    "\n",
-    "What is even more impressive is the performance on cross-lingual transfer:\n",
-    "Despite the fact that the model has been trained on English texts only,\n",
-    "its performance scores on the German test set are very good.\n",
-    "\n",
-    "This is an excellent result!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "F0fTUwd7WNv0",
-    "pycharm": {
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-   },
-   "source": [
-    "<a id='understand'></a>\n",
-    "<a name='understand'></a>\n",
-    "## 5.&nbsp;Understand Predictions Errors and Interpret Predictions\n",
-    "\n",
-    "In this section you will learn how to analyze prediction errors and how to interpret predictions.\n",
-    "\n",
-    "We will study a more challenging example.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "brifr977WNv0"
-   },
-   "source": [
-    "<a id='case_study_injuries'></a>\n",
-    "<a name='case_study_injuries'></a>\n",
-    "### 5.1 Case Study: Use Accident Descriptions to Identify Bodily Injury\n",
-    "\n",
-    "As seen in the previous section,\n",
-    "predicting the number of vehicles from the available accident descriptions is a\n",
-    "relatively easy task for the transformer model, even in a multi-lingual situation.\n",
-    "\n",
-    "Therefore, we will turn to a somewhat more difficult task: identifying cases which lead to bodily injuries. We cuse the column `INJSEVB` as label.\n",
-    "\n",
-    "The process is identical to the previous case study:\n",
-    "* Start from the original dataset, enrich it with hidden states produced by the original transformer model\n",
-    "    (before domain-specific fine-tuning).\n",
-    "    Given the experience from the previous task, we use the mean pooling output.\n",
-    "* For comparison, we also load the encodings produced by the transformer model after domain-specific fine-tuning. \n",
-    "* Define the labels.\n",
-    "* Fit a dummy classifier, which always predicts the most frequent class.\n",
-    "* Fit a regression classifier, and evaluate its performance.\n",
-    "\n",
-    "In case you have skipped Section [4.1 Domain-specific finetuning](#domain_finetuning), the dataset `../datasets/dataset_en_pretrained` will not be available.\n",
-    "In this case simply comment out the last lines of each block below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "id": "k8StUs-DWNv1",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset_en = load_from_disk(\"./datasets/dataset_en\")\n",
-    "dataset_ge = load_from_disk(\"./datasets/dataset_ge\")\n",
-    "dataset_mx = load_from_disk(\"./datasets/dataset_mx\")\n",
-    "dataset_pr = load_from_disk(\"./datasets/dataset_en_pretrained\")\n",
-    "\n",
-    "# map injuries\n",
-    "labels = [\"0\", \"1\"]\n",
-    "dataset_en = dataset_en.rename_column(\"INJSEVB\", \"labels\")\n",
-    "dataset_ge = dataset_ge.rename_column(\"INJSEVB\", \"labels\")\n",
-    "dataset_mx = dataset_mx.rename_column(\"INJSEVB\", \"labels\")\n",
-    "dataset_pr = dataset_pr.rename_column(\"INJSEVB\", \"labels\")\n",
-    "\n",
-    "x_train_en, y_train_en, x_test_en, y_test_en = get_xy(dataset_en, \"mean_hidden_state\", \"labels\")\n",
-    "x_train_ge, y_train_ge, x_test_ge, y_test_ge = get_xy(dataset_ge, \"mean_hidden_state\", \"labels\")\n",
-    "x_train_mx, y_train_mx, x_test_mx, y_test_mx = get_xy(dataset_mx, \"mean_hidden_state\", \"labels\")\n",
-    "x_train_pr, y_train_pr, x_test_pr, y_test_pr = get_xy(dataset_pr, \"mean_hidden_state\", \"labels\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {
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-     "height": 750
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-    "id": "19S0Ps_2WNv1",
-    "outputId": "e0d46046-ece0-4a63-ff38-5cc21603d4cc",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dummy classifier\n",
-      "accuracy score = 58.7%,  log loss = 0.679,  Brier loss = 0.486\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.59      1.00      0.74       816\n",
-      "           1       0.00      0.00      0.00       574\n",
-      "\n",
-      "    accuracy                           0.59      1390\n",
-      "   macro avg       0.29      0.50      0.37      1390\n",
-      "weighted avg       0.34      0.59      0.43      1390\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "id": "7ejzOSBWWNv2"
+      },
+      "outputs": [],
+      "source": [
+        "# Execute the following line to load the trained model from disk.\n",
+        "# trainer = Trainer(AutoModelForSequenceClassification.from_pretrained(model_name+\"_inj\", num_labels=len(labels)).to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")))"
+      ]
     },
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+          "text": [
+            "DistilBERT classifier - 2 epochs task-specific\n",
+            "accuracy score = 89.1%,  log loss = 0.297,  Brier loss = 0.174\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.91      0.90      0.91       816\n",
+            "           1       0.86      0.87      0.87       574\n",
+            "\n",
+            "    accuracy                           0.89      1390\n",
+            "   macro avg       0.89      0.89      0.89      1390\n",
+            "weighted avg       0.89      0.89      0.89      1390\n",
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+      ],
+      "source": [
+        "# evaluate model performance using predictions on the English test set\n",
+        "predictions_en = trainer.predict(dataset_en[\"test\"])\n",
+        "_ = evaluate_classifier(predictions_en.label_ids, None, softmax(predictions_en.predictions, axis=1), labels,\n",
+        "                        \"DistilBERT classifier - 2 epochs task-specific\", \"cm_inj_tsk\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fit dummy classifier\n",
-    "clf_dummy = dummy_classifier(x_train_en, y_train_en)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_dummy.predict_proba(x_test_en), labels, \"Dummy classifier\", \"cm_inj_dummy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
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     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logistic regression, DistilBERT\n",
-      "accuracy score = 80.1%,  log loss = 0.400,  Brier loss = 0.259\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.83      0.83      0.83       816\n",
-      "           1       0.76      0.75      0.76       574\n",
-      "\n",
-      "    accuracy                           0.80      1390\n",
-      "   macro avg       0.79      0.79      0.79      1390\n",
-      "weighted avg       0.80      0.80      0.80      1390\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcogYs7wWNv2"
+      },
+      "source": [
+        "We observe the following:\n",
+        "* Task-specific fine-tuning has further improved all scores.\n",
+        "* There is still a relatively large number of false positives."
+      ]
     },
     {
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+      },
+      "source": [
+        "<a id='investigate'></a>\n",
+        "<a name='investigate'></a>\n",
+        "### 5.2. Investigate False Positives and False Negatives\n",
+        "\n",
+        "To investigate the prediction errors, we export the predictions into an Excel file with the following columns:\n",
+        "\n",
+        "| column | meaning |\n",
+        "|---|---|\n",
+        "| `SCASEID` | unique identification number of the case |\n",
+        "| `SUMMARY_EN` | description of the accident, in English |\n",
+        "| `SUMMARY_TRUNCATED` | description of the accident, in English, truncated to a length of 512 tokens |\n",
+        "| `INJSEVA` |  most serious injury sustained in the case, as per Police Accident Report |\n",
+        "| `labels` |  indicator of odily injury `INJSEVB` (true label) |\n",
+        "| `pred` | predicted label |\n",
+        "| `0` | probability of negative label |\n",
+        "| `1` | probability of positive label |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "ZAfQV892WNv3"
+      },
+      "outputs": [],
+      "source": [
+        "# export prediction results for error analysis\n",
+        "dataset_en.set_format(type=\"pandas\")\n",
+        "df_res = pd.concat([dataset_en[\"test\"].to_pandas(),\n",
+        "                    pd.DataFrame(data=softmax(predictions_en.predictions, axis=1), columns=[\"0\", \"1\"]),\n",
+        "                    pd.DataFrame(data=np.argmax(predictions_en.predictions, -1).reshape((-1,1)), columns=['pred'])\n",
+        "                ], axis=1)\n",
+        "df_res = df_res[[\"SCASEID\", \"SUMMARY_EN\", \"INJSEVA\", \"labels\", \"pred\", \"0\", \"1\"]]\n",
+        "dataset_en.set_format()\n",
+        "for i in range(df_res.shape[0]):\n",
+        "    df_res.loc[i, \"SUMMARY_TRUNCATED\"] = tokenizer.convert_tokens_to_string(tokenizer.tokenize(df_res.loc[i, \"SUMMARY_EN\"], truncation=True))\n",
+        "df_res.to_excel(\"./results/error_analysis_inj.xlsx\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OR2KmOV2WNv3"
+      },
+      "source": [
+        "The first step of the error analysis is to inspect the samples producing false negative and false positive predictions.\n",
+        "Reading every single text would be very tedious, therefore it is worthwhile focusing on those examples where the probability assigned to the false prediction was high,\n",
+        "i.e., cases where the model was confident but wrong.\n",
+        "\n",
+        "Looking at the false negatives, we observe that there are many cases where the model assigns a high probability to negative.\n",
+        "We suspect that truncation is responsible for many of the false negatives – the relevant part of the text was discarded.\n",
+        "\n",
+        "To address this issue, we split the text into slightly overlapping chunks,\n",
+        "run the prediction on each chunk and apply the logical OR-function to the results.\n",
+        "We implement this functionality in a simple function that returns an additional column `pred`,\n",
+        "containing a list of predicted labels, with one element for each chunk."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "id": "oEKN3riTWNv3"
+      },
+      "outputs": [],
+      "source": [
+        "def predict_with_overflow(x, model, feature):\n",
+        "    t = tokenizer(x[feature], truncation=True, padding=True, return_overflowing_tokens=True)\n",
+        "    input_ids = torch.tensor(t[\"input_ids\"]).to(model.device)\n",
+        "    attention_mask = torch.tensor(t[\"attention_mask\"]).to(model.device)\n",
+        "    with torch.no_grad():\n",
+        "        preds = np.argmax(model(input_ids, attention_mask).logits.cpu(), -1)\n",
+        "    return {\"preds\": preds}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "ubKYwONYWNv3"
+      },
+      "outputs": [],
+      "source": [
+        "# Execute the following lines to load the trained model and the okenizer from disk.\n",
+        "# model_cls_inj = AutoModelForSequenceClassification.from_pretrained(\"models/\" + model_name + \"_inj\", num_labels=len(labels)).to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n",
+        "# tokenizer = AutoTokenizer.from_pretrained(model_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "colab": {
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+      ],
+      "source": [
+        "dataset_en_overflow = dataset_en[\"test\"].map(predict_with_overflow, batched=False, fn_kwargs={\"model\": model_cls_inj, \"feature\": \"SUMMARY_EN\"})\n",
+        "dataset_en_overflow = dataset_en_overflow.map(lambda x: {\"pred\": max(x[\"preds\"])})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 750
+        },
+        "id": "UqudHi0pWNv4",
+        "outputId": "19df3943-6f9f-4673-9a18-74bc8f1c730a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "DistilBERT classifier - split inputs\n",
+            "accuracy score = 91.5%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.95      0.90      0.93       816\n",
+            "           1       0.87      0.94      0.90       574\n",
+            "\n",
+            "    accuracy                           0.92      1390\n",
+            "   macro avg       0.91      0.92      0.91      1390\n",
+            "weighted avg       0.92      0.92      0.92      1390\n",
+            "\n"
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+      "source": [
+        "_ = evaluate_classifier(predictions_en.label_ids, dataset_en_overflow[\"pred\"], None, labels,\n",
+        "                        \"DistilBERT classifier - split inputs\", \"cm_inj_split\")"
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-    "# fit logistic regression classifier to the encoded English texts (by the original DistilBERT model)\n",
-    "clf_en = logistic_regression_classifier(x_train_en, y_train_en, c=10)\n",
-    "_ = evaluate_classifier(y_test_en, None, clf_en.predict_proba(x_test_en), labels, \"Logistic regression, DistilBERT\", \"cm_inj_lr\")"
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-    "In case you have skipped Section [4.1 Domain-specific finetuning](#domain_finetuning), please also skip the following cell."
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-      "Logistic regression - 2 epochs pre-training\n",
-      "accuracy score = 82.7%,  log loss = 0.375,  Brier loss = 0.238\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.85      0.86      0.85       816\n",
-      "           1       0.79      0.79      0.79       574\n",
-      "\n",
-      "    accuracy                           0.83      1390\n",
-      "   macro avg       0.82      0.82      0.82      1390\n",
-      "weighted avg       0.83      0.83      0.83      1390\n",
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+      "outputs": [],
+      "source": [
+        "dataset_en_overflow.set_format(type=\"pandas\")\n",
+        "df_res = dataset_en_overflow.to_pandas()\n",
+        "df_res = df_res[[\"SCASEID\", \"SUMMARY_EN\", \"INJSEVA\", \"labels\", \"pred\"]]\n",
+        "dataset_en.set_format()\n",
+        "for i in range(df_res.shape[0]):\n",
+        "    df_res.loc[i, \"SUMMARY_TRUNCATED\"] = tokenizer.convert_tokens_to_string(tokenizer.tokenize(df_res.loc[i, \"SUMMARY_EN\"], truncation=True))\n",
+        "df_res.to_excel(\"./results/error_analysis_inj_overflow.xlsx\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fit logistic regression classifier to the encoded English texts (by the fine-tuned DistilBERT model)\n",
-    "clf_pr = logistic_regression_classifier(x_train_pr, y_train_pr, c=10)\n",
-    "_ = evaluate_classifier(y_test_pr, None, clf_pr.predict_proba(x_test_pr), labels, \"Logistic regression - 2 epochs pre-training\", \"cm_inj_pr\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nzhkQaHsWNv2",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We observe the following:\n",
-    "* The accuracy score of the dummy classifier is 59%.\n",
-    "* Using the logistic regression classifier on the outputs of the DistilBERT model with two epochs of domain-specific fine-tuning improves the scores compared to using the outputs of the plain DistilBERT model.\n",
-    "* The performance on the class `0` is better than on the class `1` because of a large number of false positives.\n",
-    "\n",
-    "Next, we perform task-specific fine-tuning.\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 20 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "D2usIbC0WNv2",
-    "outputId": "935fcaa6-35e4-4fe0-a920-428303c764c6",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/7b48683e2e7ba71cd1d7d6551ac325eceee01db5c2f3e81cfbfd1ee7bb7877f2.c24097b0cf91dbc66977325325fd03112f0f13d0e3579abbffc8d1e45f8d0619\n",
-      "Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: mean_hidden_state, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, cls_hidden_state, WEATHER5, WEATHER1, WEATHER6. If mean_hidden_state, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, cls_hidden_state, WEATHER5, WEATHER1, WEATHER6 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/optimization.py:309: FutureWarning:\n",
-      "\n",
-      "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
-      "\n",
-      "***** Running training *****\n",
-      "  Num examples = 5559\n",
-      "  Num Epochs = 2\n",
-      "  Instantaneous batch size per device = 8\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 1390\n"
-     ]
     },
     {
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-       "    <div>\n",
-       "      \n",
-       "      <progress value='1390' max='1390' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [1390/1390 03:17, Epoch 2/2]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>694</td>\n",
-       "      <td>0.537200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1388</td>\n",
-       "      <td>0.316700</td>\n",
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-       "</table><p>"
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+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MNP9SIeHWNv4"
+      },
+      "source": [
+        "The number of false negatives has reduced significantly, as expected, and the accuracy score has improved.\n",
+        "Since we have not implemented a logic to combine the predicted probabilities of the different chunks, the log loss and Brier loss cannot be evaluated in this case."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to models/distilbert-base-multilingual-cased_inj\n",
-      "Configuration saved in models/distilbert-base-multilingual-cased_inj/config.json\n",
-      "Model weights saved in models/distilbert-base-multilingual-cased_inj/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model \n",
-    "model_cls_inj = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
-    "batch_size = 8\n",
-    "logging_steps = len(dataset_en[\"train\"]) // batch_size\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=\"models/\" + model_name + \"inj_epochs\",\n",
-    "    num_train_epochs= 2,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    metric_for_best_model=\"f1\",\n",
-    "    disable_tqdm=False,\n",
-    "    logging_steps=logging_steps,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "def compute_metrics(pred):\n",
-    "    labels = pred.label_ids\n",
-    "    preds = pred.predictions.argmax(-1)\n",
-    "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
-    "    acc = accuracy_score(labels, preds)\n",
-    "    return {\"accuracy\": acc, \"f1\": f1}\n",
-    "trainer = Trainer(model=model_cls_inj, args=training_args,\n",
-    "                  compute_metrics=compute_metrics, train_dataset=dataset_en[\"train\"], eval_dataset=dataset_en[\"test\"])\n",
-    "trainer.train();\n",
-    "trainer.save_model(\"models/\" + model_name + \"_inj\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "id": "7ejzOSBWWNv2"
-   },
-   "outputs": [],
-   "source": [
-    "# Execute the following line to load the trained model from disk.\n",
-    "# trainer = Trainer(AutoModelForSequenceClassification.from_pretrained(model_name+\"_inj\", num_labels=len(labels)).to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 860
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZC7QButQWNv4"
+      },
+      "source": [
+        "<a id='interpret'></a>\n",
+        "<a name='interpret'></a>\n",
+        "### 5.3. Use Captum and `transformers-interpret`  to Interpret Predictions\n",
+        "\n",
+        "\n",
+        "Transformer models are quite complex, and therefore, interpreting model output can be difficult.\n",
+        "\n",
+        "Our main interest is in knowing which parts of the input text cause the classifier to arrive at a particular prediction.\n",
+        "One way to answer this question is the so-called integrated gradients method.\n",
+        "It is provided conveniently by the library [transformers_interpret](https://github.com/cdpierse/transformers-interpret)\n",
+        "which provides a convenient interface to the library [Captum](https://captum.ai/),\n",
+        "an open source, extensible library for model interpretability built on PyTorch.\n",
+        "\n",
+        "With just a few lines of code, we can run this on individual examples, and receive a graphical output  as shown below.\n",
+        "Of course, the output is also available in numerical form.\n",
+        "We run this on CPU because on the AWS p2.xlarge instance, the GPU ran out of memory."
+      ]
     },
-    "id": "mvIWMTR2WNv2",
-    "outputId": "6f43c861-36eb-4166-a5a4-f492f7f8f54f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: mean_hidden_state, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, cls_hidden_state, WEATHER5, WEATHER1, WEATHER6. If mean_hidden_state, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, cls_hidden_state, WEATHER5, WEATHER1, WEATHER6 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1390\n",
-      "  Batch size = 8\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "qk8aeppCWNv4"
+      },
+      "outputs": [],
+      "source": [
+        "device = torch.device(\"cpu\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "model = model_cls_inj.to(device)\n",
+        "cls_explainer = SequenceClassificationExplainer(model, tokenizer)"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='174' max='174' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [174/174 00:07]\n",
-       "    </div>\n",
-       "    "
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "id": "5-OUMWc4WNv5",
+        "outputId": "2df1256e-17c4-4127-f104-8431a1322151"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.97)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>2.32</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> three                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> morning                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weekend                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> multi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> near                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entrance                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> runs                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> divided                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> high                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tension                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cable                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> guard                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##rail                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> location                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 89                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 65                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> there                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##verse                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weather                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> conditions                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2006                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Je                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ep                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Liberty                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nts                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> three                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> go                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1992                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Mitsubishi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Dia                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mante                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> go                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1996                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pick                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##up                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ac                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eration                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> merge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> An                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behind                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> switched                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cut                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> avoid                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> changing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Subsequently                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> departed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ac                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eration                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> After                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entered                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rolle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> est                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 6                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turns                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roof                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> drove                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 45                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Je                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ep                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> over                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> line                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##corre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ct                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> eva                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sive                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> action                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Other                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> chose                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ina                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##pp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##rop                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##riate                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> eva                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sive                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> action                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failure                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 40                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> because                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> language                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> barrier                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Korean                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); 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                    line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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+        "# true positive\n",
+        "s = tokenizer.decode(dataset_en[\"test\"][144][\"input_ids\"][1:511])\n",
+        "word_attributions = cls_explainer(s, n_steps=20)\n",
+        "cls_explainer.visualize(\"./results/viz_144.html\");"
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-      "DistilBERT classifier - 2 epochs task-specific\n",
-      "accuracy score = 90.0%,  log loss = 0.266,  Brier loss = 0.154\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
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-      "           0       0.91      0.92      0.92       816\n",
-      "           1       0.88      0.87      0.88       574\n",
-      "\n",
-      "    accuracy                           0.90      1390\n",
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+              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.94)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>4.12</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> und                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ivi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ded                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> asp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##halt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> slightly                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 64                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 40                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> early                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> conditions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> flow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> restrictions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2002                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chrysler                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Se                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bring                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> door                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> convert                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ible                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> beginning                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> nego                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##tia                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##te                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> departed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> telephone                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pole                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> located                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rota                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clock                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wise                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> then                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> trip                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ped                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> over                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rolle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turns                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roof                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 69                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 86%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 64%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> suffered                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> moderate                    </font></mark><mark style=\"background-color: hsl(120, 75%, 74%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injuries                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> has                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> since                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> been                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> put                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> nur                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> home                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> does                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reca                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ll                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> any                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> information                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accident                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accident                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> report                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> records                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> blood                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> content                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 0                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 177                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Associated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> use                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> condition                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> diabetes                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> use                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##scription                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> med                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ication                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> diabetes                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reports                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> also                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> history                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ism                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); 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opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
+            ]
           },
-          "colorscale": {
-           "diverging": [
-            [
-             0,
-             "#8e0152"
-            ],
-            [
-             0.1,
-             "#c51b7d"
-            ],
-            [
-             0.2,
-             "#de77ae"
-            ],
-            [
-             0.3,
-             "#f1b6da"
-            ],
-            [
-             0.4,
-             "#fde0ef"
-            ],
-            [
-             0.5,
-             "#f7f7f7"
-            ],
-            [
-             0.6,
-             "#e6f5d0"
-            ],
-            [
-             0.7,
-             "#b8e186"
-            ],
-            [
-             0.8,
-             "#7fbc41"
-            ],
-            [
-             0.9,
-             "#4d9221"
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# true positive\n",
+        "s = tokenizer.decode(dataset_en[\"test\"][18][\"input_ids\"][1:511])\n",
+        "word_attributions = cls_explainer(s, n_steps=20)\n",
+        "cls_explainer.visualize(\"./results/viz_18.html\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "id": "3mXRYEluWNv5",
+        "outputId": "fdf5dfb7-6f3f-48e0-c1bd-eab23be71d5b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ],
-            [
-             1,
-             "#276419"
+            "text/html": [
+              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_0 (0.75)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_0</b></text></td><td><text style=\"padding-right:2em\"><b>6.24</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> late                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> up                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##hill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> controls                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 56                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 30                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2007                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Ford                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> e                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cono                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##line                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> van                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> thirty                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 34                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> takes                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> med                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ication                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> or                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> has                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> any                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> restrictions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1994                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Honda                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Civic                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sedan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> aged                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> According                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> witness                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> high                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rate                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempting                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pass                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> fled                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> foot                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> leaving                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Both                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> did                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> know                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speak                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> about                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> his                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> illegal                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> status                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> country                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 86%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> motor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> same                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> higher                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 81%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> job                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> site                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> another                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ended                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> He                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> residential                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> area                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> observed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> approach                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> his                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mirror                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 76%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> motor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> same                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lower                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> st                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eady                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 86%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> e                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> g                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> An                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> associated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> excessive                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mis                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##jud                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gap                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tire                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wrong                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> size                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tire                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> low                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> depth                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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                    line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; 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opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
             ]
-           ],
-           "sequential": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# false negative: \"leaving an injured passenger\" overlooked\n",
+        "s = tokenizer.decode(dataset_en[\"test\"][331][\"input_ids\"][1:511])\n",
+        "word_attributions = cls_explainer(s, n_steps=20)\n",
+        "cls_explainer.visualize(\"./results/viz_331.html\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "id": "Gvq1xnu5WNv5",
+        "outputId": "cb420c49-18b3-4ef8-b981-7ee44b0bb36e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ],
-            [
-             1,
-             "#f0f921"
+            "text/html": [
+              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.97)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>1.93</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> /                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shoulder                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ward                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> radius                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curva                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ture                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 274                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> positive                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 4                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> %                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> grade                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Initially                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> there                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> grass                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> div                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##iding                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ended                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> only                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> double                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> yellow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> line                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> separat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> directions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> A                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sect                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> southeast                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Con                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ditions                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dark                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1987                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Mercury                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Marquis                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> apparently                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> confused                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 29                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> prior                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intersection                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> went                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> down                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> steep                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 62                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> %                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ground                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bottom                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> just                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shoulder                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 54                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> un                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##belt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 51%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> transported                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facility                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> adult                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passengers                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 8                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> month                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> safety                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> also                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> went                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> out                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wrong                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> exit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gas                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> station                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> few                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hundred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intended                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> circle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> back                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> around                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> enter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shopping                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> center                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> located                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gas                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> station                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> App                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##aren                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##tly                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> thought                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sign                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> identify                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> streets                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> name                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intersection                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> opposed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> initiated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 29                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> before                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inter                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sect                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> began                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> said                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> once                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> started                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> realized                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> error                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> brak                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##e                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> so                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> steep                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> recover                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> In                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##vesti                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gating                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tro                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##oper                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> agree                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> researcher                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> have                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> contributed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scenario                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> required                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> follow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> up                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rete                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sting                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> state                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> license                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> center                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> recognition                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> error                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> too                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> early                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Associated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> included                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> con                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##versi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ng                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failure                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> A                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> view                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ob                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##stru                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> included                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
             ]
-           ],
-           "sequentialminus": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# false positive:\n",
+        "s = tokenizer.decode(dataset_en[\"test\"][78][\"input_ids\"][1:511])\n",
+        "word_attributions = cls_explainer(s, n_steps=20)\n",
+        "cls_explainer.visualize(\"./results/viz_78.html\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "id": "GIUENaqY-NRX",
+        "outputId": "68b6eaff-0f70-4a84-856e-77684af9e900"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ],
-            [
-             1,
-             "#f0f921"
+            "text/html": [
+              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.95)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>4.93</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> divided                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> painted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 72                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 45                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> which                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reduce                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 56                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 35                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 100                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> site                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sign                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicating                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> narrow                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weather                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cloud                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##y                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> partially                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wet                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> flow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> normal                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> time                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> day                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> afternoon                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2002                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Alt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ima                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behind                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1991                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chevrolet                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Lu                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mina                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> drove                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> safety                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> zone                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##coming                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> order                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> illegal                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pass                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> returned                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> original                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> panel                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clock                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wise                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> position                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 180                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> degrees                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> coming                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> northeast                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##erly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##erly                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 58%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hospital                    </font></mark><mark style=\"background-color: hsl(120, 75%, 85%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> baby                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Alt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ima                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 38                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> He                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> he                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> did                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> want                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> both                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ered                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sh                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> t                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entering                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> aggressive                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driving                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behavior                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chevrolet                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 21                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 18                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 6                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> month                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> car                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> second                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> row                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> taken                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hospital                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> check                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> out                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accompanied                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> both                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> people                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> relative                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seen                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> making                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wild                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ge                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##stu                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##res                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tail                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gating                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> saw                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> coming                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> around                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> only                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> brak                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##e                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> before                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
             ]
-           ]
-          },
-          "colorway": [
-           "#636efa",
-           "#EF553B",
-           "#00cc96",
-           "#ab63fa",
-           "#FFA15A",
-           "#19d3f3",
-           "#FF6692",
-           "#B6E880",
-           "#FF97FF",
-           "#FECB52"
-          ],
-          "font": {
-           "color": "#2a3f5f"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "#E5ECF6",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "radialaxis": {
-            "gridcolor": "white",
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-           "zaxis": {
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           },
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-          "ternary": {
-           "aaxis": {
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-           "baxis": {
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-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
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-          },
-          "title": {
-           "x": 0.05
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-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-        },
-        "title": {
-         "text": "DistilBERT classifier - 2 epochs task-specific"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
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-          1
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-         "scaleanchor": "y",
-         "title": {
-          "text": "<b>predicted class</b>"
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-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
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-          1
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-         "title": {
-          "text": "<b>actual class</b>"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "# false positive:\n",
+        "s = tokenizer.decode(dataset_en[\"test\"][915][\"input_ids\"][1:511])\n",
+        "word_attributions = cls_explainer(s, n_steps=20)\n",
+        "cls_explainer.visualize(\"./results/viz_915.html\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iBJ99TPuWNv5"
       },
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+      "source": [
+        "<a id='qna'></a>\n",
+        "<a name='qna'></a>\n",
+        "## 6.&nbsp;Using Extractive Question Answering to Process Longer Texts\n",
+        "\n",
+        "In this section we use extractive question answering to extract parts of the accident description which indicate the presence of bodily injury. The aim is to reduce the length of the input texts by extracting only the relevant parts.\n",
+        "\n",
+        "The easiest implementation of extractive question answering is provided by the `pipeline` abstraction.\n",
+        "\n",
+        "We use [`deutsche-telekom/bert-multi-english-german-squad2`](https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2),\n",
+        "a multilingual English German question answering model built on `bert-base-multilingual-cased`. By specifying `device=0` we use GPU support."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# evaluate model performance using predictions on the English test set\n",
-    "predictions_en = trainer.predict(dataset_en[\"test\"])\n",
-    "_ = evaluate_classifier(predictions_en.label_ids, None, softmax(predictions_en.predictions, axis=1), labels,\n",
-    "                        \"DistilBERT classifier - 2 epochs task-specific\", \"cm_inj_tsk\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OcogYs7wWNv2"
-   },
-   "source": [
-    "We observe the following:\n",
-    "* Task-specific fine-tuning has further improved all scores.\n",
-    "* There is still a relatively large number of false positives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "udjhQrSpWNv2",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "<a id='investigate'></a>\n",
-    "<a name='investigate'></a>\n",
-    "### 5.2. Investigate False Positives and False Negatives\n",
-    "\n",
-    "To investigate the prediction errors, we export the predictions into an Excel file with the following columns:\n",
-    "\n",
-    "| column | meaning |\n",
-    "|---|---|\n",
-    "| `SCASEID` | unique identification number of the case |\n",
-    "| `SUMMARY_EN` | description of the accident, in English |\n",
-    "| `SUMMARY_TRUNCATED` | description of the accident, in English, truncated to a length of 512 tokens |\n",
-    "| `INJSEVA` |  most serious injury sustained in the case, as per Police Accident Report |\n",
-    "| `labels` |  indicator of odily injury `INJSEVB` (true label) |\n",
-    "| `pred` | predicted label |\n",
-    "| `0` | probability of negative label |\n",
-    "| `1` | probability of positive label |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "id": "ZAfQV892WNv3"
-   },
-   "outputs": [],
-   "source": [
-    "# export prediction results for error analysis\n",
-    "dataset_en.set_format(type=\"pandas\")\n",
-    "df_res = pd.concat([dataset_en[\"test\"].to_pandas(),\n",
-    "                    pd.DataFrame(data=softmax(predictions_en.predictions, axis=1), columns=[\"0\", \"1\"]),\n",
-    "                    pd.DataFrame(data=np.argmax(predictions_en.predictions, -1).reshape((-1,1)), columns=['pred'])\n",
-    "                ], axis=1)\n",
-    "df_res = df_res[[\"SCASEID\", \"SUMMARY_EN\", \"INJSEVA\", \"labels\", \"pred\", \"0\", \"1\"]]\n",
-    "dataset_en.set_format()\n",
-    "for i in range(df_res.shape[0]):\n",
-    "    df_res.loc[i, \"SUMMARY_TRUNCATED\"] = tokenizer.convert_tokens_to_string(tokenizer.tokenize(df_res.loc[i, \"SUMMARY_EN\"], truncation=True))\n",
-    "df_res.to_excel(\"./results/error_analysis_inj.xlsx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OR2KmOV2WNv3"
-   },
-   "source": [
-    "The first step of the error analysis is to inspect the samples producing false negative and false positive predictions.\n",
-    "Reading every single text would be very tedious, therefore it is worthwhile focusing on those examples where the probability assigned to the false prediction was high,\n",
-    "i.e., cases where the model was confident but wrong.\n",
-    "\n",
-    "Looking at the false negatives, we observe that there are many cases where the model assigns a high probability to negative.\n",
-    "We suspect that truncation is responsible for many of the false negatives – the relevant part of the text was discarded.\n",
-    "\n",
-    "To address this issue, we split the text into slightly overlapping chunks,\n",
-    "run the prediction on each chunk and apply the logical OR-function to the results.\n",
-    "We implement this functionality in a simple function that returns an additional column `pred`,\n",
-    "containing a list of predicted labels, with one element for each chunk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "id": "oEKN3riTWNv3"
-   },
-   "outputs": [],
-   "source": [
-    "def predict_with_overflow(x, model, feature):\n",
-    "    t = tokenizer(x[feature], truncation=True, padding=True, return_overflowing_tokens=True)\n",
-    "    input_ids = torch.tensor(t[\"input_ids\"]).to(model.device)\n",
-    "    attention_mask = torch.tensor(t[\"attention_mask\"]).to(model.device)\n",
-    "    with torch.no_grad():\n",
-    "        preds = np.argmax(model(input_ids, attention_mask).logits.cpu(), -1)\n",
-    "    return {\"preds\": preds}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "id": "ubKYwONYWNv3"
-   },
-   "outputs": [],
-   "source": [
-    "# Execute the following lines to load the trained model and the okenizer from disk.\n",
-    "# model_cls_inj = AutoModelForSequenceClassification.from_pretrained(\"models/\" + model_name + \"_inj\", num_labels=len(labels)).to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n",
-    "# tokenizer = AutoTokenizer.from_pretrained(model_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "colab": {
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-     "height": 81,
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-      "text/plain": [
-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/817 [00:00<?, ?B/s]"
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+          "data": {
+            "text/plain": [
+              "Downloading model.safetensors:   0%|          | 0.00/709M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/264 [00:00<?, ?B/s]"
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+        {
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+          "data": {
+            "text/plain": [
+              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e47af479ba2947728edead722e37507a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ],
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+      ],
+      "source": [
+        "model_name_qa =\"deutsche-telekom/bert-multi-english-german-squad2\"\n",
+        "pl = pipeline(\"question-answering\", model=model_name_qa, tokenizer=model_name_qa, device=0)\n",
+        "questions = [\"Was someone injured?\", \"Was someone transported?\"]"
       ]
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-     "metadata": {},
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       },
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-       "  0%|          | 0/1390 [00:00<?, ?ex/s]"
+      "source": [
+        "We visit each accident report in turn (the context), and ask the model the two questions “Was someone injured?”\n",
+        "and “Was someone transported?”.\n",
+        "Since the accident reports might provide information on multiple persons,\n",
+        "we allow a maximum of four candidate answers for each of the questions,\n",
+        "which we concatenate into a single (much shorter) new text.\n",
+        "\n",
+        "To achieve this, we write a short function which applies a question answering pipeline to an input text `x`.\n",
+        "The argument `questions` is a list of questions."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset_en_overflow = dataset_en[\"test\"].map(predict_with_overflow, batched=False, fn_kwargs={\"model\": model_cls_inj, \"feature\": \"SUMMARY_EN\"})\n",
-    "dataset_en_overflow = dataset_en_overflow.map(lambda x: {\"pred\": max(x[\"preds\"])})"
-   ]
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-   "cell_type": "code",
-   "execution_count": 58,
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-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DistilBERT classifier - split inputs\n",
-      "accuracy score = 93.3%,  log loss = nan,  Brier loss = nan\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.97      0.91      0.94       816\n",
-      "           1       0.89      0.96      0.92       574\n",
-      "\n",
-      "    accuracy                           0.93      1390\n",
-      "   macro avg       0.93      0.94      0.93      1390\n",
-      "weighted avg       0.94      0.93      0.93      1390\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "Oe8OTkSFWNv6"
+      },
+      "outputs": [],
+      "source": [
+        "def get_answers(x, qa_pipeline, questions):\n",
+        "    x[\"INJ\"] = \"\"\n",
+        "    for question in questions:\n",
+        "        res = qa_pipeline(context=x[\"SUMMARY_EN\"], question=question, top_k=4, handle_impossible_answer=True)\n",
+        "        if isinstance(res, dict):\n",
+        "            res = [res]\n",
+        "        if len(res[0]) > 0:\n",
+        "            x[\"INJ\"] = '. '.join([x[\"INJ\"]] + [item[\"answer\"] for item in res])\n",
+        "    return x"
+      ]
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-        }
-       },
-       "data": [
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+      "metadata": {
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+      "source": [
+        "We apply the question answering function to the entire test set.\n",
+        "\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 6 minutes. If you want to try the concept on only the first 250 samples, you can use `ds_test = dataset[\"test\"].select(range(250).map(...`"
+      ]
+    },
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-   "source": [
-    "_ = evaluate_classifier(predictions_en.label_ids, dataset_en_overflow[\"pred\"], None, labels,\n",
-    "                        \"DistilBERT classifier - split inputs\", \"cm_inj_split\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "id": "mOG699NKWNv4"
-   },
-   "outputs": [],
-   "source": [
-    "dataset_en_overflow.set_format(type=\"pandas\")\n",
-    "df_res = dataset_en_overflow.to_pandas()\n",
-    "df_res = df_res[[\"SCASEID\", \"SUMMARY_EN\", \"INJSEVA\", \"labels\", \"pred\"]]\n",
-    "dataset_en.set_format()\n",
-    "for i in range(df_res.shape[0]):\n",
-    "    df_res.loc[i, \"SUMMARY_TRUNCATED\"] = tokenizer.convert_tokens_to_string(tokenizer.tokenize(df_res.loc[i, \"SUMMARY_EN\"], truncation=True))\n",
-    "df_res.to_excel(\"./results/error_analysis_inj_overflow.xlsx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MNP9SIeHWNv4"
-   },
-   "source": [
-    "The number of false negatives has reduced significantly, as expected, and the accuracy score has improved.\n",
-    "Since we have not implemented a logic to combine the predicted probabilities of the different chunks, the log loss and Brier loss cannot be evaluated in this case."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZC7QButQWNv4"
-   },
-   "source": [
-    "<a id='interpret'></a>\n",
-    "<a name='interpret'></a>\n",
-    "### 5.3. Use Captum and `transformers-interpret`  to Interpret Predictions\n",
-    "\n",
-    "\n",
-    "Transformer models are quite complex, and therefore, interpreting model output can be difficult.\n",
-    "\n",
-    "Our main interest is in knowing which parts of the input text cause the classifier to arrive at a particular prediction.\n",
-    "One way to answer this question is the so-called integrated gradients method.\n",
-    "It is provided conveniently by the library [transformers_interpret](https://github.com/cdpierse/transformers-interpret)\n",
-    "which provides a convenient interface to the library [Captum](https://captum.ai/),\n",
-    "an open source, extensible library for model interpretability built on PyTorch.\n",
-    "\n",
-    "With just a few lines of code, we can run this on individual examples, and receive a graphical output  as shown below.\n",
-    "Of course, the output is also available in numerical form.\n",
-    "We run this on CPU because on the AWS p2.xlarge instance, the GPU ran out of memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "qk8aeppCWNv4",
-    "outputId": "f76d8780-79fc-4604-a7a0-1f2756b7f237"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt from cache at /home/ubuntu/.cache/huggingface/transformers/28e5b750bf4f39cc620367720e105de1501cf36ec4ca7029eba82c1d2cc47caf.6c5b6600e968f4b5e08c86d8891ea99e51537fc2bf251435fb46922e8f7a7b29\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json from cache at /home/ubuntu/.cache/huggingface/transformers/5cbdf121f196be5f1016cb102b197b0c34009e1e658f513515f2eebef9f38093.b33e51591f94f17c238ee9b1fac75b96ff2678cbaed6e108feadb3449d18dc24\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/special_tokens_map.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/47087d99feeb3bc6184d7576ff089c52f7fbe3219fe48c6c4fa681e617753256.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f\n",
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cpu\")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model = model_cls_inj.to(device)\n",
-    "cls_explainer = SequenceClassificationExplainer(model, tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 288
-    },
-    "id": "5-OUMWc4WNv5",
-    "outputId": "bba0c235-9b86-4840-a69d-af8bbfacedee"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.96)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>2.07</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> three                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> morning                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weekend                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> multi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> near                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entrance                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> runs                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> divided                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> high                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tension                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cable                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> guard                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##rail                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> location                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 89                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 65                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> there                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##verse                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weather                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> conditions                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2006                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Je                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ep                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Liberty                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nts                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> three                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> go                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1992                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Mitsubishi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Dia                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mante                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> go                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1996                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pick                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##up                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occupa                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ac                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eration                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inte                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nding                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> merge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> An                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behind                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> switched                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cut                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> avoid                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> changing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Subsequently                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> departed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ac                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eration                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> After                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entered                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rolle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> est                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 6                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turns                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roof                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##3                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> drove                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 45                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Je                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ep                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> over                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> line                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##corre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ct                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> eva                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sive                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> action                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> chose                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ina                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##pp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##rop                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##riate                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> eva                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sive                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> action                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failure                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 40                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> because                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> language                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> barrier                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Korean                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 65%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> transported                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 69%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hospital                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> en                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cro                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##aching                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> adjacent                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> over                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> line                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# true positive\n",
-    "s = tokenizer.decode(dataset_en[\"test\"][144][\"input_ids\"][1:511])\n",
-    "word_attributions = cls_explainer(s, n_steps=20)\n",
-    "cls_explainer.visualize(\"./results/viz_144.html\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 313
-    },
-    "id": "04jn-pgmWNv5",
-    "outputId": "8576416c-2145-4e3a-b265-d40eadf0bf83"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.95)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>2.16</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> und                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ivi                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ded                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> asp                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##halt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> slightly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 64                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 40                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> early                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> conditions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clear                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> flow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> restrictions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2002                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chrysler                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Se                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bring                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> door                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> convert                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ible                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> beginning                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> nego                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##tia                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##te                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> departed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> telephone                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pole                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> located                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roads                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ide                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rota                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clock                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wise                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> then                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> trip                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ped                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> over                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rolle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turns                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> roof                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 69                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> suffered                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> moderate                    </font></mark><mark style=\"background-color: hsl(120, 75%, 69%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injuries                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> has                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> since                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> been                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> put                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> nur                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> home                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> does                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reca                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ll                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> any                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> information                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accident                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accident                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> report                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> records                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> blood                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> content                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 0                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 177                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Associated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> use                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> condition                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> diabetes                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> use                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##scription                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> med                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ication                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> diabetes                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reports                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> also                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> history                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> alcohol                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); 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opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# true positive\n",
-    "s = tokenizer.decode(dataset_en[\"test\"][18][\"input_ids\"][1:511])\n",
-    "word_attributions = cls_explainer(s, n_steps=20)\n",
-    "cls_explainer.visualize(\"./results/viz_18.html\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 288
-    },
-    "id": "3mXRYEluWNv5",
-    "outputId": "c23651ca-7161-4180-efbb-62e1c05c4e88"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_0 (0.99)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_0</b></text></td><td><text style=\"padding-right:2em\"><b>5.34</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> late                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> up                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##hill                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> controls                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 56                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 30                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2007                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Ford                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> e                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cono                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##line                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> van                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> thirty                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 34                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> takes                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> no                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> med                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ication                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> or                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> has                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> any                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> restrictions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1994                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Honda                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Civic                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sedan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> unknown                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> aged                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> According                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> witness                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> high                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rate                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempting                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pass                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> struck                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> fled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> foot                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> leaving                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Both                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scene                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> did                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> know                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speak                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> about                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> his                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> illegal                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> status                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> country                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> motor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> same                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> higher                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 86%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 85%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> one                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> job                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> site                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> another                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ended                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> He                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> residential                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> area                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> observed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> approach                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> his                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mirror                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 78%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> motor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> same                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lower                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> st                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##eady                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 89%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 82%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> e                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> g                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> An                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> associated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> excessive                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mis                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##jud                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gap                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tire                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wrong                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> size                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tire                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> low                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tre                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> depth                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; 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                    line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [PAD]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "ds_test = dataset[\"test\"].map(get_answers, batched=False, fn_kwargs={\"qa_pipeline\": pl, \"questions\": questions})"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# false negative: \"leaving an injured passenger\" overlooked\n",
-    "s = tokenizer.decode(dataset_en[\"test\"][331][\"input_ids\"][1:511])\n",
-    "word_attributions = cls_explainer(s, n_steps=20)\n",
-    "cls_explainer.visualize(\"./results/viz_331.html\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 288
-    },
-    "id": "Gvq1xnu5WNv5",
-    "outputId": "f112c0ea-5e38-4a1b-ce2a-e36910a4b53f"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.95)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>1.85</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> /                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> four                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shoulder                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curve                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ward                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> radius                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> curva                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ture                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 274                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> positive                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 4                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> %                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> grade                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Initially                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> there                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> grass                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> div                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##iding                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ended                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> only                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> double                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> yellow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> line                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> separat                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> directions                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> travel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> A                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sect                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> west                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveled                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> southeast                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Con                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ditions                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dark                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> dry                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> evening                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1987                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Mercury                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Marquis                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bound                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> apparently                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> confused                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 29                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> prior                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intersection                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> went                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> down                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> steep                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 62                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> %                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> striking                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ground                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bottom                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> It                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> just                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shoulder                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 54                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##belt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 53%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> transported                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> medical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facility                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Two                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> adult                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passengers                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 8                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> month                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> safety                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> were                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> also                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> went                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> out                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wrong                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> exit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gas                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> station                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> east                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> few                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hundred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intended                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> circle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> back                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> around                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> enter                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> shopping                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> center                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> located                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> across                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> highway                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> gas                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> station                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> App                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##aren                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##tly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> thought                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> street                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sign                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> identify                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> streets                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> name                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> north                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> intersection                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> opposed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> initiated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 29                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> before                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> inter                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sect                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> began                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> said                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> once                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> started                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> realized                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> error                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> brak                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##e                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wheels                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pave                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> so                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> steep                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> recover                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> In                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##vesti                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gating                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tro                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##oper                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> agree                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> researcher                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> have                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> contributed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> scenario                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> required                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> follow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> up                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vision                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rete                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sting                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> at                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> state                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> license                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> center                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> edge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> recognition                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> error                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> attempted                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> turn                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> too                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> early                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Associated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factors                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> included                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> con                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##versi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ng                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> passenger                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> poor                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##al                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> failure                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> control                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> skill                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ord                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##inar                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ily                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> expected                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> A                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> view                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ob                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##stru                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ction                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> included                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# false positive:\n",
-    "s = tokenizer.decode(dataset_en[\"test\"][78][\"input_ids\"][1:511])\n",
-    "word_attributions = cls_explainer(s, n_steps=20)\n",
-    "cls_explainer.visualize(\"./results/viz_78.html\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 264
     },
-    "id": "GIUENaqY-NRX",
-    "outputId": "1217b5dd-6bb7-40e6-8e7a-b38c2c3a07b5"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (0.65)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>-2.65</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> straight                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> level                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bit                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##umi                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##nou                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> two                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> divided                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> painted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> median                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> posted                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> speed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> limit                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 72                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 45                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> which                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> reduce                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 56                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> km                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> (                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 35                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> mph                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> )                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 100                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> meters                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> site                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sign                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> indicating                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> narrow                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> weather                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> cloud                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##y                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> partially                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wet                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Traffic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> flow                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> normal                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> time                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> day                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> crash                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> occurred                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> week                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##day                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> afternoon                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2002                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Alt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ima                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behind                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1991                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chevrolet                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Lu                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##mina                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> when                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> drove                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> safety                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> zone                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> into                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##coming                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traffic                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> order                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> illegal                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ly                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> pass                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> returned                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> original                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> lane                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> quarter                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> panel                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sp                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##un                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> clock                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wise                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> position                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 180                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> degrees                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> coming                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> after                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> an                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##bank                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ment                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rear                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> off                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> northeast                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##erly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> came                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> final                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> rest                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> facing                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> south                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##erly                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> direction                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> damage                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##2                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##wed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> due                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> its                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> going                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 63%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hospital                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> baby                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Nissan                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Alt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ima                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 38                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> refused                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> interviewed                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> He                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> he                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> did                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> want                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> be                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> both                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ered                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> with                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> t                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Other                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> this                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> traveling                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> entering                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> from                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> side                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> road                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##way                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Reason                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##d                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> as                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> :                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> related                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> factor                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> aggressive                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driving                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> behavior                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Vehicle                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 2                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Chevrolet                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driven                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 21                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> belt                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ed                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 18                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> year                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> male                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> front                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> right                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> who                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> not                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> injured                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> There                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 6                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> month                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> -                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> old                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> female                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> car                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seat                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> second                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> row                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> child                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> was                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> taken                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 86%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> hospital                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> for                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> a                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> check                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> out                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> accompanied                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> by                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> both                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> other                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> people                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> vehicle                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> This                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> relative                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> that                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> had                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> seen                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> driver                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> making                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> wild                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ge                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##stu                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##res                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> \"                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> tail                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##gating                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> She                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> stated                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> she                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> saw                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> V                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##1                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> coming                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> around                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> her                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> on                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> left                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> but                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> could                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> only                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> brak                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##e                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> before                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> impact                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> The                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Critical                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Pre                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##cra                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##sh                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> Event                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> code                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yY5X79_lWNv6"
+      },
+      "source": [
+        "Next, we tokenize the extracted texts and define the labels, and store the dataset for later use:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# false positive:\n",
-    "s = tokenizer.decode(dataset_en[\"test\"][915][\"input_ids\"][1:511])\n",
-    "word_attributions = cls_explainer(s, n_steps=20)\n",
-    "cls_explainer.visualize(\"./results/viz_915.html\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "iBJ99TPuWNv5"
-   },
-   "source": [
-    "<a id='qna'></a>\n",
-    "<a name='qna'></a>\n",
-    "## 6.&nbsp;Using Extractive Question Answering to Process Longer Texts \n",
-    "\n",
-    "In this section we use extractive question answering to extract parts of the accident description which indicate the presence of bodily injury. The aim is to reduce the length of the input texts by extracting only the relevant parts.\n",
-    "\n",
-    "The easiest implementation of extractive question answering is provided by the `pipeline` abstraction.\n",
-    "\n",
-    "We use [`deutsche-telekom/bert-multi-english-german-squad2`](https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2),\n",
-    "a multilingual English German question answering model built on `bert-base-multilingual-cased`. By specifying `device=0` we use GPU support."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000,
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-    "id": "axQK5AJlWNv6",
-    "outputId": "a19e39bf-c1d3-43f4-9675-b1a711f3fb4f"
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-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json not found in cache or force_download set to True, downloading to /home/ubuntu/.cache/huggingface/transformers/tmpr8343lrt\n"
-     ]
-    },
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-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0411f1efb8754e06a7c2e016c99b039e",
-       "version_major": 2,
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+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "V1wQI2RnWNv6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
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+            "e426b72d26fa4196a244b6882c025efc",
+            "f23f875a300a4a3ea7c266830c636f15",
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+          ]
+        },
+        "outputId": "367e2cd7-9d7f-4e16-b835-9188e9ececb1"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/817 [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "387894ba27fe4f7faf45f167413e9b5f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Saving the dataset (0/1 shards):   0%|          | 0/1390 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f23f875a300a4a3ea7c266830c636f15"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ds_test = ds_test.map(tokenize, batched=True, fn_kwargs={\"column\": \"INJ\"})\n",
+        "ds_test = ds_test.rename_column(\"INJSEVB\", \"labels\")\n",
+        "ds_test.save_to_disk(\"./datasets/ds_test\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "storing https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json in cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "creating metadata file for /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "loading configuration file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"deutsche-telekom/bert-multi-english-german-squad2\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertForQuestionAnswering\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"directionality\": \"bidi\",\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 768,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 3072,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"pooler_fc_size\": 768,\n",
-      "  \"pooler_num_attention_heads\": 12,\n",
-      "  \"pooler_num_fc_layers\": 3,\n",
-      "  \"pooler_size_per_head\": 128,\n",
-      "  \"pooler_type\": \"first_token_transform\",\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading configuration file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"deutsche-telekom/bert-multi-english-german-squad2\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertForQuestionAnswering\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"directionality\": \"bidi\",\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 768,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 3072,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"pooler_fc_size\": 768,\n",
-      "  \"pooler_num_attention_heads\": 12,\n",
-      "  \"pooler_num_fc_layers\": 3,\n",
-      "  \"pooler_size_per_head\": 128,\n",
-      "  \"pooler_type\": \"first_token_transform\",\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /home/ubuntu/.cache/huggingface/transformers/tmpm7s4od6g\n"
-     ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "88e7e791e98c4df9b7008ef3b00b9c8e",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lrKxMMqYWNv7"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/676M [00:00<?, ?B/s]"
+      "source": [
+        "We load the transformer model that was trained on the classification task..."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "storing https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/pytorch_model.bin in cache at /home/ubuntu/.cache/huggingface/transformers/5093fe1f6b33244b4044dc6d33d682c1967cf3374acb5031c16c3cf3c38e9a89.11da33732f8611f6f122123b9ec186905e5084656efa0fa47ca5f0f680b7eac8\n",
-      "creating metadata file for /home/ubuntu/.cache/huggingface/transformers/5093fe1f6b33244b4044dc6d33d682c1967cf3374acb5031c16c3cf3c38e9a89.11da33732f8611f6f122123b9ec186905e5084656efa0fa47ca5f0f680b7eac8\n",
-      "loading weights file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/5093fe1f6b33244b4044dc6d33d682c1967cf3374acb5031c16c3cf3c38e9a89.11da33732f8611f6f122123b9ec186905e5084656efa0fa47ca5f0f680b7eac8\n",
-      "All model checkpoint weights were used when initializing BertForQuestionAnswering.\n",
-      "\n",
-      "All the weights of BertForQuestionAnswering were initialized from the model checkpoint at deutsche-telekom/bert-multi-english-german-squad2.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForQuestionAnswering for predictions without further training.\n",
-      "https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/tokenizer_config.json not found in cache or force_download set to True, downloading to /home/ubuntu/.cache/huggingface/transformers/tmpsxzsjt9u\n"
-     ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f8ee48f2f4b94c2bb81c648558f0aa9e",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "nmqqYs_OWNv7"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/264 [00:00<?, ?B/s]"
+      "outputs": [],
+      "source": [
+        "#ds_test = load_from_disk(\"./datasets/ds_test\")\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(\"models/\" + model_name + \"_inj\").to(device)\n",
+        "trainer = Trainer(model)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "storing https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/tokenizer_config.json in cache at /home/ubuntu/.cache/huggingface/transformers/bb76acf9011b1f4e14813c2680af980c4a359b2512e38cd5315f68629e78589a.c60f034cf5bf819518a0170960ddb62b4576fa3d01e9021876b801600cbb6f42\n",
-      "creating metadata file for /home/ubuntu/.cache/huggingface/transformers/bb76acf9011b1f4e14813c2680af980c4a359b2512e38cd5315f68629e78589a.c60f034cf5bf819518a0170960ddb62b4576fa3d01e9021876b801600cbb6f42\n",
-      "loading configuration file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"deutsche-telekom/bert-multi-english-german-squad2\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertForQuestionAnswering\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"directionality\": \"bidi\",\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 768,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 3072,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"pooler_fc_size\": 768,\n",
-      "  \"pooler_num_attention_heads\": 12,\n",
-      "  \"pooler_num_fc_layers\": 3,\n",
-      "  \"pooler_size_per_head\": 128,\n",
-      "  \"pooler_type\": \"first_token_transform\",\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/vocab.txt not found in cache or force_download set to True, downloading to /home/ubuntu/.cache/huggingface/transformers/tmp0ihwpv8x\n"
-     ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "191693164d78457389a6e913edab5de3",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TgJjAASnWNv7"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/972k [00:00<?, ?B/s]"
+      "source": [
+        "...apply it to the tokenized text extracts and evaluate the predictions."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "storing https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/vocab.txt in cache at /home/ubuntu/.cache/huggingface/transformers/93301d199c143a7d7e9b71c94261dc6920fcb9f8c6a1067a9d17f1d77935b8e5.6c5b6600e968f4b5e08c86d8891ea99e51537fc2bf251435fb46922e8f7a7b29\n",
-      "creating metadata file for /home/ubuntu/.cache/huggingface/transformers/93301d199c143a7d7e9b71c94261dc6920fcb9f8c6a1067a9d17f1d77935b8e5.6c5b6600e968f4b5e08c86d8891ea99e51537fc2bf251435fb46922e8f7a7b29\n",
-      "https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/special_tokens_map.json not found in cache or force_download set to True, downloading to /home/ubuntu/.cache/huggingface/transformers/tmpzaaqdek0\n"
-     ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba54641fa7a94ff588ea86ca3f2d94cd",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "c34H0zoOWNv7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 750
+        },
+        "outputId": "e2db142b-d974-45e1-baa2-dbe3a007cf9a"
       },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": []
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Extractive QA\n",
+            "accuracy score = 82.9%,  log loss = 0.456,  Brier loss = 0.265\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.81      0.94      0.87       816\n",
+            "           1       0.88      0.68      0.77       574\n",
+            "\n",
+            "    accuracy                           0.83      1390\n",
+            "   macro avg       0.84      0.81      0.82      1390\n",
+            "weighted avg       0.84      0.83      0.82      1390\n",
+            "\n"
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+            ]
+          },
+          "metadata": {}
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+      ],
+      "source": [
+        "predictions = trainer.predict(ds_test)\n",
+        "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), [\"0\", \"1\"], \"Extractive QA\", \"cm_inj_qa\")"
       ]
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-     "text": [
-      "storing https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/special_tokens_map.json in cache at /home/ubuntu/.cache/huggingface/transformers/5438742f7fe793114a6cb6d1ac46a28b6d8b8b0aa8fd55a8ea8f8ddb70b463c7.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
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-      "loading file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/vocab.txt from cache at /home/ubuntu/.cache/huggingface/transformers/93301d199c143a7d7e9b71c94261dc6920fcb9f8c6a1067a9d17f1d77935b8e5.6c5b6600e968f4b5e08c86d8891ea99e51537fc2bf251435fb46922e8f7a7b29\n",
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-      "loading file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/transformers/5438742f7fe793114a6cb6d1ac46a28b6d8b8b0aa8fd55a8ea8f8ddb70b463c7.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
-      "loading file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/bb76acf9011b1f4e14813c2680af980c4a359b2512e38cd5315f68629e78589a.c60f034cf5bf819518a0170960ddb62b4576fa3d01e9021876b801600cbb6f42\n",
-      "loading configuration file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"deutsche-telekom/bert-multi-english-german-squad2\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertForQuestionAnswering\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"directionality\": \"bidi\",\n",
-      "  \"gradient_checkpointing\": false,\n",
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-      "  \"hidden_dropout_prob\": 0.1,\n",
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-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 3072,\n",
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-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"pooler_fc_size\": 768,\n",
-      "  \"pooler_num_attention_heads\": 12,\n",
-      "  \"pooler_num_fc_layers\": 3,\n",
-      "  \"pooler_size_per_head\": 128,\n",
-      "  \"pooler_type\": \"first_token_transform\",\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading configuration file https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/98815a531e6b412916e105532c140400a6e221e5d249dbc2652fc3bbbc02bb03.063bf511b0ec1ed4ac464b049fce380c9d6f729f38e5413cc3fa45026ec0a0de\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"deutsche-telekom/bert-multi-english-german-squad2\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertForQuestionAnswering\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"directionality\": \"bidi\",\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 768,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 3072,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"pooler_fc_size\": 768,\n",
-      "  \"pooler_num_attention_heads\": 12,\n",
-      "  \"pooler_num_fc_layers\": 3,\n",
-      "  \"pooler_size_per_head\": 128,\n",
-      "  \"pooler_type\": \"first_token_transform\",\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name_qa =\"deutsche-telekom/bert-multi-english-german-squad2\"\n",
-    "pl = pipeline(\"question-answering\", model=model_name_qa, tokenizer=model_name_qa, device=0)\n",
-    "questions = [\"Was someone injured?\", \"Was someone transported?\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "tRdXtynCWNv6"
-   },
-   "source": [
-    "We visit each accident report in turn (the context), and ask the model the two questions “Was someone injured?”\n",
-    "and “Was someone transported?”.\n",
-    "Since the accident reports might provide information on multiple persons,\n",
-    "we allow a maximum of four candidate answers for each of the questions,\n",
-    "which we concatenate into a single (much shorter) new text.\n",
-    "\n",
-    "To achieve this, we write a short function which applies a question answering pipeline to an input text `x`.\n",
-    "The argument `questions` is a list of questions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "id": "Oe8OTkSFWNv6"
-   },
-   "outputs": [],
-   "source": [
-    "def get_answers(x, qa_pipeline, questions):\n",
-    "    x[\"INJ\"] = \"\"\n",
-    "    for question in questions:\n",
-    "        res = qa_pipeline(context=x[\"SUMMARY_EN\"], question=question, top_k=4, handle_impossible_answer=True)\n",
-    "        if isinstance(res, dict):\n",
-    "            res = [res]\n",
-    "        if len(res[0]) > 0:\n",
-    "            x[\"INJ\"] = '. '.join([x[\"INJ\"]] + [item[\"answer\"] for item in res])\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nUfUYHs7WNv6"
-   },
-   "source": [
-    "We apply the question answering function to the entire test set.\n",
-    "\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 6 minutes. If you want to try the concept on only the first 250 samples, you can use `ds_test = dataset[\"test\"].select(range(250).map(...`"
-   ]
-  },
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-   "execution_count": 68,
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-       "model_id": "c16928f2c4344705a9dcdd9c279d4127",
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+      "source": [
+        "The performance is comparable with the logistic regression classifier on mean-pooled encodings of the original texts.\n",
+        "On the other hand, from there is a larger number of false negatives than obtained by task-specific training\n",
+        "and evaluation on the full-length sequence.\n",
+        "This indicates that in some cases the extractive question answering has missed out or suppressed certain relevant parts.\n",
+        "For instance, if the original text reads “The driver was injured.”,\n",
+        "the extract “The driver” is a correct answer to the question “Was someone injured?”;\n",
+        "however, it is too short to detect the presence of an injury from the extract."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:707: VisibleDeprecationWarning:\n",
-      "\n",
-      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "\n",
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/pipelines/question_answering.py:300: VisibleDeprecationWarning:\n",
-      "\n",
-      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "\n",
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/transformers/pipelines/base.py:998: UserWarning:\n",
-      "\n",
-      "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_test = dataset[\"test\"].map(get_answers, batched=False, fn_kwargs={\"qa_pipeline\": pl, \"questions\": questions})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yY5X79_lWNv6"
-   },
-   "source": [
-    "Next, we tokenize the extracted texts and define the labels, and store the dataset for later use:"
-   ]
-  },
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-   "cell_type": "code",
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-       "version_major": 2,
-       "version_minor": 0
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+      "source": [
+        "<a id='conclusion'></a>\n",
+        "<a name='conclusion'></a>\n",
+        "## 7.&nbsp;Conclusion\n",
+        "\n",
+        "Congratulations!\n",
+        "\n",
+        "In this notebook, you have learned how to apply transformer-based models to classification tasks that often arise in actuarial applications.\n",
+        "\n",
+        "You have seen how to address challenges that often arise in practical applications:\n",
+        "\n",
+        "a.\tThe text corpus may be highly domain-specific, i.e., it may use specialized terminology.\n",
+        "    – In [Section 4.1](#domain_finetuning) we have applied domain-specific fine-tuning to improve model performance\n",
+        "    in a specific domain.\n",
+        "\n",
+        "b.\tMultiple languages might be present in parallel.\n",
+        "    – In [Section 3.5](#multi_lingual_training) we have used a multi-lingual transformer model to encode multi-lingual texts\n",
+        "    and to use this output for a classification task. Performance was good even when one language is underrepresented.  \n",
+        "\n",
+        "c.\tText sequences might be short and ambiguous.\n",
+        "    Or they might be so long that it is hard to identify the parts relevant to the task.\n",
+        "    – In this tutorial we have demonstrated two approaches to deal with long texts:\n",
+        "    \n",
+        "   * In [Section 5.2](#investigate) we have split long input texts into slightly overlapping chunks and applied\n",
+        "   the classifier to each chunk separately.\n",
+        "    \n",
+        "   * In [Section 6](#qna) we have used extractive question answering to extract parts of the original texts which are relevant\n",
+        "   to the task.\n",
+        "\n",
+        "d.\tThe amount of training data may be relatively small.\n",
+        "    In particular, gathering large amounts of labelled data (i.e., text sequences augmented with a target label)\n",
+        "    might be expensive.\n",
+        "    – Throughout this workbook, we have used transformer models which have been trained on a large corpus of text data.\n",
+        "    We have applied these models to the specific task with no or little specific training,\n",
+        "    thus transferring the language understanding skills to the task at hand.\n",
+        "\n",
+        "e.\tIt is important to understand why a model arrives at a particular prediction.\n",
+        "    – In [Section 5.3](#interpret) we have shown how to visualize which parts of the input text\n",
+        "    cause the classifier to arrive at a particular prediction.\n",
+        "\n",
+        "The notebook Part II deals with another dataset that has only short text descriptions.\n",
+        "It demonstrates possible approaches in case no or few labels are available."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_test = ds_test.map(tokenize, batched=True, fn_kwargs={\"column\": \"INJ\"})\n",
-    "ds_test = ds_test.rename_column(\"INJSEVB\", \"labels\")\n",
-    "ds_test.save_to_disk(\"./datasets/ds_test\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lrKxMMqYWNv7"
-   },
-   "source": [
-    "We load the transformer model that was trained on the classification task..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "nmqqYs_OWNv7",
-    "outputId": "5b68a0ca-288a-4274-e6aa-4a9910808438"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt from cache at /home/ubuntu/.cache/huggingface/transformers/28e5b750bf4f39cc620367720e105de1501cf36ec4ca7029eba82c1d2cc47caf.6c5b6600e968f4b5e08c86d8891ea99e51537fc2bf251435fb46922e8f7a7b29\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json from cache at /home/ubuntu/.cache/huggingface/transformers/5cbdf121f196be5f1016cb102b197b0c34009e1e658f513515f2eebef9f38093.b33e51591f94f17c238ee9b1fac75b96ff2678cbaed6e108feadb3449d18dc24\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/special_tokens_map.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/47087d99feeb3bc6184d7576ff089c52f7fbe3219fe48c6c4fa681e617753256.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f\n",
-      "loading configuration file https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/cf37a9dc282a679f121734d06f003625d14cfdaf55c14358c4c0b8e7e2b89ac9.7a727bd85e40715bec919a39cdd6f0aba27a8cd488f2d4e0f512448dcd02bf0f\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading configuration file models/distilbert-base-multilingual-cased_inj/config.json\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"models/distilbert-base-multilingual-cased_inj\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForSequenceClassification\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"output_past\": true,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"problem_type\": \"single_label_classification\",\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"torch_dtype\": \"float32\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 119547\n",
-      "}\n",
-      "\n",
-      "loading weights file models/distilbert-base-multilingual-cased_inj/pytorch_model.bin\n",
-      "All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n",
-      "\n",
-      "All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at models/distilbert-base-multilingual-cased_inj.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n",
-      "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
-     ]
     }
-   ],
-   "source": [
-    "#ds_test = load_from_disk(\"./datasets/ds_test\")\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(\"models/\" + model_name + \"_inj\").to(device)\n",
-    "trainer = Trainer(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TgJjAASnWNv7"
-   },
-   "source": [
-    "...apply it to the tokenized text extracts and evaluate the predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
+  ],
+  "metadata": {
+    "accelerator": "GPU",
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 860
-    },
-    "id": "c34H0zoOWNv7",
-    "outputId": "1dca8821-ecf6-4852-e38f-734f94231dfc"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: SUMMARY_MX, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, INJ, WEATHER5, WEATHER1, WEATHER6. If SUMMARY_MX, words per case summary, WEATHER7, WEATHER2, WEATHER4, NUMTOTV, SUMMARY_GE, WEATHER8, INJSEVA, SCASEID, SUMMARY_EN, index, WEATHER3, level_0, INJ, WEATHER5, WEATHER1, WEATHER6 are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1390\n",
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-      "Extractive QA\n",
-      "accuracy score = 85.5%,  log loss = 0.408,  Brier loss = 0.243\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.85      0.91      0.88       816\n",
-      "           1       0.85      0.78      0.82       574\n",
-      "\n",
-      "    accuracy                           0.85      1390\n",
-      "   macro avg       0.85      0.84      0.85      1390\n",
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-    "predictions = trainer.predict(ds_test)\n",
-    "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), [\"0\", \"1\"], \"Extractive QA\", \"cm_inj_qa\")"
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-    "The performance is comparable with the logistic regression classifier on mean-pooled encodings of the original texts.\n",
-    "On the other hand, from there is a larger number of false negatives than obtained by task-specific training\n",
-    "and evaluation on the full-length sequence.\n",
-    "This indicates that in some cases the extractive question answering has missed out or suppressed certain relevant parts.\n",
-    "For instance, if the original text reads “The driver was injured.”,\n",
-    "the extract “The driver” is a correct answer to the question “Was someone injured?”;\n",
-    "however, it is too short to detect the presence of an injury from the extract."
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-    "<a id='conclusion'></a>\n",
-    "<a name='conclusion'></a>\n",
-    "## 7.&nbsp;Conclusion \n",
-    "\n",
-    "Congratulations!\n",
-    "\n",
-    "In this notebook, you have learned how to apply transformer-based models to classification tasks that often arise in actuarial applications.\n",
-    "\n",
-    "You have seen how to address challenges that often arise in practical applications:\n",
-    "\n",
-    "a.\tThe text corpus may be highly domain-specific, i.e., it may use specialized terminology.\n",
-    "    – In [Section 4.1](#domain_finetuning) we have applied domain-specific fine-tuning to improve model performance\n",
-    "    in a specific domain.\n",
-    "\n",
-    "b.\tMultiple languages might be present in parallel.\n",
-    "    – In [Section 3.5](#multi_lingual_training) we have used a multi-lingual transformer model to encode multi-lingual texts \n",
-    "    and to use this output for a classification task. Performance was good even when one language is underrepresented.  \n",
-    "\n",
-    "c.\tText sequences might be short and ambiguous.\n",
-    "    Or they might be so long that it is hard to identify the parts relevant to the task.\n",
-    "    – In this tutorial we have demonstrated two approaches to deal with long texts:\n",
-    "    \n",
-    "   * In [Section 5.2](#investigate) we have split long input texts into slightly overlapping chunks and applied\n",
-    "   the classifier to each chunk separately.\n",
-    "    \n",
-    "   * In [Section 6](#qna) we have used extractive question answering to extract parts of the original texts which are relevant\n",
-    "   to the task. \n",
-    "\n",
-    "d.\tThe amount of training data may be relatively small.\n",
-    "    In particular, gathering large amounts of labelled data (i.e., text sequences augmented with a target label)\n",
-    "    might be expensive.\n",
-    "    – Throughout this workbook, we have used transformer models which have been trained on a large corpus of text data.\n",
-    "    We have applied these models to the specific task with no or little specific training,\n",
-    "    thus transferring the language understanding skills to the task at hand.\n",
-    "\n",
-    "e.\tIt is important to understand why a model arrives at a particular prediction.\n",
-    "    – In [Section 5.3](#interpret) we have shown how to visualize which parts of the input text\n",
-    "    cause the classifier to arrive at a particular prediction.\n",
-    "\n",
-    "The notebook Part II deals with another dataset that has only short text descriptions.\n",
-    "It demonstrates possible approaches in case no or few labels are available. "
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diff --git a/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_2.ipynb b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_2.ipynb
index 4ccbd29..4301c54 100644
--- a/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_2.ipynb	
+++ b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_2.ipynb	
@@ -1,52274 +1,22699 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kmbK3sewEVGy"
-   },
-   "source": [
-    "# Actuarial Applications of Natural Language Processing Using Transformers\n",
-    "### A Case Study for Processing Text Features in an Actuarial Context\n",
-    "### Part II – Case Studies on Property Insurance Claim Descriptions - Unsupervised Techniques\n",
-    "\n",
-    "By Andreas Troxler, June 2022\n",
-    "\n",
-    "In this Part II of the tutorial, you will learn techniques that can be applied in situations with few or no labels.\n",
-    "This is very relevant in practice: text data is often available, but labels are missing or sparse! \n",
-    "\n",
-    "Let’s get started."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EnmTW4uhEVG3"
-   },
-   "source": [
-    "## Notebook Overview\n",
-    "\n",
-    "This notebook is divided into tutorial is divided into six parts; they are:\n",
-    "\n",
-    "1. [Introduction.](#intro)<br>\n",
-    "    We begin by explaining pre-requisites. Then we turn to loading and exploring the dataset – ca. 6k records of short property insurance claim description which we aim to classify by peril type.<br><br>\n",
-    "\n",
-    "2. [Classify by peril type in a supervised setting.](#supervised)<br>\n",
-    "     To warm up, we apply supervised learning techniques you have learned in Part I to the dataset of this Part II.<br><br>\n",
-    "\n",
-    "3. [Zero-shot classification.](#zero_shot)<br>\n",
-    "    This technique assigns each text sample to one element of a pre-defined list of candidate expressions. This allows classification without any task-specific training and without using the labels. This fully unsupervised approach is useful in situations with no labels.<br><br>\n",
-    "\n",
-    "4. [Unsupervised classification using similarity.](#similarity)<br>\n",
-    "    This technique encodes each input sentence and each candidate expression into en embedding vector. Then, pairwise similarity scores between each input sequence and each candiate expression are calculated. The candidate expression with the highest similarity score is selected. This fully unsupervised approach is useful in situations with no labels.<br><br>\n",
-    "   \n",
-    "5. [Unsupervised topic modeling by clustering of document embeddings.](#topic_modeling)<br>\n",
-    "    This approach extracts clusters of similar text samples and proposes verbal representations of these clusters. The labels are not required, but may be used in the process if available. This technique does not require prior knowledge of candidate expressions.<br><br>\n",
-    "    \n",
-    "6. [Conclusion](#conclusion)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hcc6Je4lEVG4"
-   },
-   "source": [
-    "<a id='intro'></a>\n",
-    "<a name=\"intro\"></a>\n",
-    "## 1.&nbsp;Introduction\n",
-    "\n",
-    "In this section we discuss the pre-requisites, load and inspect the dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "iEu2UBDEEVG4"
-   },
-   "source": [
-    "<a id='prerequisites'></a>\n",
-    "<a name='prerequisites'></a>\n",
-    "### 1.1. Prerequisites\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2mGZv-UxvKO_"
-   },
-   "source": [
-    "#### Computing Power\n",
-    "\n",
-    "This notebook is computationally intensive. We recommend using a platform with GPU support.\n",
-    "\n",
-    "We have run this notebook on Google Colab and on an Amazon EC2 p2.xlarge instance (an older generation of GPU-based instances).\n",
-    "\n",
-    "Please note that the results may not be reproducible across platforms and versions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qXPfoNIUIpuv"
-   },
-   "source": [
-    "#### Local files\n",
-    "Make sure the following files are available in the directory of the notebook:\n",
-    "* `tutorial_utils.py` - a collection of utility functions used throughout this notebook\n",
-    "* `peril.training.csv` - the training data\n",
-    "* `peril.validation.csv` - the validation data\n",
-    "\n",
-    "This notebook will create the following subdirectories:\n",
-    "* `models` - trained Transformer models\n",
-    "* `results` - figures and Excel files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3ahENX7-EVG5"
-   },
-   "source": [
-    "#### Getting started with Python and Jupyter Notebook\n",
-    "\n",
-    "For this tutorial, we assume that you are already familiar with Python and Jupyter Notebook.\n",
-    "We also assume that you have worked through Part I of this tutorial.\n",
-    "\n",
-    "In this section, Jupyter Notebook and Python settings are initialized.\n",
-    "For code in Python, the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/)\n",
-    "(\"PEP = Python Enhancement Proposal\") is enforced with minor variations to improve readability.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kmbK3sewEVGy"
+      },
+      "source": [
+        "# Actuarial Applications of Natural Language Processing Using Transformers\n",
+        "### A Case Study for Processing Text Features in an Actuarial Context\n",
+        "### Part II – Case Studies on Property Insurance Claim Descriptions - Unsupervised Techniques\n",
+        "\n",
+        "By Andreas Troxler, June 2022\n",
+        "\n",
+        "In this Part II of the tutorial, you will learn techniques that can be applied in situations with few or no labels.\n",
+        "This is very relevant in practice: text data is often available, but labels are missing or sparse!\n",
+        "\n",
+        "Let’s get started."
+      ]
     },
-    "id": "1wK6e7a5EVG5",
-    "outputId": "96247e63-3acb-43bf-9371-05765dcc2024",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>.container { width:100% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EnmTW4uhEVG3"
+      },
+      "source": [
+        "## Notebook Overview\n",
+        "\n",
+        "This notebook is divided into tutorial is divided into six parts; they are:\n",
+        "\n",
+        "1. [Introduction.](#intro)<br>\n",
+        "    We begin by explaining pre-requisites. Then we turn to loading and exploring the dataset – ca. 6k records of short property insurance claim description which we aim to classify by peril type.<br><br>\n",
+        "\n",
+        "2. [Classify by peril type in a supervised setting.](#supervised)<br>\n",
+        "     To warm up, we apply supervised learning techniques you have learned in Part I to the dataset of this Part II.<br><br>\n",
+        "\n",
+        "3. [Zero-shot classification.](#zero_shot)<br>\n",
+        "    This technique assigns each text sample to one element of a pre-defined list of candidate expressions. This allows classification without any task-specific training and without using the labels. This fully unsupervised approach is useful in situations with no labels.<br><br>\n",
+        "\n",
+        "4. [Unsupervised classification using similarity.](#similarity)<br>\n",
+        "    This technique encodes each input sentence and each candidate expression into en embedding vector. Then, pairwise similarity scores between each input sequence and each candiate expression are calculated. The candidate expression with the highest similarity score is selected. This fully unsupervised approach is useful in situations with no labels.<br><br>\n",
+        "   \n",
+        "5. [Unsupervised topic modeling by clustering of document embeddings.](#topic_modeling)<br>\n",
+        "    This approach extracts clusters of similar text samples and proposes verbal representations of these clusters. The labels are not required, but may be used in the process if available. This technique does not require prior knowledge of candidate expressions.<br><br>\n",
+        "    \n",
+        "6. [Conclusion](#conclusion)\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Notebook settings\n",
-    "\n",
-    "# clear the namespace variables\n",
-    "from IPython import get_ipython\n",
-    "get_ipython().run_line_magic(\"reset\", \"-sf\")\n",
-    "\n",
-    "# formatting: cell width\n",
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5V9gFHqyEVG7"
-   },
-   "source": [
-    "#### Importing Required Libraries\n",
-    "\n",
-    "If you run this notebook on Google Colab, you will need to install the following libraries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "Mxggg0WmFDuy",
-    "outputId": "b6ee319a-540d-400e-c889-f27245d894c9"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcc6Je4lEVG4"
+      },
+      "source": [
+        "<a id='intro'></a>\n",
+        "<a name=\"intro\"></a>\n",
+        "## 1.&nbsp;Introduction\n",
+        "\n",
+        "In this section we discuss the pre-requisites, load and inspect the dataset."
+      ]
     },
-    "id": "lSUQsZPlFeBu",
-    "outputId": "9828bf8d-6e1b-4b5f-bbe3-e5297cf85939"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iEu2UBDEEVG4"
+      },
+      "source": [
+        "<a id='prerequisites'></a>\n",
+        "<a name='prerequisites'></a>\n",
+        "### 1.1. Prerequisites\n"
+      ]
     },
-    "id": "1BEZPSNtGwzp",
-    "outputId": "0e0f193a-b652-4cfd-fada-1f6c7f941a96"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install plotly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2mGZv-UxvKO_"
+      },
+      "source": [
+        "#### Computing Power\n",
+        "\n",
+        "This notebook is computationally intensive. We recommend using a platform with GPU support.\n",
+        "\n",
+        "We have run this notebook on Google Colab and on an Amazon EC2 p2.xlarge instance (an older generation of GPU-based instances).\n",
+        "\n",
+        "Please note that the results may not be reproducible across platforms and versions."
+      ]
     },
-    "id": "ItEs2TIcR8fz",
-    "outputId": "08fe2b77-d4ba-4193-91ce-cd81a9b3d62e"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install kaleido"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qXPfoNIUIpuv"
+      },
+      "source": [
+        "#### Local files\n",
+        "Make sure the following files are available in the directory of the notebook:\n",
+        "* `tutorial_utils.py` - a collection of utility functions used throughout this notebook\n",
+        "* `peril.training.csv` - the training data\n",
+        "* `peril.validation.csv` - the validation data\n",
+        "\n",
+        "This notebook will create the following subdirectories:\n",
+        "* `models` - trained Transformer models\n",
+        "* `results` - figures and Excel files"
+      ]
     },
-    "id": "iflvB6DtHIHj",
-    "outputId": "3476d786-2932-4412-cc46-630ddfad4e41"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install pyyaml==5.4.1  ## https://github.com/yaml/pyyaml/issues/576"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3ahENX7-EVG5"
+      },
+      "source": [
+        "#### Getting started with Python and Jupyter Notebook\n",
+        "\n",
+        "For this tutorial, we assume that you are already familiar with Python and Jupyter Notebook.\n",
+        "We also assume that you have worked through Part I of this tutorial.\n",
+        "\n",
+        "In this section, Jupyter Notebook and Python settings are initialized.\n",
+        "For code in Python, the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/)\n",
+        "(\"PEP = Python Enhancement Proposal\") is enforced with minor variations to improve readability.\n"
+      ]
     },
-    "id": "zt3VhIayHUvh",
-    "outputId": "09678be9-24b9-4e53-d160-ddc2b22e9809"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install bertopic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "7b2yEtZjEVG8"
-   },
-   "source": [
-    "and loaded:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "LuwY5ubtEVG9",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from collections import OrderedDict\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy.special import softmax\n",
-    "from datasets import Dataset, DatasetDict\n",
-    "from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, trainer_utils, AutoModelForSequenceClassification\n",
-    "from transformers import pipeline\n",
-    "import torch\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import plotly.express as px\n",
-    "from wordcloud import WordCloud\n",
-    "from bertopic import BERTopic\n",
-    "from umap import UMAP\n",
-    "from hdbscan import HDBSCAN\n",
-    "from tutorial_utils import extract_sequence_encoding, get_xy, dummy_classifier, logistic_regression_classifier, evaluate_classifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "44WiOM3SEVG-"
-   },
-   "source": [
-    "<a id='dataloading'></a>\n",
-    "<a name='dataloading'></a>\n",
-    "### 1.2. Loading the Data\n",
-    "\n",
-    "The dataset used throughout this tutorial concerns property insurance claims\n",
-    "of the Wisconsin Local Government Property Insurance Fund (LPGIF),\n",
-    "made available in the open text project of [Frees](https://ewfrees.github.io/Loss-Data-Analytics/).\n",
-    "The Wisconsin LGPIF is an insurance pool managed by the Wisconsin Office of the Insurance Commissioner.\n",
-    "This fund provides insurance protection to local governmental institutions such as counties, schools,\n",
-    "libraries, airports, etc.\n",
-    "It insures property claims at buildings and motor vehicles, and it excludes certain natural and man-made perils like\n",
-    "flood, earthquakes or nuclear accidents.\n",
-    "\n",
-    "The data consists of 6’030 records (4’991 in the training set, 1’039 in the test set)\n",
-    "which include a claim amount, a short English claim description and a hazard type with 9 different levels:\n",
-    "Fire, Lightning, Hail, Wind, WaterW (weather related water claims), WaterNW (other weather claims), Vehicle,\n",
-    "Vandalism and Misc (any other).\n",
-    "\n",
-    "The training and validation set are available in separate csv files, which we load into Pandas DataFrames,\n",
-    "create a single column containing the label, and finally create a `dataset`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "1wK6e7a5EVG5",
+        "outputId": "c254f1de-a7fb-4412-f285-73dcf85eda4f",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<style>.container { width:100% !important; }</style>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Notebook settings\n",
+        "\n",
+        "# clear the namespace variables\n",
+        "from IPython import get_ipython\n",
+        "get_ipython().run_line_magic(\"reset\", \"-sf\")\n",
+        "\n",
+        "# formatting: cell width\n",
+        "from IPython.display import display, HTML\n",
+        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+      ]
     },
-    "id": "DogoPiqXEVG-",
-    "outputId": "997accba-fc26-4de7-f7d3-d7d582edd3b4",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['Vandalism', 'Fire', 'Lightning', 'Wind', 'Hail', 'Vehicle', 'WaterNW', 'WaterW', 'Misc', 'Loss', 'Description', 'labels'],\n",
-      "        num_rows: 4991\n",
-      "    })\n",
-      "    test: Dataset({\n",
-      "        features: ['Vandalism', 'Fire', 'Lightning', 'Wind', 'Hail', 'Vehicle', 'WaterNW', 'WaterW', 'Misc', 'Loss', 'Description', 'labels'],\n",
-      "        num_rows: 1039\n",
-      "    })\n",
-      "})\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load data\n",
-    "df_train = pd.read_csv(\"peril.training.csv\")\n",
-    "df_valid = pd.read_csv(\"peril.validation.csv\")\n",
-    "\n",
-    "# extract label texts and create column \"labels\" which encodes the peril\n",
-    "labels = df_train.columns[:9].to_list()\n",
-    "df_train[\"labels\"] = np.matmul(df_train.iloc[:, :9].values, np.array(range(9),).reshape((9,1)))\n",
-    "df_valid[\"labels\"] = np.matmul(df_valid.iloc[:, :9].values, np.array(range(9),).reshape((9,1)))\n",
-    "\n",
-    "# create dataset\n",
-    "ds = DatasetDict({\"train\": Dataset.from_pandas(df_train), \"test\": Dataset.from_pandas(df_valid)})\n",
-    "\n",
-    "print(f\"{ds}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ilcy8gKCEVG_"
-   },
-   "source": [
-    "<a id='dataexploration'></a>\n",
-    "<a name='dataexploration'></a>\n",
-    "### 1.3 Exploring the data\n",
-    "\n",
-    "The first records of the training dataset look like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5V9gFHqyEVG7"
+      },
+      "source": [
+        "#### Importing Required Libraries\n",
+        "\n",
+        "If you run this notebook on Google Colab, you will need to install the following libraries:"
+      ]
     },
-    "id": "de1ksaP-EVG_",
-    "outputId": "539bbca5-c64d-47a7-cb02-57927c7a41fb",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Vandalism</th>\n",
-       "      <th>Fire</th>\n",
-       "      <th>Lightning</th>\n",
-       "      <th>Wind</th>\n",
-       "      <th>Hail</th>\n",
-       "      <th>Vehicle</th>\n",
-       "      <th>WaterNW</th>\n",
-       "      <th>WaterW</th>\n",
-       "      <th>Misc</th>\n",
-       "      <th>Loss</th>\n",
-       "      <th>Description</th>\n",
-       "      <th>labels</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6838.87</td>\n",
-       "      <td>lightning damage                              ...</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2085.00</td>\n",
-       "      <td>lightning damage at Comm. Center              ...</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11335.00</td>\n",
-       "      <td>lightning damage at water tower               ...</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1480.00</td>\n",
-       "      <td>lightning damge to radio tower                ...</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>600.00</td>\n",
-       "      <td>vandalism damage at recycle center            ...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "Mxggg0WmFDuy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aa32bc1b-eee6-4e61-db0a-1766f975ef36"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
+            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
+            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting multiprocess (from datasets)\n",
+            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
+            "Collecting huggingface-hub<1.0.0,>=0.14.0 (from datasets)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
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+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
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+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
+            "Successfully installed datasets-2.14.4 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 xxhash-3.3.0\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   Vandalism  Fire  Lightning  Wind  Hail  Vehicle  WaterNW  WaterW  Misc  \\\n",
-       "0          0     0          1     0     0        0        0       0     0   \n",
-       "1          0     0          1     0     0        0        0       0     0   \n",
-       "2          0     0          1     0     0        0        0       0     0   \n",
-       "3          0     0          1     0     0        0        0       0     0   \n",
-       "4          1     0          0     0     0        0        0       0     0   \n",
-       "\n",
-       "       Loss                                        Description  labels  \n",
-       "0   6838.87  lightning damage                              ...       2  \n",
-       "1   2085.00  lightning damage at Comm. Center              ...       2  \n",
-       "2  11335.00  lightning damage at water tower               ...       2  \n",
-       "3   1480.00  lightning damge to radio tower                ...       2  \n",
-       "4    600.00  vandalism damage at recycle center            ...       0  "
+      "source": [
+        "!pip install datasets"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Axos90P1EVG_"
-   },
-   "source": [
-    "Let's look at the distribution of peril types in the training and validation set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
     },
-    "id": "WwAKZ-KYEVHA",
-    "outputId": "fba350dd-8c84-4324-bce9-b75a05ef6d4c",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>peril</th>\n",
-       "      <th>train</th>\n",
-       "      <th>valid</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Vandalism</td>\n",
-       "      <td>1774</td>\n",
-       "      <td>310</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Fire</td>\n",
-       "      <td>171</td>\n",
-       "      <td>46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Lightning</td>\n",
-       "      <td>832</td>\n",
-       "      <td>123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Wind</td>\n",
-       "      <td>296</td>\n",
-       "      <td>107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Hail</td>\n",
-       "      <td>76</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Vehicle</td>\n",
-       "      <td>852</td>\n",
-       "      <td>227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>WaterNW</td>\n",
-       "      <td>202</td>\n",
-       "      <td>67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>WaterW</td>\n",
-       "      <td>426</td>\n",
-       "      <td>38</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Misc</td>\n",
-       "      <td>362</td>\n",
-       "      <td>103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Total</td>\n",
-       "      <td>4991</td>\n",
-       "      <td>1039</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "lSUQsZPlFeBu",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5b259df6-c0a7-42b1-9da2-c8ae97386690"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting transformers[torch]\n",
+            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.12.2)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.16.4)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
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+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2023.6.3)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
+            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[torch])\n",
+            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m64.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[torch])\n",
+            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.1)\n",
+            "Requirement already satisfied: torch!=1.12.0,>=1.9 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.0.1+cu118)\n",
+            "Collecting accelerate>=0.20.3 (from transformers[torch])\n",
+            "  Downloading accelerate-0.22.0-py3-none-any.whl (251 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m32.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->transformers[torch]) (5.9.5)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers[torch]) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers[torch]) (4.7.1)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (3.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (3.1.2)\n",
+            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (2.0.0)\n",
+            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch!=1.12.0,>=1.9->transformers[torch]) (3.27.2)\n",
+            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch!=1.12.0,>=1.9->transformers[torch]) (16.0.6)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.2.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2023.7.22)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.9->transformers[torch]) (2.1.3)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.9->transformers[torch]) (1.3.0)\n",
+            "Installing collected packages: tokenizers, safetensors, transformers, accelerate\n",
+            "Successfully installed accelerate-0.22.0 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.32.1\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "       peril  train  valid\n",
-       "0  Vandalism   1774    310\n",
-       "1       Fire    171     46\n",
-       "2  Lightning    832    123\n",
-       "3       Wind    296    107\n",
-       "4       Hail     76     18\n",
-       "5    Vehicle    852    227\n",
-       "6    WaterNW    202     67\n",
-       "7     WaterW    426     38\n",
-       "8       Misc    362    103\n",
-       "9      Total   4991   1039"
+      "source": [
+        "!pip install transformers[torch]"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats = pd.DataFrame({\n",
-    "    \"peril\": df_train.columns.values[:-3],\n",
-    "    \"train\": df_train.groupby(\"labels\")[\"labels\"].count().values,\n",
-    "    \"valid\": df_valid.groupby(\"labels\")[\"labels\"].count().values\n",
-    "})\n",
-    "summary = pd.DataFrame({\"peril\": [\"Total\"], \"train\": [stats[\"train\"].sum()], \"valid\": [stats[\"valid\"].sum()]})\n",
-    "stats = pd.concat([stats, summary], ignore_index=True)\n",
-    "stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
     },
-    "id": "FKfk4QAoEVHA",
-    "outputId": "413a754f-0ae9-4040-df0d-2affba6f66f6",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
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r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var 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a=V(s,i);return[r/n*(t+e/o(a)),a]},a}function Mt(t){return[t[0]/2,S(g(t[1]/2*M))*A]}function St(t){return[2*t[0],2*i(m(t[1]*M))*A]}function Et(t,r){var i=2*y/r,s=t*t;function l(r,l){var c=e.geoAzimuthalEquidistantRaw(r,l),u=c[0],f=c[1],h=u*u+f*f;if(h>s){var d=L(h),g=a(f,u),b=i*p(g/i),_=g-b,w=t*o(_),T=(t*m(_)-_*m(w))/(x-w),k=Lt(_,T),A=(y-t)/Ct(k,w,y);u=d;var M,S=50;do{u-=M=(t+Ct(k,w,u)*A-d)/(k(u)*A)}while(n(M)>v&&--S>0);f=_*m(u),u<x&&(f-=T*(u-x));var E=m(b),C=o(b);c[0]=u*C-f*E,c[1]=u*E+f*C}return c}return l.invert=function(r,l){var c=r*r+l*l;if(c>s){var u=L(c),f=a(l,r),h=i*p(f/i),d=f-h;r=u*o(d),l=u*m(d);for(var g=r-x,v=m(r),b=l/v,_=r<x?1/0:0,w=10;;){var T=t*m(b),k=t*o(b),A=m(k),M=x-k,S=(T-b*A)/M,E=Lt(b,S);if(n(_)<1e-12||!--w)break;b-=_=(b*v-S*g-l)/(v-2*g*(M*(k+b*T*o(k)-A)-T*(T-b*A))/(M*M))}r=(u=t+Ct(E,k,r)*(y-t)/Ct(E,k,y))*o(f=h+b),l=u*m(f)}return e.geoAzimuthalEquidistantRaw.invert(r,l)},l}function Lt(t,e){return function(r){var n=t*o(r);return r<x&&(n-=e),L(1+n*n)}}function 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":81,\"color-normalize\":89,\"css-font\":99,\"detect-kerning\":125,\"es6-weak-map\":183,\"flatten-vertex-data\":191,\"font-atlas\":192,\"font-measure\":193,\"gl-util/context\":226,\"is-plain-obj\":236,\"object-assign\":247,\"parse-rect\":249,\"parse-unit\":251,\"pick-by-alias\":253,regl:283,\"to-px\":314,\"typedarray-pool\":327}],226:[function(t,e,r){(function(r){(function(){\"use strict\";var n=t(\"pick-by-alias\");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new 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e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",m),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",m),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=(new Error).stack||\"\",this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var m=s.columns,g=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/m,w=y/m;s.passes=[];for(var T=0;T<m;T++)for(var k=0;k<m;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:m},y:{buffer:s.buffer,offset:T,count:g,stride:m}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":346,\"object-assign\":247}],337:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":346,\"object-assign\":247}],338:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":346,\"object-assign\":247}],341:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":346,\"object-assign\":247}],344:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":346,\"object-assign\":247}],345:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var 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r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),m=t(\"../titles\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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r=n.select(this);c.ensureSingle(r,\"rect\",A.cbbg),c.ensureSingle(r,\"g\",A.cbfills),c.ensureSingle(r,\"g\",A.cblines),c.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),c.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),c.ensureSingle(r,\"rect\",A.cboutline);var v=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,v=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,I=e.ypad,O=e.x,z=o?e.y:1-e.y,D=r._fullLayout,R=D._size,F=e._fillcolor,B=e._line,N=e.title,j=N.side,U=e._zrange||n.extent((\"function\"==typeof F?F:B.color).domain()),V=\"function\"==typeof B.color?B.color:function(){return B.color},H=\"function\"==typeof F?F:function(){return F},q=e._levels,G=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,c=a.size,u=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*c,!(c>0?n>=l:n<=l));i++)n>u&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,c=h.size,i=0;i<1e5&&(n=h.start+i*c,!(c>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);a.size<0&&(o.reverse(),s.reverse());return{line:o,fill:s}}(0,e,U),Y=G.fill,W=G.line,X=Math.round(v*(\"fraction\"===_?o?R.w:R.h:1)),Z=X/(o?R.w:R.h),J=Math.round(l*(\"fraction\"===h?o?R.h:R.w:1)),K=J/(o?R.h:R.w),Q=Math.round(o?O*R.w+P:z*R.h+I),$={center:.5,right:1}[L]||0,tt={top:1,middle:.5}[C]||0,et=o?O-$*Z:z-tt*Z,rt=o?z-tt*K:O-$*K,nt=Math.round(o?R.h*(1-rt):R.w*rt);e._lenFrac=K,e._thickFrac=Z,e._uFrac=et,e._vFrac=rt;var it=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),mt=0,gt=0;function vt(n,i){var a={propContainer:it,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:D._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),m.draw(r,n,f(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&at||!o&&!at)&&(\"top\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt-K)+3+.75*pt),\"bottom\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt)-3-.25*pt),\"right\"===j&&(e=I+R.t+R.h*z+3+.75*pt,t=P+R.l+R.w*rt),vt(it._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!at||o&&at){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+it._id+\"title-math-group\").node(),m=15.6;if(f.node()&&(m=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),gt=a.width,(mt=a.height)>m&&(h[1]-=(mt-m)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),gt=a.width,mt=a.height),o){if(mt){if(mt+=5,\"top\"===j)it.domain[1]-=mt/R.h,h[1]*=-1;else{it.domain[0]+=mt/R.h;var v=g.lineCount(f);h[1]+=(1-v)*m}l.attr(\"transform\",u(h[0],h[1])),it.setScale()}}else gt&&(\"right\"===j&&(it.domain[0]+=(gt+pt/2)/R.w),l.attr(\"transform\",u(h[0],h[1])),it.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?u(0,Math.round(R.h*(1-it.domain[1]))):u(Math.round(R.w*it.domain[0]),0)),dt.attr(\"transform\",o?u(0,Math.round(-R.t)):u(Math.round(-R.l),0));var y=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(Y);y.enter().append(\"rect\").classed(A.cbfill,!0).style(\"stroke\",\"none\"),y.exit().remove();var x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,m=!o&&\"right\"===j,g=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,g=n.height)),m&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,g+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=g;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+g-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=m?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?mt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+mt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-g))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var Y={},W=T[L],Z=k[L],K=T[C],et=k[C],rt=b-X;o?(\"pixels\"===h?(Y.y=z,Y.t=J*K,Y.b=J*et):(Y.t=Y.b=0,Y.yt=z+l*K,Y.yb=z-l*et),\"pixels\"===_?(Y.x=O,Y.l=b*W,Y.r=b*Z):(Y.l=rt*W,Y.r=rt*Z,Y.xl=O-v*W,Y.xr=O+v*Z)):(\"pixels\"===h?(Y.x=O,Y.l=J*W,Y.r=J*Z):(Y.l=Y.r=0,Y.xl=O+l*W,Y.xr=O-l*Z),\"pixels\"===_?(Y.y=1-z,Y.t=b*K,Y.b=b*et):(Y.t=rt*K,Y.b=rt*et,Y.yt=z-v*K,Y.yb=z+v*et)),a.autoMargin(r,e._id,Y)}],r)}(r,e,t);v&&v.then&&(t._promises||[]).push(v),t._context.edits.colorbarPosition&&function(t,e,r){var n,i,a,s=\"v\"===e.orientation,c=r._fullLayout._size;l.init({element:t.node(),gd:r,prepFn:function(){n=t.attr(\"transform\"),h(t)},moveFn:function(r,o){t.attr(\"transform\",n+u(r,o)),i=l.align((s?e._uFrac:e._vFrac)+r/c.w,s?e._thickFrac:e._lenFrac,0,1,e.xanchor),a=l.align((s?e._vFrac:1-e._uFrac)-o/c.h,s?e._lenFrac:e._thickFrac,0,1,e.yanchor);var f=l.getCursor(i,a,e.xanchor,e.yanchor);h(t,f)},doneFn:function(){if(h(t),void 0!==i&&void 0!==a){var n={};n[e._propPrefix+\"x\"]=i,n[e._propPrefix+\"y\"]=a,void 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var m=d[0];i(m.x0)&&i(m.y0)&&(h=ht(m),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=mt;var gt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};gt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),m=t.trace;if(g.traceIs(m,\"gl3d\")){var v=i._fullLayout[m.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(g,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),m=d.LINE_SPACING,g=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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rectangle\")},attr:\"dragmode\",val:\"drawrect\",icon:o.drawrect,click:f},u.drawcircle={name:\"drawcircle\",title:function(t){return c(t,\"Draw circle\")},attr:\"dragmode\",val:\"drawcircle\",icon:o.drawcircle,click:f},u.eraseshape={name:\"eraseshape\",title:function(t){return c(t,\"Erase active shape\")},icon:o.eraseshape,click:s},u.zoomIn2d={name:\"zoomIn2d\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:f},u.zoomOut2d={name:\"zoomOut2d\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:f},u.autoScale2d={name:\"autoScale2d\",_cat:\"autoscale\",title:function(t){return c(t,\"Autoscale\")},attr:\"zoom\",val:\"auto\",icon:o.autoscale,click:f},u.resetScale2d={name:\"resetScale2d\",_cat:\"resetscale\",title:function(t){return c(t,\"Reset axes\")},attr:\"zoom\",val:\"reset\",icon:o.home,click:f},u.hoverClosestCartesian={name:\"hoverClosestCartesian\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:f},u.hoverCompareCartesian={name:\"hoverCompareCartesian\",_cat:\"hoverCompare\",title:function(t){return c(t,\"Compare data on hover\")},attr:\"hovermode\",val:function(t){return t._fullLayout._isHoriz?\"y\":\"x\"},icon:o.tooltip_compare,gravity:\"ne\",click:f},u.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(t){return c(t,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:o.zoombox,click:h},u.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(t){return c(t,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:o.pan,click:h},u.orbitRotation={name:\"orbitRotation\",title:function(t){return c(t,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:o[\"3d_rotate\"],click:h},u.tableRotation={name:\"tableRotation\",title:function(t){return c(t,\"Turntable rotation\")},attr:\"scene.dragmode\",val:\"turntable\",icon:o[\"z-axis\"],click:h},u.resetCameraDefault3d={name:\"resetCameraDefault3d\",_cat:\"resetCameraDefault\",title:function(t){return c(t,\"Reset camera to default\")},attr:\"resetDefault\",icon:o.home,click:p},u.resetCameraLastSave3d={name:\"resetCameraLastSave3d\",_cat:\"resetCameraLastSave\",title:function(t){return c(t,\"Reset camera to last save\")},attr:\"resetLastSave\",icon:o.movie,click:p},u.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);n.call(\"_guiRelayout\",t,r)}},u.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:m},u.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(t){return c(t,\"Reset\")},attr:\"reset\",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestGl2d={name:\"hoverClosestGl2d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:v},u.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return c(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},u.toggleHover={name:\"toggleHover\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call(\"_guiRelayout\",t,r)}},u.resetViews={name:\"resetViews\",title:function(t){return c(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:\"zoomOutMapbox\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:y}},{\"../../fonts/ploticon\":482,\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../plots/plots\":619,\"../../registry\":638,\"../shapes/draw\":450}],427:[function(t,e,r){\"use strict\";var n=t(\"./buttons\"),i=Object.keys(n),a=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],o=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),e.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},{\"./buttons\":426}],428:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES,u=t(\"../../lib\").extendDeep;e.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=u([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(m||b||y||x||_)+v+g+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):g?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];m&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!m&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e})(r)&&z.push(\"select2d\",\"lasso2d\");var D=[],R=function(t){-1===D.indexOf(t)&&-1!==I.indexOf(t)&&D.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];\"string\"==typeof N?(N=N.toLowerCase(),-1!==c.indexOf(N)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&z.push(N):\"togglespikelines\"===N?R(\"toggleSpikelines\"):\"togglehover\"===N?R(\"toggleHover\"):\"hovercompare\"===N?R(\"hoverCompareCartesian\"):\"hoverclosest\"===N?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\")):\"v1hovermode\"===N&&(R(\"toggleHover\"),R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\"))):F.push(N)}p=F}return L(z),L(P.concat(O)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},{\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../../traces/scatter/subtypes\":952,\"../fx/helpers\":402,\"./buttons\":426,\"./constants\":427,\"./modebar\":431}],431:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"fast-isnumeric\"),a=t(\"../../lib\"),o=t(\"../../fonts/ploticon\"),s=t(\"../../version\").version,l=new DOMParser;function c(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var u=c.prototype;u.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover \":\"\";a.deleteRelatedStyleRule(i),a.addRelatedStyleRule(i,s+\"#\"+i+\" .modebar-group\",\"background-color: \"+o.bgcolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn .icon path\",\"fill: \"+o.color),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn:hover .icon path\",\"fill: \"+o.activecolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn.active .icon path\",\"fill: \"+o.activecolor);var l=!this.hasButtons(e),c=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(l||c||u)&&(this.removeAllButtons(),this.updateButtons(e),r.watermark||r.displaylogo)){var f=this.getLogo();r.watermark&&(f.className=f.className+\" watermark\"),\"v\"===n.modebar.orientation?this.element.insertBefore(f,this.element.childNodes[0]):this.element.appendChild(f),this.hasLogo=!0}this.updateActiveButton()},u.updateButtons=function(t){var e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},u.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},u.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute(\"data-toggle\",t.toggle||!1),t.toggle&&n.select(r).classed(\"active\",!0);var s=t.icon;return\"function\"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute(\"data-gravity\",t.gravity||\"n\"),r},u.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n=\"http://www.w3.org/2000/svg\";if(t.path){(e=document.createElementNS(n,\"svg\")).setAttribute(\"viewBox\",[0,0,t.width,r].join(\" \")),e.setAttribute(\"class\",\"icon\");var a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]);return e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},u.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",c===i)}}))},u.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var 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t.match(n.segmentRE).forEach((function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}})),r},r.getDataToPixel=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){return e._length*(n?1-t:t)+e._offset};else{var s=r.shapePositionToRange(e);a=function(t){return e._offset+e.r2p(s(t,!0))},\"date\"===e.type&&(a=r.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},r.getPixelToData=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){var r=(t-e._offset)/e._length;return n?1-r:r};else{var s=r.rangeToShapePosition(e);a=function(t){return s(e.p2r(t-e._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},r.makeOptionsAndPlotinfo=function(t,e){var 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/pad_attributes\"),a=t(\"../../lib/extend\").extendDeepAll,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/animation_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=t(\"./constants\"),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plot_api/plot_template\":543,\"../../plots/animation_attributes\":548,\"../../plots/font_attributes\":585,\"../../plots/pad_attributes\":618,\"./constants\":460}],460:[function(t,e,r){\"use 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return 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t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),o.on(\"touchend\",null),o.on(\"touchmove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}T(e,r,t,c,!0),t._dragging=!0,o.on(\"mousemove\",u),o.on(\"touchmove\",u),o.on(\"mouseup\",h),o.on(\"touchend\",h)}t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function M(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var 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n=t(\"@plotly/d3\"),i=t(\"d3-time-format\").utcFormat,a=t(\"d3-format\").format,o=t(\"fast-isnumeric\"),s=t(\"../constants/numerical\"),l=s.FP_SAFE,c=-l,u=s.BADNUM,f=e.exports={};f.adjustFormat=function(t){return!t||/^\\d[.]\\df/.test(t)||/[.]\\d%/.test(t)?t:\"0.f\"===t?\"~f\":/^\\d%/.test(t)?\"~%\":/^\\ds/.test(t)?\"~s\":!/^[~,.0$]/.test(t)&&/[&fps]/.test(t)?\"~\"+t:t};var h={};f.warnBadFormat=function(t){var e=String(t);h[e]||(h[e]=1,f.warn('encountered bad format: \"'+e+'\"'))},f.noFormat=function(t){return String(t)},f.numberFormat=function(t){var e;try{e=a(f.adjustFormat(t))}catch(e){return f.warnBadFormat(t),f.noFormat}return e},f.nestedProperty=t(\"./nested_property\"),f.keyedContainer=t(\"./keyed_container\"),f.relativeAttr=t(\"./relative_attr\"),f.isPlainObject=t(\"./is_plain_object\"),f.toLogRange=t(\"./to_log_range\"),f.relinkPrivateKeys=t(\"./relink_private\");var 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b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener(\"resize\",t._responsiveChartHandler)):o.clearResponsive(t);var T=o.extendFlat({},m._size),k=0;function A(){if(h.clearAutoMarginIds(t),w.drawMarginPushers(t),p.allowAutoMargin(t),m._has(\"pie\"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];\"pie\"===n.type&&n.automargin&&h.allowAutoMargin(t,\"pie.\"+n.uid+\".automargin\")}return h.doAutoMargin(t),h.previousPromises(t)}function S(){t._transitioning||(w.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),u.getComponentMethod(\"rangeslider\",\"calcAutorange\")(t))}var E=[h.previousPromises,function(){if(s)return r.addFrames(t,s)},function e(){for(var 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u=e._clips.selectAll(\".axesclip\").data(c,(function(t){return t.x._id+t.y._id}));u.enter().append(\"clipPath\").classed(\"axesclip\",!0).attr(\"id\",(function(t){return\"clip\"+e._uid+t.x._id+t.y._id})).append(\"rect\"),u.exit().remove(),u.each((function(t){n.select(this).select(\"rect\").attr({x:t.x._offset||0,y:t.y._offset||0,width:t.x._length||1,height:t.y._length||1})}))}},H.draw=function(t,e,r){var n=t._fullLayout;\"redraw\"===e&&n._paper.selectAll(\"g.subplot\").each((function(t){var e=t[0],r=n._plots[e];if(r){var i=r.xaxis,a=r.yaxis;r.xaxislayer.selectAll(\".\"+i._id+\"tick\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"tick2\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick2\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"divider\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"divider\").remove(),r.minorGridlayer&&r.minorGridlayer.selectAll(\"path\").remove(),r.gridlayer&&r.gridlayer.selectAll(\"path\").remove(),r.zerolinelayer&&r.zerolinelayer.selectAll(\"path\").remove(),n._infolayer.select(\".g-\"+i._id+\"title\").remove(),n._infolayer.select(\".g-\"+a._id+\"title\").remove()}}));var i=e&&\"redraw\"!==e?e:H.listIds(t);return s.syncOrAsync(i.map((function(e){return function(){if(e){var n=H.getFromId(t,e),i=H.drawOne(t,n,r);return n._r=n.range.slice(),n._rl=s.simpleMap(n._r,n.r2l),i}}})))},H.drawOne=function(t,e,r){var n,i,l;r=r||{},e.setScale();var c=t._fullLayout,p=e._id,d=p.charAt(0),m=H.counterLetter(p),g=c._plots[e._mainSubplot];if(g){var v=g[d+\"axislayer\"],y=e._mainLinePosition,x=e._mainMirrorPosition,b=e._vals=H.calcTicks(e),_=[e.mirror,y,x].join(\"_\");for(n=0;n<b.length;n++)b[n].axInfo=_;e._selections={},e._tickAngles&&(e._prevTickAngles=e._tickAngles),e._tickAngles={},e._depth=null;var w={};if(e.visible){var T,k,A=H.makeTransTickFn(e),M=H.makeTransTickLabelFn(e),S=\"inside\"===e.ticks,E=\"outside\"===e.ticks;if(\"boundaries\"===e.tickson){var L=function(t,e){var r,n=[],i=function(t,e){var r=t.xbnd[e];null!==r&&n.push(s.extendFlat({},t,{x:r}))};if(e.length){for(r=0;r<e.length;r++)i(e[r],0);i(e[r-1],1)}return n}(0,b);k=H.clipEnds(e,L),T=S?k:L}else k=H.clipEnds(e,b),T=S&&\"period\"!==e.ticklabelmode?k:b;var C,P=e._gridVals=k,I=function(t,e){var r,n,i=[],a=e.length&&e[e.length-1].x<e[0].x,o=function(t,e){var r=t.xbnd[e];null!==r&&i.push(s.extendFlat({},t,{x:r}))};if(t.showdividers&&e.length){for(r=0;r<e.length;r++){var l=e[r];l.text2!==n&&o(l,a?1:0),n=l.text2}o(e[r-1],a?0:1)}return i}(e,b);if(!c._hasOnlyLargeSploms){var O=e._subplotsWith,z={};for(n=0;n<O.length;n++){i=O[n];var D=(l=c._plots[i])[m+\"axis\"],R=D._mainAxis._id;if(!z[R]){z[R]=1;var F=\"x\"===d?\"M0,\"+D._offset+\"v\"+D._length:\"M\"+D._offset+\",0h\"+D._length;H.drawGrid(t,e,{vals:P,counterAxis:D,layer:l.gridlayer.select(\".\"+p),minorLayer:l.minorGridlayer.select(\".\"+p),path:F,transFn:A}),H.drawZeroLine(t,e,{counterAxis:D,layer:l.zerolinelayer,path:F,transFn:A})}}}var B=H.getTickSigns(e),N=H.getTickSigns(e,\"minor\");if(e.ticks||e.minor&&e.minor.ticks){var j,q,G,Y,W=H.makeTickPath(e,y,B[2]),X=H.makeTickPath(e,y,N[2],{minor:!0});if(e._anchorAxis&&e.mirror&&!0!==e.mirror?(j=H.makeTickPath(e,x,B[3]),q=H.makeTickPath(e,x,N[3],{minor:!0}),G=W+j,Y=X+q):(j=\"\",q=\"\",G=W,Y=X),e.showdividers&&E&&\"boundaries\"===e.tickson){var Z={};for(n=0;n<I.length;n++)Z[I[n].x]=1;C=function(t){return Z[t.x]?j:G}}else C=function(t){return t.minor?Y:G}}if(H.drawTicks(t,e,{vals:T,layer:v,path:C,transFn:A}),\"allticks\"===e.mirror){var J=Object.keys(e._linepositions||{});for(n=0;n<J.length;n++){i=J[n],l=c._plots[i];var K=e._linepositions[i]||[],Q=K[0],$=K[1],tt=K[2],et=H.makeTickPath(e,Q,tt?B[0]:N[0],{minor:tt})+H.makeTickPath(e,$,tt?B[1]:N[1],{minor:tt});H.drawTicks(t,e,{vals:T,layer:l[d+\"axislayer\"],path:et,transFn:A})}}var rt=[];if(rt.push((function(){return H.drawLabels(t,e,{vals:b,layer:v,plotinfo:l,transFn:M,labelFns:H.makeLabelFns(e,y)})})),\"multicategory\"===e.type){var nt={x:2,y:10}[d];rt.push((function(){var r={x:\"height\",y:\"width\"}[d],n=at()[r]+nt+(e._tickAngles[p+\"tick\"]?e.tickfont.size*U:0);return H.drawLabels(t,e,{vals:vt(e,b),layer:v,cls:p+\"tick2\",repositionOnUpdate:!0,secondary:!0,transFn:A,labelFns:H.makeLabelFns(e,y+n*B[4])})})),rt.push((function(){return e._depth=B[4]*(at(\"tick2\")[e.side]-y),function(t,e,r){var n=e._id+\"divider\",i=r.vals,a=r.layer.selectAll(\"path.\"+n).data(i,xt);a.exit().remove(),a.enter().insert(\"path\",\":first-child\").classed(n,1).classed(\"crisp\",1).call(f.stroke,e.dividercolor).style(\"stroke-width\",h.crispRound(t,e.dividerwidth,1)+\"px\"),a.attr(\"transform\",r.transFn).attr(\"d\",r.path)}(t,e,{vals:I,layer:v,path:H.makeTickPath(e,y,B[4],{len:e._depth}),transFn:A})}))}else e.title.hasOwnProperty(\"standoff\")&&rt.push((function(){e._depth=B[4]*(at()[e.side]-y)}));var it=o.getComponentMethod(\"rangeslider\",\"isVisible\")(e);return rt.push((function(){var r,n,i,s,l=e.side.charAt(0),u=V[e.side].charAt(0),f=H.getPxPosition(t,e),h=E?e.ticklen:0;if((e.automargin||it)&&(\"multicategory\"===e.type?r=at(\"tick2\"):(r=at(),\"x\"===d&&\"b\"===l&&(e._depth=Math.max(r.width>0?r.bottom-f:0,h)))),e.automargin){n={x:0,y:0,r:0,l:0,t:0,b:0};var p=[0,1];if(\"x\"===d){if(\"b\"===l?n[l]=e._depth:(n[l]=e._depth=Math.max(r.width>0?f-r.top:0,h),p.reverse()),r.width>0){var g=r.right-(e._offset+e._length);g>0&&(n.xr=1,n.r=g);var v=e._offset-r.left;v>0&&(n.xl=0,n.l=v)}}else if(\"l\"===l?n[l]=e._depth=Math.max(r.height>0?f-r.left:0,h):(n[l]=e._depth=Math.max(r.height>0?r.right-f:0,h),p.reverse()),r.height>0){var y=r.bottom-(e._offset+e._length);y>0&&(n.yb=0,n.b=y);var x=e._offset-r.top;x>0&&(n.yt=1,n.t=x)}n[m]=\"free\"===e.anchor?e.position:e._anchorAxis.domain[p[0]],e.title.text!==c._dfltTitle[d]&&(n[l]+=bt(e)+(e.title.standoff||0)),e.mirror&&\"free\"!==e.anchor&&((i={x:0,y:0,r:0,l:0,t:0,b:0})[u]=e.linewidth,e.mirror&&!0!==e.mirror&&(i[u]+=h),!0===e.mirror||\"ticks\"===e.mirror?i[m]=e._anchorAxis.domain[p[1]]:\"all\"!==e.mirror&&\"allticks\"!==e.mirror||(i[m]=[e._counterDomainMin,e._counterDomainMax][p[1]]))}it&&(s=o.getComponentMethod(\"rangeslider\",\"autoMarginOpts\")(t,e)),a.autoMargin(t,Tt(e),n),a.autoMargin(t,kt(e),i),a.autoMargin(t,At(e),s)})),r.skipTitle||it&&\"bottom\"===e.side||rt.push((function(){return function(t,e){var r,n=t._fullLayout,i=e._id,a=i.charAt(0),o=e.title.font.size;if(e.title.hasOwnProperty(\"standoff\"))r=e._depth+e.title.standoff+bt(e);else{var s=Pt(e);if(\"multicategory\"===e.type)r=e._depth;else{var l=1.5*o;s&&(l=.5*o,\"outside\"===e.ticks&&(l+=e.ticklen)),r=10+l+(e.linewidth?e.linewidth-1:0)}s||(r+=\"x\"===a?\"top\"===e.side?o*(e.showticklabels?1:0):o*(e.showticklabels?1.5:.5):\"right\"===e.side?o*(e.showticklabels?1:.5):o*(e.showticklabels?.5:0))}var c,f,p,d,m=H.getPxPosition(t,e);\"x\"===a?(f=e._offset+e._length/2,p=\"top\"===e.side?m-r:m+r):(p=e._offset+e._length/2,f=\"right\"===e.side?m+r:m-r,c={rotate:\"-90\",offset:0});if(\"multicategory\"!==e.type){var g=e._selections[e._id+\"tick\"];if(d={selection:g,side:e.side},g&&g.node()&&g.node().parentNode){var v=h.getTranslate(g.node().parentNode);d.offsetLeft=v.x,d.offsetTop=v.y}e.title.hasOwnProperty(\"standoff\")&&(d.pad=0)}return u.draw(t,i+\"title\",{propContainer:e,propName:e._name+\".title.text\",placeholder:n._dfltTitle[a],avoid:d,transform:c,attributes:{x:f,y:p,\"text-anchor\":\"middle\"}})}(t,e)})),s.syncOrAsync(rt)}}function at(t){var r=p+(t||\"tick\");return w[r]||(w[r]=function(t,e){var r,n,i,a;t._selections[e].size()?(r=1/0,n=-1/0,i=1/0,a=-1/0,t._selections[e].each((function(){var t=wt(this),e=h.bBox(t.node().parentNode);r=Math.min(r,e.top),n=Math.max(n,e.bottom),i=Math.min(i,e.left),a=Math.max(a,e.right)}))):(r=0,n=0,i=0,a=0);return{top:r,bottom:n,left:i,right:a,height:n-r,width:a-i}}(e,r)),w[r]}},H.getTickSigns=function(t,e){var r=t._id.charAt(0),n={x:\"top\",y:\"right\"}[r],i=t.side===n?1:-1,a=[-1,1,i,-i];return\"inside\"!==(e?(t.minor||{}).ticks:t.ticks)==(\"x\"===r)&&(a=a.map((function(t){return-t}))),t.side&&a.push({l:-1,t:-1,r:1,b:1}[t.side.charAt(0)]),a},H.makeTransTickFn=function(t){return\"x\"===t._id.charAt(0)?function(e){return l(t._offset+t.l2p(e.x),0)}:function(e){return l(0,t._offset+t.l2p(e.x))}},H.makeTransTickLabelFn=function(t){var e=function(t){var e=t.ticklabelposition||\"\",r=function(t){return-1!==e.indexOf(t)},n=r(\"top\"),i=r(\"left\"),a=r(\"right\"),o=r(\"bottom\"),s=r(\"inside\"),l=o||i||n||a;if(!l&&!s)return[0,0];var c=t.side,u=l?(t.tickwidth||0)/2:0,f=3,h=t.tickfont?t.tickfont.size:12;(o||n)&&(u+=h*j,f+=(t.linewidth||0)/2);(i||a)&&(u+=(t.linewidth||0)/2,f+=3);s&&\"top\"===c&&(f-=h*(1-j));(i||n)&&(u=-u);\"bottom\"!==c&&\"right\"!==c||(f=-f);return[l?u:0,s?f:0]}(t),r=e[0],n=e[1];return\"x\"===t._id.charAt(0)?function(e){return l(r+t._offset+t.l2p(yt(e)),n)}:function(e){return l(n,r+t._offset+t.l2p(yt(e)))}},H.makeTickPath=function(t,e,r,n){n||(n={});var i=n.minor;if(i&&!t.minor)return\"\";var a=void 0!==n.len?n.len:i?t.minor.ticklen:t.ticklen,o=t._id.charAt(0),s=(t.linewidth||1)/2;return\"x\"===o?\"M0,\"+(e+s*r)+\"v\"+a*r:\"M\"+(e+s*r)+\",0h\"+a*r},H.makeLabelFns=function(t,e,r){var n=t.ticklabelposition||\"\",a=function(t){return-1!==n.indexOf(t)},o=a(\"top\"),l=a(\"left\"),c=a(\"right\"),u=a(\"bottom\")||l||o||c,f=a(\"inside\"),h=\"inside\"===n&&\"inside\"===t.ticks||!f&&\"outside\"===t.ticks&&\"boundaries\"!==t.tickson,p=0,d=0,m=h?t.ticklen:0;if(f?m*=-1:u&&(m=0),h&&(p+=m,r)){var g=s.deg2rad(r);p=m*Math.cos(g)+1,d=m*Math.sin(g)}t.showticklabels&&(h||t.showline)&&(p+=.2*t.tickfont.size);var v,y,x,b,_,w={labelStandoff:p+=(t.linewidth||1)/2*(f?-1:1),labelShift:d},T=0,k=t.side,A=t._id.charAt(0),M=t.tickangle;if(\"x\"===A)b=(_=!f&&\"bottom\"===k||f&&\"top\"===k)?1:-1,f&&(b*=-1),v=d*b,y=e+p*b,x=_?1:-.2,90===Math.abs(M)&&(f?x+=N:x=-90===M&&\"bottom\"===k?j:90===M&&\"top\"===k?N:.5,T=N/2*(M/90)),w.xFn=function(t){return t.dx+v+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*x},w.anchorFn=function(t,e){if(u){if(l)return\"end\";if(c)return\"start\"}return i(e)&&0!==e&&180!==e?e*b<0!==f?\"end\":\"start\":\"middle\"},w.heightFn=function(e,r,n){return r<-60||r>60?-.5*n:\"top\"===t.side!==f?-n:0};else if(\"y\"===A){if(b=(_=!f&&\"left\"===k||f&&\"right\"===k)?1:-1,f&&(b*=-1),v=p,y=d*b,x=0,f||90!==Math.abs(M)||(x=-90===M&&\"left\"===k||90===M&&\"right\"===k?j:.5),f){var S=i(M)?+M:0;if(0!==S){var E=s.deg2rad(S);T=Math.abs(Math.sin(E))*j*b,x=0}}w.xFn=function(t){return t.dx+e-(v+t.fontSize*x)*b+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*N},w.anchorFn=function(t,e){return i(e)&&90===Math.abs(e)?\"middle\":_?\"end\":\"start\"},w.heightFn=function(e,r,n){return\"right\"===t.side&&(r*=-1),r<-30?-n:r<30?-.5*n:0}}return w},H.drawTicks=function(t,e,r){r=r||{};var i=e._id+\"tick\",a=[].concat(e.minor&&e.minor.ticks?r.vals.filter((function(t){return t.minor&&!t.noTick})):[]).concat(e.ticks?r.vals.filter((function(t){return!t.minor&&!t.noTick})):[]),o=r.layer.selectAll(\"path.\"+i).data(a,xt);o.exit().remove(),o.enter().append(\"path\").classed(i,1).classed(\"ticks\",1).classed(\"crisp\",!1!==r.crisp).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.tickcolor:e.tickcolor)})).style(\"stroke-width\",(function(r){return h.crispRound(t,r.minor?e.minor.tickwidth:e.tickwidth,1)+\"px\"})).attr(\"d\",r.path).style(\"display\",null),It(e,[R]),o.attr(\"transform\",r.transFn)},H.drawGrid=function(t,e,r){r=r||{};var i=e._id+\"grid\",a=e.minor&&e.minor.showgrid,o=a?r.vals.filter((function(t){return t.minor})):[],s=e.showgrid?r.vals.filter((function(t){return!t.minor})):[],l=r.counterAxis;if(l&&H.shouldShowZeroLine(t,e,l))for(var c=\"array\"===e.tickmode,u=0;u<s.length;u++){var p=s[u].x;if(c?!p:Math.abs(p)<e.dtick/100){if(s=s.slice(0,u).concat(s.slice(u+1)),!c)break;u--}}e._gw=h.crispRound(t,e.gridwidth,1);for(var d=a?h.crispRound(t,e.minor.gridwidth,1):0,m=r.layer,g=r.minorLayer,v=1;v>=0;v--){var y=v?m:g;if(y){var x=y.selectAll(\"path.\"+i).data(v?s:o,xt);x.exit().remove(),x.enter().append(\"path\").classed(i,1).classed(\"crisp\",!1!==r.crisp),x.attr(\"transform\",r.transFn).attr(\"d\",r.path).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.gridcolor:e.gridcolor||\"#ddd\")})).style(\"stroke-dasharray\",(function(t){return h.dashStyle(t.minor?e.minor.griddash:e.griddash,t.minor?e.minor.gridwidth:e.gridwidth)})).style(\"stroke-width\",(function(t){return(t.minor?d:e._gw)+\"px\"})).style(\"display\",null),\"function\"==typeof r.path&&x.attr(\"d\",r.path)}}It(e,[z,D])},H.drawZeroLine=function(t,e,r){r=r||r;var n=e._id+\"zl\",i=H.shouldShowZeroLine(t,e,r.counterAxis),a=r.layer.selectAll(\"path.\"+n).data(i?[{x:0,id:e._id}]:[]);a.exit().remove(),a.enter().append(\"path\").classed(n,1).classed(\"zl\",1).classed(\"crisp\",!1!==r.crisp).each((function(){r.layer.selectAll(\"path\").sort((function(t,e){return Y(t.id,e.id)}))})),a.attr(\"transform\",r.transFn).attr(\"d\",r.path).call(f.stroke,e.zerolinecolor||f.defaultLine).style(\"stroke-width\",h.crispRound(t,e.zerolinewidth,e._gw||1)+\"px\").style(\"display\",null),It(e,[O])},H.drawLabels=function(t,e,r){r=r||{};var a=t._fullLayout,o=e._id,u=o.charAt(0),f=r.cls||o+\"tick\",p=r.vals.filter((function(t){return t.text})),d=r.labelFns,m=r.secondary?0:e.tickangle,g=(e._prevTickAngles||{})[f],v=r.layer.selectAll(\"g.\"+f).data(e.showticklabels?p:[],xt),y=[];function x(t,a){t.each((function(t){var o=n.select(this),s=o.select(\".text-math-group\"),u=d.anchorFn(t,a),f=r.transFn.call(o.node(),t)+(i(a)&&0!=+a?\" rotate(\"+a+\",\"+d.xFn(t)+\",\"+(d.yFn(t)-t.fontSize/2)+\")\":\"\"),p=c.lineCount(o),m=U*t.fontSize,g=d.heightFn(t,i(a)?+a:0,(p-1)*m);if(g&&(f+=l(0,g)),s.empty()){var v=o.select(\"text\");v.attr({transform:f,\"text-anchor\":u}),v.style(\"opacity\",1),e._adjustTickLabelsOverflow&&e._adjustTickLabelsOverflow()}else{var y=h.bBox(s.node()).width*{end:-.5,start:.5}[u];s.attr(\"transform\",f+l(y,0))}}))}v.enter().append(\"g\").classed(f,1).append(\"text\").attr(\"text-anchor\",\"middle\").each((function(e){var r=n.select(this),i=t._promises.length;r.call(c.positionText,d.xFn(e),d.yFn(e)).call(h.font,e.font,e.fontSize,e.fontColor).text(e.text).call(c.convertToTspans,t),t._promises[i]?y.push(t._promises.pop().then((function(){x(r,m)}))):x(r,m)})),It(e,[F]),v.exit().remove(),r.repositionOnUpdate&&v.each((function(t){n.select(this).select(\"text\").call(c.positionText,d.xFn(t),d.yFn(t))})),e._adjustTickLabelsOverflow=function(){var r=e.ticklabeloverflow;if(r&&\"allow\"!==r){var i=-1!==r.indexOf(\"hide\"),o=\"x\"===e._id.charAt(0),l=0,c=o?t._fullLayout.width:t._fullLayout.height;if(-1!==r.indexOf(\"domain\")){var u=s.simpleMap(e.range,e.r2l);l=e.l2p(u[0])+e._offset,c=e.l2p(u[1])+e._offset}var f=Math.min(l,c),p=Math.max(l,c),d=e.side,m=1/0,g=-1/0;for(var y in v.each((function(t){var 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o=\"tick\"===t.K&&\"text\"===t.L&&\"period\"===e.ticklabelmode,s=a._plots[e._mainSubplot];(t.K===O.K?s.zerolinelayer.selectAll(\".\"+e._id+\"zl\"):t.K===D.K?s.minorGridlayer.selectAll(\".\"+e._id):t.K===z.K?s.gridlayer.selectAll(\".\"+e._id):s[e._id.charAt(0)+\"axislayer\"]).each((function(){var a=n.select(this);t.L&&(a=a.selectAll(t.L)),a.each((function(a){var s=e.l2p(o?yt(a):a.x)+e._offset,l=n.select(this);s<e[\"_visibleLabelMax_\"+r._id]&&s>e[\"_visibleLabelMin_\"+r._id]?l.style(\"display\",\"none\"):\"tick\"!==t.K||i||l.style(\"display\",null)}))}))}))}))},x(v,g+1?g:m);var b=null;e._selections&&(e._selections[f]=v);var _=[function(){return y.length&&Promise.all(y)}];e.automargin&&a._redrawFromAutoMarginCount&&90===g?(b=90,_.push((function(){x(v,g)}))):_.push((function(){if(x(v,m),p.length&&\"x\"===u&&!i(m)&&(\"log\"!==e.type||\"D\"!==String(e.dtick).charAt(0))){b=0;var t,n=0,a=[];if(v.each((function(t){n=Math.max(n,t.fontSize);var 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n=t(\"fast-isnumeric\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../../plot_api/plot_template\"),s=t(\"../array_container_defaults\"),l=t(\"./layout_attributes\"),c=t(\"./tick_value_defaults\"),u=t(\"./tick_mark_defaults\"),f=t(\"./tick_label_defaults\"),h=t(\"./prefix_suffix_defaults\"),p=t(\"./category_order_defaults\"),d=t(\"./line_grid_defaults\"),m=t(\"./set_convert\"),g=t(\"./constants\").WEEKDAY_PATTERN,v=t(\"./constants\").HOUR_PATTERN;function y(t,e,r){function i(r,n){return a.coerce(t,e,l.rangebreaks,r,n)}if(i(\"enabled\")){var o=i(\"bounds\");if(o&&o.length>=2){var s,c,u=\"\";if(2===o.length)for(s=0;s<2;s++)if(c=b(o[s])){u=g;break}var f=i(\"pattern\",u);if(f===g)for(s=0;s<2;s++)(c=b(o[s]))&&(e.bounds[s]=o[s]=c-1);if(f)for(s=0;s<2;s++)switch(c=o[s],f){case g:if(!n(c))return void(e.enabled=!1);if((c=+c)!==Math.floor(c)||c<0||c>=7)return void(e.enabled=!1);e.bounds[s]=o[s]=c;break;case v:if(!n(c))return void(e.enabled=!1);if((c=+c)<0||c>24)return 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),yt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":y(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,y(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=Ot,y(a)&&It(),ot||(\"zoom\"===s?(Pt.moveFn=zt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(mt,kt,Q,$,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(mt,Q,$)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;y(r)||(yt(),Yt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b=\"ns\"===at?-e[1]*a._length/O._length:Zt(a,y,{n:\"top\",s:\"bottom\"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T=\"ew\"===it?-e[0]*o._length/I._length:Zt(o,x,{e:\"right\",w:\"left\"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||\"middle\"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(\".nsewdrag\").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt(\"x\"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt(\"y\"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit(\"plotly_relayouting\",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log(\"Did not find wheel motion attributes: \",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{\"../../components/color\":366,\"../../components/dragelement\":385,\"../../components/dragelement/helpers\":384,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/clear_gl_canvases\":487,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/subroutines\":544,\"../../registry\":638,\"../plots\":619,\"./axes\":554,\"./axis_ids\":558,\"./constants\":561,\"./scale_zoom\":574,\"./select\":575,\"@plotly/d3\":58,\"has-passive-events\":229,tinycolor2:312}],564:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/fx\"),a=t(\"../../components/dragelement\"),o=t(\"../../lib/setcursor\"),s=t(\"./dragbox\").makeDragBox,l=t(\"./constants\").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(\".drag\").remove();else if(e._has(\"cartesian\")||e._has(\"splom\")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split(\"y\"),i=r.split(\"y\");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,\"ns\",\"ew\");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,\"n\",\"w\"),s(t,n,o._offset+o._length,c._offset-l,l,l,\"n\",\"e\"),s(t,n,o._offset-l,c._offset+c._length,l,l,\"s\",\"w\"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,\"s\",\"e\"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;\"top\"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,\"\",\"ew\"),s(t,n,o._offset,f,.1*o._length,l,\"\",\"w\"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,\"\",\"e\")}if(r===c._mainSubplot){var h=c._mainLinePosition;\"right\"!==c.side&&(h-=l),s(t,n,h,c._offset+.1*c._length,l,.8*c._length,\"ns\",\"\"),s(t,n,h,c._offset+.9*c._length,l,.1*c._length,\"s\",\"\"),s(t,n,h,c._offset,l,.1*c._length,\"n\",\"\")}}}));var o=e._hoverlayer.node();o.onmousemove=function(r){r.target=t._fullLayout._lasthover,i.hover(t,r,e._hoversubplot)},o.onclick=function(e){e.target=t._fullLayout._lasthover,i.click(t,e)},o.onmousedown=function(e){t._fullLayout._lasthover.onmousedown(e)},r.updateFx(t)}},r.updateFx=function(t){var e=t._fullLayout,r=\"pan\"===e.dragmode?\"move\":\"crosshair\";o(e._draggers,r)}},{\"../../components/dragelement\":385,\"../../components/fx\":406,\"../../lib/setcursor\":524,\"./constants\":561,\"./dragbox\":563,\"@plotly/d3\":58}],565:[function(t,e,r){\"use strict\";e.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearSelect:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._drawing=!1}}},{}],566:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{\"../../lib\":503}],567:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"./axis_ids\");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has(\"cartesian\")||r._has(\"gl2d\"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var g=a.cleanId(m.xref,\"x\",!1),v=a.cleanId(m.yref,\"y\",!1),y=l.x.test(g),x=l.y.test(v);if(y||x){p||i.pushUnique(r._basePlotModules,s);var b=!1;y&&-1===u.indexOf(g)&&(u.push(g),b=!0),x&&-1===f.indexOf(v)&&(f.push(v),b=!0),b&&y&&x&&h.push(g+v)}}}}}},{\"../../lib\":503,\"../../registry\":638,\"./axis_ids\":558}],568:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../plots\"),s=t(\"../../components/drawing\"),l=t(\"../get_data\").getModuleCalcData,c=t(\"./axis_ids\"),u=t(\"./constants\"),f=t(\"../../constants/xmlns_namespaces\"),h=a.ensureSingle;function p(t,e,r){return a.ensureSingle(t,e,r,(function(t){t.datum(r)}))}function d(t,e,r,a,o){for(var c,f,h,p=u.traceLayerClasses,d=t._fullLayout,m=d._modules,g=[],v=[],y=0;y<m.length;y++){var x=(c=m[y]).name,b=i.modules[x].categories;if(b.svg){var _=c.layerName||x+\"layer\",w=c.plot;h=(f=l(r,w))[0],r=f[1],h.length&&g.push({i:p.indexOf(_),className:_,plotMethod:w,cdModule:h}),b.zoomScale&&v.push(\".\"+_)}}g.sort((function(t,e){return t.i-e.i}));var T=e.plot.selectAll(\"g.mlayer\").data(g,(function(t){return t.className}));if(T.enter().append(\"g\").attr(\"class\",(function(t){return t.className})).classed(\"mlayer\",!0).classed(\"rangeplot\",e.isRangePlot),T.exit().remove(),T.order(),T.each((function(r){var i=n.select(this),l=r.className;r.plotMethod(t,e,r.cdModule,i,a,o),-1===u.clipOnAxisFalseQuery.indexOf(\".\"+l)&&s.setClipUrl(i,e.layerClipId,t)})),d._has(\"scattergl\")&&(c=i.getModule(\"scattergl\"),h=l(r,c)[0],c.plot(t,e,h)),!t._context.staticPlot&&(e._hasClipOnAxisFalse&&(e.clipOnAxisFalseTraces=e.plot.selectAll(u.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),v.length)){var k=e.plot.selectAll(v.join(\",\")).selectAll(\".trace\");e.zoomScalePts=k.selectAll(\"path.point\"),e.zoomScaleTxt=k.selectAll(\".textpoint\")}}function m(t,e){var r=e.plotgroup,n=e.id,i=u.layerValue2layerClass[e.xaxis.layer],a=u.layerValue2layerClass[e.yaxis.layer],o=t._fullLayout._hasOnlyLargeSploms;if(e.mainplot){var s=e.mainplotinfo,l=s.plotgroup,f=n+\"-x\",d=n+\"-y\";e.minorGridlayer=s.minorGridlayer,e.gridlayer=s.gridlayer,e.zerolinelayer=s.zerolinelayer,h(s.overlinesBelow,\"path\",f),h(s.overlinesBelow,\"path\",d),h(s.overaxesBelow,\"g\",f),h(s.overaxesBelow,\"g\",d),e.plot=h(s.overplot,\"g\",n),h(s.overlinesAbove,\"path\",f),h(s.overlinesAbove,\"path\",d),h(s.overaxesAbove,\"g\",f),h(s.overaxesAbove,\"g\",d),e.xlines=l.select(\".overlines-\"+i).select(\".\"+f),e.ylines=l.select(\".overlines-\"+a).select(\".\"+d),e.xaxislayer=l.select(\".overaxes-\"+i).select(\".\"+f),e.yaxislayer=l.select(\".overaxes-\"+a).select(\".\"+d)}else 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yt=b(\"rangeslider\",\"handleDefaults\"),xt=b(\"rangeselector\",\"handleDefaults\");for(y=0;y<j.length;y++)q=j[y],W=t[q],X=e[q],yt(t,e,q),\"date\"===X.type&&xt(W,X,e,U,X.calendar),K(\"fixedrange\");for(y=0;y<U.length;y++){q=U[y],W=t[q],X=e[q];var bt=e[m(X.anchor)];K(\"fixedrange\",b(\"rangeslider\",\"isVisible\")(bt))}h.handleDefaults(t,e,{axIds:rt.concat(it).sort(d.idSort),axHasImage:P})}},{\"../../components/color\":366,\"../../components/fx/helpers\":402,\"../../components/fx/hovermode_defaults\":405,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638,\"../layout_attributes\":610,\"./axis_defaults\":556,\"./axis_ids\":558,\"./constants\":561,\"./constraints\":562,\"./layout_attributes\":569,\"./position_defaults\":572,\"./type_defaults\":582}],571:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../components/color/attributes\"),a=t(\"../../lib\");e.exports=function(t,e,r,o){var s=(o=o||{}).dfltColor;function l(r,n){return a.coerce2(t,e,o.attributes,r,n)}var c=l(\"linecolor\",s),u=l(\"linewidth\");r(\"showline\",o.showLine||!!c||!!u)||(delete e.linecolor,delete e.linewidth);var f=l(\"gridcolor\",n(s,o.bgColor,o.blend||i.lightFraction).toRgbString()),h=l(\"gridwidth\"),p=l(\"griddash\");if(r(\"showgrid\",o.showGrid||!!f||!!h||!!p)||(delete e.gridcolor,delete e.gridwidth,delete e.griddash),o.hasMinor){var d=l(\"minor.gridcolor\",n(e.gridcolor,o.bgColor,67).toRgbString()),m=l(\"minor.gridwidth\",e.gridwidth||1),g=l(\"minor.griddash\",e.griddash||\"solid\");r(\"minor.showgrid\",!!d||!!m||!!g)||(delete e.minor.gridcolor,delete e.minor.gridwidth,delete e.minor.griddash)}if(!o.noZeroLine){var v=l(\"zerolinecolor\",s),y=l(\"zerolinewidth\");r(\"zeroline\",o.showGrid||!!v||!!y)||(delete e.zerolinecolor,delete e.zerolinewidth)}}},{\"../../components/color/attributes\":365,\"../../lib\":503,tinycolor2:312}],572:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\");e.exports=function(t,e,r,a){var o,s,l,c,u=a.counterAxes||[],f=a.overlayableAxes||[],h=a.letter,p=a.grid;p&&(s=p._domains[h][p._axisMap[e._id]],o=p._anchors[e._id],s&&(l=p[h+\"side\"].split(\" \")[0],c=p.domain[h][\"right\"===l||\"top\"===l?1:0])),s=s||[0,1],o=o||(n(t.position)?\"free\":u[0]||\"free\"),l=l||(\"x\"===h?\"bottom\":\"left\"),c=c||0,\"free\"===i.coerce(t,e,{anchor:{valType:\"enumerated\",values:[\"free\"].concat(u),dflt:o}},\"anchor\")&&r(\"position\",c),i.coerce(t,e,{side:{valType:\"enumerated\",values:\"x\"===h?[\"bottom\",\"top\"]:[\"left\",\"right\"],dflt:l}},\"side\");var d=!1;if(f.length&&(d=i.coerce(t,e,{overlaying:{valType:\"enumerated\",values:[!1].concat(f),dflt:!1}},\"overlaying\")),!d){var m=r(\"domain\",s);m[0]>m[1]-1/4096&&(e.domain=s),i.noneOrAll(t.domain,e.domain,s)}return r(\"layer\"),e}},{\"../../lib\":503,\"fast-isnumeric\":190}],573:[function(t,e,r){\"use strict\";var n=t(\"./show_dflt\");e.exports=function(t,e,r,i,a){a||(a={});var o=a.tickSuffixDflt,s=n(t);r(\"tickprefix\")&&r(\"showtickprefix\",s),r(\"ticksuffix\",o)&&r(\"showticksuffix\",s)}},{\"./show_dflt\":577}],574:[function(t,e,r){\"use strict\";var n=t(\"../../constants/alignment\").FROM_BL;e.exports=function(t,e,r){void 0===r&&(r=n[t.constraintoward||\"center\"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*r;t.range=t._input.range=[t.l2r(a+(i[0]-a)*e),t.l2r(a+(i[1]-a)*e)],t.setScale()}},{\"../../constants/alignment\":471}],575:[function(t,e,r){\"use strict\";var n=t(\"polybooljs\"),i=t(\"../../registry\"),a=t(\"../../components/drawing\").dashStyle,o=t(\"../../components/color\"),s=t(\"../../components/fx\"),l=t(\"../../components/fx/helpers\").makeEventData,c=t(\"../../components/dragelement/helpers\"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t(\"../../components/shapes/draw_newshape/display_outlines\"),g=t(\"../../components/shapes/draw_newshape/helpers\").handleEllipse,v=t(\"../../components/shapes/draw_newshape/newshapes\"),y=t(\"../../lib\"),x=t(\"../../lib/polygon\"),b=t(\"../../lib/throttle\"),_=t(\"./axis_ids\").getFromId,w=t(\"../../lib/clear_gl_canvases\"),T=t(\"../../plot_api/subroutines\").redrawReglTraces,k=t(\"./constants\"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t(\"./handle_outline\").clearSelect,L=t(\"./helpers\"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return 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e=[],r=z(t[0])?0:t[0][0][0],n=r,i=z(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(z(t[o]))e.push(D(t[o]));else{var s=x.tester(t[o]);s.subtract=t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!1===e[o].subtract);return a},isRect:!1,degenerate:!1}}function F(t,e,r){e._fullLayout._drawing=!1;var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(h(a)&&p(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||B(r),o||(E(e),n._lastSelectedSubplot=i.id)}function B(t){var e=t.dragmode,r=t.plotinfo,n=t.gd;if(n._fullLayout._activeShapeIndex>=0&&n._fullLayout._deactivateShape(n),h(e)){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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e=t._fullLayout,r=t.calcdata,i=e._subplots.geo,s=0;s<i.length;s++){var l=i[s],c=n(r,o,l),u=e[l]._subplot;u||(u=a({id:l,graphDiv:t,container:e._geolayer.node(),topojsonURL:t._context.topojsonURL,staticPlot:t._context.staticPlot}),e[l]._subplot=u),u.plot(c,e,t._promises)}},updateFx:function(t){for(var e=t._fullLayout,r=e._subplots.geo,n=0;n<r.length;n++){var i=e[r[n]];i._subplot.updateFx(e,i)}},clean:function(t,e,r,n){for(var i=n._subplots.geo||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!e[o]&&s&&(s.framework.remove(),s.clipDef.remove())}}}},{\"../../lib\":503,\"../../plots/get_data\":593,\"./geo\":588,\"./layout_attributes\":590,\"./layout_defaults\":591}],590:[function(t,e,r){\"use strict\";var n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../../components/drawing/attributes\").dash,o=t(\"./constants\"),s=t(\"../../plot_api/edit_types\").overrideAll,l=t(\"../../lib/sort_object_keys\"),c={range:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},showgrid:{valType:\"boolean\",dflt:!1},tick0:{valType:\"number\",dflt:0},dtick:{valType:\"number\"},gridcolor:{valType:\"color\",dflt:n.lightLine},gridwidth:{valType:\"number\",min:0,dflt:1},griddash:a};(e.exports=s({domain:i({name:\"geo\"},{}),fitbounds:{valType:\"enumerated\",values:[!1,\"locations\",\"geojson\"],dflt:!1,editType:\"plot\"},resolution:{valType:\"enumerated\",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:\"enumerated\",values:l(o.scopeDefaults),dflt:\"world\"},projection:{type:{valType:\"enumerated\",values:l(o.projNames)},rotation:{lon:{valType:\"number\"},lat:{valType:\"number\"},roll:{valType:\"number\"}},tilt:{valType:\"number\",dflt:0},distance:{valType:\"number\",min:1.001,dflt:2},parallels:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},scale:{valType:\"number\",min:0,dflt:1}},center:{lon:{valType:\"number\"},lat:{valType:\"number\"}},visible:{valType:\"boolean\",dflt:!0},showcoastlines:{valType:\"boolean\"},coastlinecolor:{valType:\"color\",dflt:n.defaultLine},coastlinewidth:{valType:\"number\",min:0,dflt:1},showland:{valType:\"boolean\",dflt:!1},landcolor:{valType:\"color\",dflt:o.landColor},showocean:{valType:\"boolean\",dflt:!1},oceancolor:{valType:\"color\",dflt:o.waterColor},showlakes:{valType:\"boolean\",dflt:!1},lakecolor:{valType:\"color\",dflt:o.waterColor},showrivers:{valType:\"boolean\",dflt:!1},rivercolor:{valType:\"color\",dflt:o.waterColor},riverwidth:{valType:\"number\",min:0,dflt:1},showcountries:{valType:\"boolean\"},countrycolor:{valType:\"color\",dflt:n.defaultLine},countrywidth:{valType:\"number\",min:0,dflt:1},showsubunits:{valType:\"boolean\"},subunitcolor:{valType:\"color\",dflt:n.defaultLine},subunitwidth:{valType:\"number\",min:0,dflt:1},showframe:{valType:\"boolean\"},framecolor:{valType:\"color\",dflt:n.defaultLine},framewidth:{valType:\"number\",min:0,dflt:1},bgcolor:{valType:\"color\",dflt:n.background},lonaxis:c,lataxis:c},\"plot\",\"from-root\")).uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../components/drawing/attributes\":387,\"../../lib/sort_object_keys\":526,\"../../plot_api/edit_types\":536,\"../domain\":584,\"./constants\":587}],591:[function(t,e,r){\"use 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d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var 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n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1124,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)\"skip\"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var s,l=o.getContext(\"2d\",{willReadFrequently:!0}),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(this._mockAxis,t)},e.exports=_},{\"../../../stackgl_modules\":1124,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/show_no_webgl_msg\":525,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"./layout/convert\":602,\"./layout/spikes\":605,\"./layout/tick_marks\":606,\"./project\":607,\"has-passive-events\":229,\"webgl-context\":331}],609:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],610:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"./animation_attributes\"),a=t(\"../components/color/attributes\"),o=t(\"../components/shapes/draw_newshape/attributes\"),s=t(\"./pad_attributes\"),l=t(\"../lib/extend\").extendFlat,c=n({editType:\"calc\"});c.family.dflt='\"Open Sans\", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:\"string\",editType:\"layoutstyle\"},font:n({editType:\"layoutstyle\"}),xref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},x:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"layoutstyle\"},y:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"left\",\"center\",\"right\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"layoutstyle\"},pad:l(s({editType:\"layoutstyle\"}),{}),editType:\"layoutstyle\"},uniformtext:{mode:{valType:\"enumerated\",values:[!1,\"hide\",\"show\"],dflt:!1,editType:\"plot\"},minsize:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"},autosize:{valType:\"boolean\",dflt:!1,editType:\"none\"},width:{valType:\"number\",min:10,dflt:700,editType:\"plot\"},height:{valType:\"number\",min:10,dflt:450,editType:\"plot\"},margin:{l:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},r:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},t:{valType:\"number\",min:0,dflt:100,editType:\"plot\"},b:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},autoexpand:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},computed:{valType:\"any\",editType:\"none\"},paper_bgcolor:{valType:\"color\",dflt:a.background,editType:\"plot\"},plot_bgcolor:{valType:\"color\",dflt:a.background,editType:\"layoutstyle\"},autotypenumbers:{valType:\"enumerated\",values:[\"convert types\",\"strict\"],dflt:\"convert types\",editType:\"calc\"},separators:{valType:\"string\",editType:\"plot\"},hidesources:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:{valType:\"boolean\",editType:\"legend\"},colorway:{valType:\"colorlist\",dflt:a.defaults,editType:\"calc\"},datarevision:{valType:\"any\",editType:\"calc\"},uirevision:{valType:\"any\",editType:\"none\"},editrevision:{valType:\"any\",editType:\"none\"},selectionrevision:{valType:\"any\",editType:\"none\"},template:{valType:\"any\",editType:\"calc\"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:\"any\",arrayOk:!0,editType:\"plot\"},transition:l({},i.transition,{editType:\"none\"}),_deprecated:{title:{valType:\"string\",editType:\"layoutstyle\"},titlefont:n({editType:\"layoutstyle\"})}}},{\"../components/color/attributes\":365,\"../components/shapes/draw_newshape/attributes\":451,\"../lib/extend\":493,\"./animation_attributes\":548,\"./font_attributes\":585,\"./pad_attributes\":618}],611:[function(t,e,r){\"use strict\";var n=t(\"../../lib/sort_object_keys\"),i='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',a=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',i].join(\" \"),o=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),s={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:i,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map 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t&&(-1!==p.styleValuesMapbox.indexOf(t)||0===t.indexOf(\"mapbox://\"))}r.name=\"mapbox\",r.attr=\"subplot\",r.idRoot=\"mapbox\",r.idRegex=r.attrRegex=i.counterRegex(\"mapbox\"),r.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots.mapbox;if(n.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(\"\"===t._context.mapboxAccessToken)return\"\";for(var n=[],a=[],o=!1,s=!1,l=0;l<e.length;l++){var c=r[e[l]],u=c.accesstoken;d(c.style)&&(u?i.pushUnique(n,u):(d(c._input.style)&&(i.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),u&&i.pushUnique(a,u)}if(s){var f=o?p.noAccessTokenErrorMsg:p.missingStyleErrorMsg;throw i.error(f),new Error(f)}return 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m=c.select(s._subplot.div);if(!(null===m.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var g=e._glimages.append(\"g\");g.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),g.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=m.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var b=u.bBox(x.node()),_=n.w*(h.x[1]-h.x[0]);if(b.width>_/2){var w=v.split(\"|\").join(\"<br>\");x.text(w).attr(\"data-unformatted\",w).call(f.convertToTspans,t),b=u.bBox(x.node())}x.attr(\"transform\",a(-3,8-b.height)),y.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var T=1;b.width+6>_&&(T=_/(b.width+6));var k=[n.l+n.w*h.x[1],n.t+n.h*(1-h.y[0])];y.attr(\"transform\",a(k[0],k[1])+o(T))}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../components/drawing\":388,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/get_data\":593,\"./constants\":611,\"./layout_attributes\":615,\"./layout_defaults\":616,\"./mapbox\":617,\"@plotly/d3\":58,\"mapbox-gl/dist/mapbox-gl-unminified\":239}],614:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var e=u(t);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},l.removeLayer=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"./constants\":611,\"./convert_text_opts\":612}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=t(\"./constants\"),f=o({});f.family.dflt=\"Open Sans Regular, Arial Unicode MS 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e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var 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O,z,D,B=g.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),N={element:B,gd:c};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),m.visible&&h<f,{transform:l(b,_)}),N.prepFn=function(){O=null,z=null,D=null,N.moveFn=j,N.doneFn=H,w(c)},N.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<S.MINDRAG&&(t=0,e=0),[t,e]},v.init(N)}function j(t,e){if(O)O(t,e);else{var n=[t,-e],a=[Math.cos(M),Math.sin(M)],s=Math.abs(o.dot(n,a)/Math.sqrt(o.dot(n,n)));isNaN(s)||(O=s<.5?q:G)}var l={};!function(t){null!==z?t[i.id+\".radialaxis.angle\"]=z:null!==D&&(t[i.id+\".radialaxis.range[\"+r+\"]\"]=D)}(l),c.emit(\"plotly_relayouting\",l)}function H(){null!==z?a.call(\"_guiRelayout\",c,i.id+\".radialaxis.angle\",z):null!==D&&a.call(\"_guiRelayout\",c,i.id+\".radialaxis.range[\"+r+\"]\",D)}function q(t,e){if(0!==r){var n=b+t,a=_+e;z=Math.atan2(d-a,n-p),i.vangles&&(z=U(z,i.vangles)),z=F(z);var o=l(p,d)+s(-z);u[\"radial-axis\"].attr(\"transform\",o),u[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above 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r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var 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r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),c=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[c]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(u.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":376,\"./label_defaults\":750}],757:[function(t,e,r){\"use strict\";var n=t(\"../heatmap/attributes\"),i=t(\"../contour/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=i.contours;e.exports=o({carpet:{valType:\"string\",editType:\"calc\"},z:n.z,a:n.x,a0:n.x0,da:n.dx,b:n.y,b0:n.y0,db:n.dy,text:n.text,hovertext:n.hovertext,transpose:n.transpose,atype:n.xtype,btype:n.ytype,fillcolor:i.fillcolor,autocontour:i.autocontour,ncontours:i.ncontours,contours:{type:s.type,start:s.start,end:s.end,size:s.size,coloring:{valType:\"enumerated\",values:[\"fill\",\"lines\",\"none\"],dflt:\"fill\",editType:\"calc\"},showlines:s.showlines,showlabels:s.showlabels,labelfont:s.labelfont,labelformat:s.labelformat,operation:s.operation,value:s.value,editType:\"calc\",impliedEdits:{autocontour:!1}},line:{color:i.line.color,width:i.line.width,dash:i.line.dash,smoothing:i.line.smoothing,editType:\"plot\"},transforms:void 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n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":908}],789:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat;e.exports=u({z:{valType:\"data_array\",editType:\"calc\"},x:u({},n.x,{impliedEdits:{xtype:\"array\"}}),x0:u({},n.x0,{impliedEdits:{xtype:\"scaled\"}}),dx:u({},n.dx,{impliedEdits:{xtype:\"scaled\"}}),y:u({},n.y,{impliedEdits:{ytype:\"array\"}}),y0:u({},n.y0,{impliedEdits:{ytype:\"scaled\"}}),dy:u({},n.dy,{impliedEdits:{ytype:\"scaled\"}}),xperiod:u({},n.xperiod,{impliedEdits:{xtype:\"scaled\"}}),yperiod:u({},n.yperiod,{impliedEdits:{ytype:\"scaled\"}}),xperiod0:u({},n.xperiod0,{impliedEdits:{xtype:\"scaled\"}}),yperiod0:u({},n.yperiod0,{impliedEdits:{ytype:\"scaled\"}}),xperiodalignment:u({},n.xperiodalignment,{impliedEdits:{xtype:\"scaled\"}}),yperiodalignment:u({},n.yperiodalignment,{impliedEdits:{ytype:\"scaled\"}}),text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"data_array\",editType:\"calc\"},transpose:{valType:\"boolean\",dflt:!1,editType:\"calc\"},xtype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},ytype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},zsmooth:{valType:\"enumerated\",values:[\"fast\",\"best\",!1],dflt:!1,editType:\"calc\"},hoverongaps:{valType:\"boolean\",dflt:!0,editType:\"none\"},connectgaps:{valType:\"boolean\",editType:\"calc\"},xgap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},ygap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:s(),texttemplate:l({arrayOk:!1,editType:\"plot\"},{keys:[\"x\",\"y\",\"z\",\"text\"]}),textfont:a({editType:\"plot\",autoSize:!0,autoColor:!0,colorEditType:\"style\"}),showlegend:u({},i.showlegend,{dflt:!1})},{transforms:void 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W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../surface/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat;e.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],869:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../../stackgl_modules\").delaunay_triangulate,a=t(\"../../../stackgl_modules\").alpha_shape,o=t(\"../../../stackgl_modules\").convex_hull,s=t(\"../../lib/gl_format_color\").parseColorScale,l=t(\"../../lib/str2rgbarray\"),c=t(\"../../components/colorscale\").extractOpts,u=t(\"../../plots/gl3d/zip3\");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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n=t(\"../../lib\").extendFlat,i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/fx/attributes\"),l=t(\"../../constants/delta.js\"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function 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E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var m={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],896:[function(t,e,r){\"use strict\";var n=t(\"./base_index\");n.plot=t(\"./plot\"),e.exports=n},{\"./base_index\":890,\"./plot\":900}],897:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 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);\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var 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i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{\"../../lib\":503,\"./constants\":893,glslify:227}],898:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],899:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"color-rgba\"),s=t(\"../../plots/cartesian/axes\"),l=i.strRotate,c=i.strTranslate,u=t(\"../../lib/svg_text_utils\"),f=t(\"../../components/drawing\"),h=t(\"../../components/colorscale\"),p=t(\"../../lib/gup\"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t(\"./helpers\"),y=t(\"./constants\"),x=t(\"./axisbrush\"),b=t(\"./lines\");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top 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r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],904:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":901,\"fast-isnumeric\":190}],905:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],906:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],907:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":901,\"./base_plot\":902,\"./calc\":903,\"./defaults\":904,\"./layout_attributes\":908,\"./layout_defaults\":909,\"./plot\":910,\"./style\":911,\"./style_one\":912}],908:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],909:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":908}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"./render\"),s=t(\"../../components/fx\"),l=t(\"../../components/color\"),c=t(\"./constants\").cn,u=i._;function f(t){return\"\"!==t}function h(t,e){return t.filter((function(t){return t.key===e.traceId}))}function p(t,e){n.select(t).select(\"path\").style(\"fill-opacity\",e),n.select(t).select(\"rect\").style(\"fill-opacity\",e)}function d(t){n.select(t).select(\"text.name\").style(\"fill\",\"black\")}function m(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function g(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll(\".\"+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,\"source:\")+\" \",T=u(t,\"target:\")+\" \",k=u(t,\"concentration:\")+\" \",A=u(t,\"incoming flow count:\")+\" \",M=u(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||\"\",w+m.source.label,T+m.target.label,m.concentrationscale?k+a(\"%0.2f\")(m.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(m.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{\"../../components/color\":366,\"../../components/fx\":406,\"../../lib\":503,\"./constants\":920,\"./render\":924,\"@plotly/d3\":58}],924:[function(t,e,r){\"use strict\";var n=t(\"d3-force\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"@plotly/d3\"),o=t(\"@plotly/d3-sankey\"),s=t(\"@plotly/d3-sankey-circular\"),l=t(\"./constants\"),c=t(\"tinycolor2\"),u=t(\"../../components/color\"),f=t(\"../../components/drawing\"),h=t(\"../../lib\"),p=h.strTranslate,d=h.strRotate,m=t(\"../../lib/gup\"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t(\"../../lib/svg_text_utils\"),b=t(\"../../registry\"),_=t(\"../../constants/alignment\"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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g=\"projection.\"+d[m];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}var v=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");v(t,e,h||p||r,{axis:\"z\"}),v(t,e,h||p||r,{axis:\"y\",inherit:\"z\"}),v(t,e,h||p||r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":955}],960:[function(t,e,r){\"use 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../components/colorscale/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927}],962:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/line_shape_defaults\":942,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":961}],964:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],965:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],966:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":938}],967:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var 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M=p.font[r]={};M.size=(s.isTypedArray(f)?f[r]:Array.isArray(f)?n(f[r])?f[r]:0:f)*d,M.family=Array.isArray(h)?h[r]:h}else p.font={size:f*d,family:h};return p}function x(t){var e,r,n=t._length,i=t.marker,o={},l=s.isArrayOrTypedArray(i.symbol),c=s.isArrayOrTypedArray(i.color),f=s.isArrayOrTypedArray(i.line.color),d=s.isArrayOrTypedArray(i.opacity),m=s.isArrayOrTypedArray(i.size),g=s.isArrayOrTypedArray(i.line.width);if(l||(r=p.isOpenSymbol(i.symbol)),l||c||f||d){o.colors=new Array(n),o.borderColors=new Array(n);var v=u(i,i.opacity,n),y=u(i.line,i.opacity,n);if(!Array.isArray(y[0])){var x=y;for(y=Array(n),e=0;e<n;e++)y[e]=x}if(!Array.isArray(v[0])){var b=v;for(v=Array(n),e=0;e<n;e++)v[e]=b}for(o.colors=v,o.borderColors=y,e=0;e<n;e++){if(l){var _=i.symbol[e];r=p.isOpenSymbol(_)}r&&(y[e]=v[e].slice(),v[e]=v[e].slice(),v[e][3]=0)}o.opacity=t.opacity}else 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=y(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.isDotSymbol(t);return M[t]?M[t]:(e=c&&!o?a(1.1*k)+S:a(k),r=i(e,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:s?A:-A}),M[t]=r,r||null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{\"../../components/drawing\":388,\"../../components/fx/helpers\":402,\"../../constants/interactions\":478,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../scatter/make_bubble_size_func\":944,\"../scatter/subtypes\":952,\"./constants\":982,\"./helpers\":987,\"color-normalize\":89,\"fast-isnumeric\":190,\"svg-path-sdf\":310}],984:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/period_defaults\":947,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"../scatter/xy_defaults\":954,\"./attributes\":979,\"./helpers\":987}],985:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],986:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":936}],987:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":982}],988:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 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_=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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R=y.selectBatch[I]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<O;g++)F[g]||B.push(g);y.unselectBatch[I]=B}var N=P.xpx=new Array(O),j=P.ypx=new Array(O);for(g=0;g<O;g++)N[g]=x.c2p(z[g]),j[g]=b.c2p(D[g])}else P.xpx=P.ypx=null}if(S){if(y.select2d||(y.select2d=n(v._glcanvas.data()[1].regl)),y.scatter2d){var U=new Array(_);for(m=0;m<_;m++)U[m]=y.selectBatch[m].length||y.unselectBatch[m].length?y.markerUnselectedOptions[m]:{};y.scatter2d.update(U)}y.select2d&&(y.select2d.update(y.markerOptions),y.select2d.update(y.markerSelectedOptions)),y.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else y.scatter2d&&y.scatter2d.update(y.markerOptions);var V={viewport:d(v,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},H=s.repeat(V,y.count);y.fill2d&&y.fill2d.update(H),y.line2d&&y.line2d.update(H),y.error2d&&y.error2d.update(H.concat(H)),y.scatter2d&&y.scatter2d.update(H),y.select2d&&y.select2d.update(H),y.glText&&y.glText.forEach((function(t){t.update(V)}))}else y.init()}}).reglPrecompiled=p},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":943,\"../scatter/subtypes\":952,\"./edit_style\":985,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],991:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":503}],992:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{\"../scatter/subtypes\":952,\"./edit_style\":985}],993:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/mapbox/layout_attributes\":615,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927,\"../scattergeo/attributes\":969}],994:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b=\"none\"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&\"circle\"===a.marker.symbol,A=w&&\"circle\"!==a.marker.symbol,M=m(),S=m(),E=m(),L=m(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"fill-color\":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",i.extendFlat(S.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),m=i.isArrayOrTypedArray(f.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var 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n=t(\"../scatterpolar/attributes\"),i=t(\"../scattergl/attributes\"),a=t(\"../../plots/template_attributes\").texttemplateAttrs;e.exports={mode:n.mode,r:n.r,theta:n.theta,r0:n.r0,dr:n.dr,theta0:n.theta0,dtheta:n.dtheta,thetaunit:n.thetaunit,text:n.text,texttemplate:a({editType:\"plot\"},{keys:[\"r\",\"theta\",\"text\"]}),hovertext:n.hovertext,hovertemplate:n.hovertemplate,line:i.line,connectgaps:i.connectgaps,marker:i.marker,fill:i.fill,fillcolor:i.fillcolor,textposition:i.textposition,textfont:i.textfont,hoverinfo:n.hoverinfo,selected:n.selected,unselected:n.unselected}},{\"../../plots/template_attributes\":633,\"../scattergl/attributes\":979,\"../scatterpolar/attributes\":1002}],1010:[function(t,e,r){\"use 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A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":906,\"../pie/plot\":910,\"./constants\":1052,\"./fx\":1054,\"./helpers\":1055,\"./style\":1060,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1060:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1061:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1062:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../../stackgl_modules\":1124,\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../lib/str2rgbarray\":528,\"../heatmap/find_empties\":798,\"../heatmap/interp2d\":801}],1064:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:2}],34:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],35:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{\"./lib/intersect\":37,\"./lib/sweep\":41,\"typedarray-pool\":308}],36:[function(t,e,r){\"use strict\";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=c[e+y+t],_=u[v];if(!(b<d||m<x)){for(var w=e+1;w<t;++w){var T=a[w+p],k=a[w+t+p],A=c[w+y],M=c[w+t+y];if(k<A||M<T)continue t}var S=r(g,_);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u):function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=s,p=f*s;h<l;++h,p+=f){var d=c[e+p],m=c[e+p+t],g=u[h];t:for(var v=n,y=f*n;v<i;++v,y+=f){var x=a[e+y],b=a[e+y+t],_=o[v];if(!(m<x||b<d)){for(var w=e+1;w<t;++w){var T=a[w+y],k=a[w+t+y],A=c[w+p],M=c[w+t+p];if(k<A||M<T)continue t}var S=r(_,g);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u)}:function(t,e,r,n,i,a,o,s,l,c,u,f){return a-i>c-l?n?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=u[v];if(!(x<=d||m<x)){for(var _=e+1;_<t;++_){var w=a[_+p],T=a[_+t+p],k=c[_+y],A=c[_+t+y];if(T<k||A<w)continue t}var M=r(b,g);if(void 0!==M)return 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e=i.nextPow2(t);o.length<e&&(n.free(o),o=n.mallocInt32(e));s.length<e&&(n.free(s),s=n.mallocInt32(e));l.length<e&&(n.free(l),l=n.mallocInt32(e));c.length<e&&(n.free(c),c=n.mallocInt32(e));u.length<e&&(n.free(u),u=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;h.length<r&&(n.free(h),h=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,i,u,f,m,g,v){for(var y=0,x=2*t,b=t-1,_=x-1,w=r;w<n;++w){var T=u[w],k=x*w;h[y++]=i[k+b],h[y++]=-(T+1),h[y++]=i[k+_],h[y++]=T}for(w=f;w<m;++w){T=v[w]+(1<<28);var A=x*w;h[y++]=g[A+b],h[y++]=-T,h[y++]=g[A+_],h[y++]=T}var M=y>>>1;a(h,M);var S=0,E=0;for(w=0;w<M;++w){var L=0|h[2*w+1];if(L>=1<<28)p(l,c,E--,L=L-(1<<28)|0);else if(L>=0)p(o,s,S--,L);else if(L<=-(1<<28)){L=-L-(1<<28)|0;for(var C=0;C<S;++C){if(void 0!==(P=e(o[C],L)))return P}d(l,c,E++,L)}else{L=-L-1|0;for(C=0;C<E;++C){var P;if(void 0!==(P=e(L,l[C])))return P}d(o,s,S++,L)}}},sweepComplete:function(t,e,r,n,i,m,g,v,y,x){for(var b=0,_=2*t,w=t-1,T=_-1,k=r;k<n;++k){var 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e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function m(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORM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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":129,\"gl-buffer\":78,\"gl-shader\":132}],131:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":32,\"gl-fbo\":86,ndarray:259,\"typedarray-pool\":308}],132:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var 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w=c[y];if(w.type.indexOf(\"mat\")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+\"[\"+A+\"]\"),\"number\"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&\"number\"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),\"number\"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,\"uniforms\",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=m(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=g(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var 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-       "/*!\n",
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-       " * @license  MIT\n",
-       " */\n",
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l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var m=i[2],g=i[6],v=i[10],y=m*a+g*o+v*s,x=m*u+g*f+v*h,b=l(m-=y*a+x*u,g-=y*o+x*f,v-=y*s+x*h);m/=b,g/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],m=i[10],g=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-m*v),b=-(m*g-p*y),_=-(p*v-d*g),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=m;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return 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-       "/*!\n",
-       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
-       " *\n",
-       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
-       " * Licensed under the MIT license.\n",
-       " */\n",
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e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return 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-       "        });\n",
-       "        require(['plotly'], function(Plotly) {\n",
-       "            window._Plotly = Plotly;\n",
-       "        });\n",
-       "        }\n",
-       "        </script>\n",
-       "        "
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "1BEZPSNtGwzp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1297dc6e-751c-4562-c3be-cf1ad66fe3eb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install plotly"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "peril_type",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "ItEs2TIcR8fz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e54eac1b-46b8-4a70-c21d-d34a95d835b3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting kaleido\n",
+            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: kaleido\n",
+            "Successfully installed kaleido-0.2.1\n"
+          ]
         }
-       },
-       "data": [
+      ],
+      "source": [
+        "!pip install kaleido"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "zt3VhIayHUvh",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7cc81188-4819-4fe3-c2ba-02a5e8150791"
+      },
+      "outputs": [
         {
-         "alignmentgroup": "True",
-         "hovertemplate": "variable=labels<br>index=%{x}<br>value=%{y}<extra></extra>",
-         "legendgroup": "labels",
-         "marker": {
-          "color": "#636efa",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "labels",
-         "offsetgroup": "labels",
-         "orientation": "v",
-         "showlegend": true,
-         "textposition": "auto",
-         "type": "bar",
-         "x": [
-          0,
-          1,
-          2,
-          3,
-          4,
-          5,
-          6,
-          7,
-          8
-         ],
-         "xaxis": "x",
-         "y": [
-          2084,
-          217,
-          955,
-          403,
-          94,
-          1079,
-          269,
-          464,
-          465
-         ],
-         "yaxis": "y"
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting bertopic\n",
+            "  Downloading bertopic-0.15.0-py2.py3-none-any.whl (143 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/143.4 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.23.5)\n",
+            "Collecting hdbscan>=0.8.29 (from bertopic)\n",
+            "  Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting umap-learn>=0.5.0 (from bertopic)\n",
+            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.2/88.2 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.5.3)\n",
+            "Requirement already satisfied: scikit-learn>=0.22.2.post1 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.2.2)\n",
+            "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.10/dist-packages (from bertopic) (4.66.1)\n",
+            "Collecting sentence-transformers>=0.4.1 (from bertopic)\n",
+            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: plotly>=4.7.0 in /usr/local/lib/python3.10/dist-packages (from bertopic) (5.15.0)\n",
+            "Requirement already satisfied: cython<3,>=0.27 in /usr/local/lib/python3.10/dist-packages (from hdbscan>=0.8.29->bertopic) (0.29.36)\n",
+            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from hdbscan>=0.8.29->bertopic) (1.10.1)\n",
+            "Requirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.10/dist-packages (from hdbscan>=0.8.29->bertopic) (1.3.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.5->bertopic) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.5->bertopic) (2023.3)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.7.0->bertopic) (8.2.3)\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (2023.7.22)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers>=0.4.1->bertopic) (1.3.0)\n",
+            "Building wheels for collected packages: hdbscan, sentence-transformers, umap-learn, pynndescent\n",
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-           }
+      ],
+      "source": [
+        "!pip install bertopic"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7b2yEtZjEVG8"
+      },
+      "source": [
+        "and loaded:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "LuwY5ubtEVG9",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from collections import OrderedDict\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from scipy.special import softmax\n",
+        "from datasets import Dataset, DatasetDict\n",
+        "from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, trainer_utils, AutoModelForSequenceClassification\n",
+        "from transformers import pipeline\n",
+        "import torch\n",
+        "from sklearn.metrics import accuracy_score, f1_score\n",
+        "import plotly.express as px\n",
+        "from wordcloud import WordCloud\n",
+        "from bertopic import BERTopic\n",
+        "from umap import UMAP\n",
+        "from hdbscan import HDBSCAN\n",
+        "from tutorial_utils import extract_sequence_encoding, get_xy, dummy_classifier, logistic_regression_classifier, evaluate_classifier"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "44WiOM3SEVG-"
+      },
+      "source": [
+        "<a id='dataloading'></a>\n",
+        "<a name='dataloading'></a>\n",
+        "### 1.2. Loading the Data\n",
+        "\n",
+        "The dataset used throughout this tutorial concerns property insurance claims\n",
+        "of the Wisconsin Local Government Property Insurance Fund (LPGIF),\n",
+        "made available in the open text project of [Frees](https://ewfrees.github.io/Loss-Data-Analytics/).\n",
+        "The Wisconsin LGPIF is an insurance pool managed by the Wisconsin Office of the Insurance Commissioner.\n",
+        "This fund provides insurance protection to local governmental institutions such as counties, schools,\n",
+        "libraries, airports, etc.\n",
+        "It insures property claims at buildings and motor vehicles, and it excludes certain natural and man-made perils like\n",
+        "flood, earthquakes or nuclear accidents.\n",
+        "\n",
+        "The data consists of 6’030 records (4’991 in the training set, 1’039 in the test set)\n",
+        "which include a claim amount, a short English claim description and a hazard type with 9 different levels:\n",
+        "Fire, Lightning, Hail, Wind, WaterW (weather related water claims), WaterNW (other weather claims), Vehicle,\n",
+        "Vandalism and Misc (any other).\n",
+        "\n",
+        "The training and validation set are available in separate csv files, which we load into Pandas DataFrames,\n",
+        "create a single column containing the label, and finally create a `dataset`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DogoPiqXEVG-",
+        "outputId": "3b6c80aa-d921-4d84-be97-d9f0659c7c72",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "DatasetDict({\n",
+            "    train: Dataset({\n",
+            "        features: ['Vandalism', 'Fire', 'Lightning', 'Wind', 'Hail', 'Vehicle', 'WaterNW', 'WaterW', 'Misc', 'Loss', 'Description', 'labels'],\n",
+            "        num_rows: 4991\n",
+            "    })\n",
+            "    test: Dataset({\n",
+            "        features: ['Vandalism', 'Fire', 'Lightning', 'Wind', 'Hail', 'Vehicle', 'WaterNW', 'WaterW', 'Misc', 'Loss', 'Description', 'labels'],\n",
+            "        num_rows: 1039\n",
+            "    })\n",
+            "})\n"
           ]
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-             0.1111111111111111,
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-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
+        }
+      ],
+      "source": [
+        "# load data\n",
+        "df_train = pd.read_csv(\"peril.training.csv\")\n",
+        "df_valid = pd.read_csv(\"peril.validation.csv\")\n",
+        "\n",
+        "# extract label texts and create column \"labels\" which encodes the peril\n",
+        "labels = df_train.columns[:9].to_list()\n",
+        "df_train[\"labels\"] = np.matmul(df_train.iloc[:, :9].values, np.array(range(9),).reshape((9,1)))\n",
+        "df_valid[\"labels\"] = np.matmul(df_valid.iloc[:, :9].values, np.array(range(9),).reshape((9,1)))\n",
+        "\n",
+        "# create dataset\n",
+        "ds = DatasetDict({\"train\": Dataset.from_pandas(df_train), \"test\": Dataset.from_pandas(df_valid)})\n",
+        "\n",
+        "print(f\"{ds}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ilcy8gKCEVG_"
+      },
+      "source": [
+        "<a id='dataexploration'></a>\n",
+        "<a name='dataexploration'></a>\n",
+        "### 1.3 Exploring the data\n",
+        "\n",
+        "The first records of the training dataset look like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "de1ksaP-EVG_",
+        "outputId": "1a3a8647-f2b5-4fa7-c4de-07ba5de39dd3",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Vandalism  Fire  Lightning  Wind  Hail  Vehicle  WaterNW  WaterW  Misc  \\\n",
+              "0          0     0          1     0     0        0        0       0     0   \n",
+              "1          0     0          1     0     0        0        0       0     0   \n",
+              "2          0     0          1     0     0        0        0       0     0   \n",
+              "3          0     0          1     0     0        0        0       0     0   \n",
+              "4          1     0          0     0     0        0        0       0     0   \n",
+              "\n",
+              "       Loss                                        Description  labels  \n",
+              "0   6838.87  lightning damage                              ...       2  \n",
+              "1   2085.00  lightning damage at Comm. Center              ...       2  \n",
+              "2  11335.00  lightning damage at water tower               ...       2  \n",
+              "3   1480.00  lightning damge to radio tower                ...       2  \n",
+              "4    600.00  vandalism damage at recycle center            ...       0  "
+            ],
+            "text/html": [
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+              "  <div id=\"df-94690951-007c-48c9-96e2-4be2a1fbfdaf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Vandalism</th>\n",
+              "      <th>Fire</th>\n",
+              "      <th>Lightning</th>\n",
+              "      <th>Wind</th>\n",
+              "      <th>Hail</th>\n",
+              "      <th>Vehicle</th>\n",
+              "      <th>WaterNW</th>\n",
+              "      <th>WaterW</th>\n",
+              "      <th>Misc</th>\n",
+              "      <th>Loss</th>\n",
+              "      <th>Description</th>\n",
+              "      <th>labels</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6838.87</td>\n",
+              "      <td>lightning damage                              ...</td>\n",
+              "      <td>2</td>\n",
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+              "    <tr>\n",
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+              "      <td>lightning damage at Comm. Center              ...</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11335.00</td>\n",
+              "      <td>lightning damage at water tower               ...</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1480.00</td>\n",
+              "      <td>lightning damge to radio tower                ...</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>600.00</td>\n",
+              "      <td>vandalism damage at recycle center            ...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-94690951-007c-48c9-96e2-4be2a1fbfdaf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-94690951-007c-48c9-96e2-4be2a1fbfdaf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4bf994a7-a708-4463-9057-3e19404a2c18\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bf994a7-a708-4463-9057-3e19404a2c18')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4bf994a7-a708-4463-9057-3e19404a2c18 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
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+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Axos90P1EVG_"
+      },
+      "source": [
+        "Let's look at the distribution of peril types in the training and validation set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "WwAKZ-KYEVHA",
+        "outputId": "88ac986d-2f92-4f2d-b2e0-135fad450cf2",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       peril  train  valid\n",
+              "0  Vandalism   1774    310\n",
+              "1       Fire    171     46\n",
+              "2  Lightning    832    123\n",
+              "3       Wind    296    107\n",
+              "4       Hail     76     18\n",
+              "5    Vehicle    852    227\n",
+              "6    WaterNW    202     67\n",
+              "7     WaterW    426     38\n",
+              "8       Misc    362    103\n",
+              "9      Total   4991   1039"
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
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+              "      <th>train</th>\n",
+              "      <th>valid</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>Vandalism</td>\n",
+              "      <td>1774</td>\n",
+              "      <td>310</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Fire</td>\n",
+              "      <td>171</td>\n",
+              "      <td>46</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Lightning</td>\n",
+              "      <td>832</td>\n",
+              "      <td>123</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Wind</td>\n",
+              "      <td>296</td>\n",
+              "      <td>107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Hail</td>\n",
+              "      <td>76</td>\n",
+              "      <td>18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Vehicle</td>\n",
+              "      <td>852</td>\n",
+              "      <td>227</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>WaterNW</td>\n",
+              "      <td>202</td>\n",
+              "      <td>67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>WaterW</td>\n",
+              "      <td>426</td>\n",
+              "      <td>38</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Misc</td>\n",
+              "      <td>362</td>\n",
+              "      <td>103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Total</td>\n",
+              "      <td>4991</td>\n",
+              "      <td>1039</td>\n",
+              "    </tr>\n",
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+              "        document.querySelector('#df-9cfecac3-5996-4803-adc8-a8ce6c6cce1e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+              "          + ' to learn more about interactive tables.';\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
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+              "    }\n",
+              "    (() => {\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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           },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "number of claims by peril type"
-        },
-        "width": 640,
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "peril type"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "number of claims"
-         }
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "stats = pd.DataFrame({\n",
+        "    \"peril\": df_train.columns.values[:-3],\n",
+        "    \"train\": df_train.groupby(\"labels\")[\"labels\"].count().values,\n",
+        "    \"valid\": df_valid.groupby(\"labels\")[\"labels\"].count().values\n",
+        "})\n",
+        "summary = pd.DataFrame({\"peril\": [\"Total\"], \"train\": [stats[\"train\"].sum()], \"valid\": [stats[\"valid\"].sum()]})\n",
+        "stats = pd.concat([stats, summary], ignore_index=True)\n",
+        "stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "FKfk4QAoEVHA",
+        "outputId": "7d060915-3a5c-4ffe-f999-5178955afac0",
+        "pycharm": {
+          "name": "#%%\n"
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+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = px.bar(df_train[\"labels\"].value_counts().sort_index()+df_valid[\"labels\"].value_counts().sort_index(), width=640)\n",
+        "fig.update_layout(title=\"number of claims by peril type\", xaxis_title=\"peril type\",\n",
+        "                  yaxis_title=\"number of claims\")\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"peril_type\"}})"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = px.bar(df_train[\"labels\"].value_counts().sort_index()+df_valid[\"labels\"].value_counts().sort_index(), width=640)\n",
-    "fig.update_layout(title=\"number of claims by peril type\", xaxis_title=\"peril type\",\n",
-    "                  yaxis_title=\"number of claims\")\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"peril_type\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rrxncDLlEVHA"
-   },
-   "source": [
-    "Next, we want to see some statistics on the length of the claim descriptions.\n",
-    "To this end, we split the texts into words, with blank spaces as separator.\n",
-    "The text length averages to 5 words and does not seem to vary significantly by peril:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
     },
-    "id": "emd-I7T5EVHB",
-    "outputId": "5f169d71-129b-43ad-ae8a-2f3aa59ee0bf",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overall number of words by claim description: min 1, average 5, max 11\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rrxncDLlEVHA"
+      },
+      "source": [
+        "Next, we want to see some statistics on the length of the claim descriptions.\n",
+        "To this end, we split the texts into words, with blank spaces as separator.\n",
+        "The text length averages to 5 words and does not seem to vary significantly by peril:"
+      ]
     },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "peril_len",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "emd-I7T5EVHB",
+        "outputId": "3fe0942c-18ca-4024-c7eb-5c799d1f1009",
+        "pycharm": {
+          "name": "#%%\n"
         }
-       },
-       "data": [
+      },
+      "outputs": [
         {
-         "alignmentgroup": "True",
-         "hovertemplate": "labels=%{x}<br>words per description=%{y}<extra></extra>",
-         "legendgroup": "",
-         "marker": {
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-         },
-         "name": "",
-         "notched": false,
-         "offsetgroup": "",
-         "orientation": "v",
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-             "fill": {
-              "color": "#C8D4E3"
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-             "line": {
-              "color": "white"
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-            },
-            "type": "table"
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-          ]
-         },
-         "layout": {
-          "annotationdefaults": {
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-           "arrowhead": 0,
-           "arrowwidth": 1
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-          "autotypenumbers": "strict",
-          "coloraxis": {
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+      ],
+      "source": [
+        "# statistics of description length\n",
+        "df_train[\"words per description\"] = df_train[\"Description\"].str.split().apply(len)\n",
+        "print(f\"Overall number of words by claim description: min {df_train['words per description'].min()}, \"\n",
+        "      f\"average {df_train['words per description'].mean():.0f}, max {df_train['words per description'].max()}\")\n",
+        "fig = px.box(df_train, x=\"labels\", y=\"words per description\", width=640)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"peril_len\"}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rXOTLyd-EVHB"
+      },
+      "source": [
+        "To get an impression of the most frequent words, we generate a simple word cloud form all case descriptions.\n",
+        "By default, the word cloud excludes so-called stop words (such as articles, prepositions, pronouns, conjunctions, etc.),\n",
+        "which are the most common words and do not add much information to the text."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "eq952mq8EVHB",
+        "outputId": "bfd3701a-102e-4582-d9a8-01d4dfb8b884",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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eUJlMADA3Ni4t6ZOt848AsCihAHPjmvu+vrVmTPmHCUrd4Fw7S1LrbPtAJDkH\\u002fDKxMnm5ePFxQ6e3jkqg+GjU829dKdERq1feuvY0DDrMiAkQYwICn5pwqQID09L0FVctes659tzqeaFubFxz44bb\\u002f2v9hQKv547L8GnnZr1j4xK+Wbe\\u002fMmRkdb5bgCQCQSfz5nrKRQCAM0wp0tLrVsthgYGWgqj35bUfIFBMcwDI0bKBAIA4HE4c2LjzPH8hgbzgkVX3n1vXq554Y6kIcj0AEIu98kxY83LltUQXTxwXd\\u002fzGIZhGIa5FmU4q62doW962m623QFGQxuvEKQXGscwJwzyCMuQlxWoaga3yWLnKatUJp11hGbojSWnAcA8i2mHcAmOt0BWoq4z0ia0zTkzA5MlXMG6ohPlWpsz8waDytDx1+z69mB9BE64YxjmFBNj0lAqmqHRBgAAYIBRU0qKQRMoGNZPcQjC0ofXuqK0tS1ZzfHFAxKRLvC1avWu3Bzz8nPj2WtK3DY4yVyFplqtOllSgja3uHvYMHPqHzE7NtZcy8VAUYVNjWhz12zMuKbU6wFAwud\\u002fPGs2zzZ1261cuOs6oUTeZCmX\\u002f2hKim0jAACHJO8bPhyNOk3M402KiDQvVyqVto3NkvxbK3qFurlLWtLWKSEhlniIm5t5oVattgTb1e67FzQ2f5BYJ8Id0JLvLmxZzVoXD1x373kM6245BdXrd1zYti8dbcAwDOu3TJp\\u002fdPW30aYCtMFpHMFEnHTCOmewR7ieMgJAonvrObDZuqLjsw+9fX\\u002fq969d\\u002fveTzP9eu\\u002fzvTcc+2FuZNsYn3snu8IgZAUkNBtVD5376Of\\u002fgD3n7P8jYiq7hkDtP\\u002fPrgWzWUYeWpL9+7tvmX\\u002fIMfZ25\\u002f9NzP84+8V69nOeVuVxe3B+sj8HcfhmFO8RcE6yjN+cYjaAMAAFxoOKqjNH6CYLQBw\\u002fqtWwY2V4nZnZerN6H9C9QGg7nUNQGwuE09mdOlpRRNA0C8j0+YuwfSaibici1pzbPl7J3oAcDSlxnB53D8pVLzcpPOpotH151smXVzYXyCuU90j3HhruuEi5WV5oVAmcxef+oJ4RFoqCOCWnLlKqPRtqVZoMym0ot5mlAOQQRblS+X8Jqz8Lo2n0zHHL+7kW6+aSrkohMSAIBlnlWKbYhrFw9cD+x5DOtW67ad\\u002f\\u002fLXwz\\u002f+1Tw2CMMwrL8z6Xbr5S8AdOxMA8ERTEVDGOacIJGnF18aLfUXcdBB9tMDk0b7xNbo5Ieqr24qPX2qLjtE5P184qJPhq0y12PpqIdiZ62KnNyoV\\u002f1RcHhjyekiVQ26Rnsm+A34ffQjE3wHnKzN+i3\\u002f8KGqK3ra9GDsDMezrdrT9e3B+gJcwx3DMKeM9J5WVlawueyHal1Jssd4f2EonxQYaUONvvxS4\\u002fETdbsAYITXFPRpGNZvxXp5J\\u002fkHXK6uUur1+wvy58fFW7fuzs015zpHBoeEudsU3wCA9OrmvrrxPo5qVvpJms\\u002fAytm6GwOAgMuN8rRbs0XQ0vHcSLEPPem0y9XNE2+OtupV3TNctes6J6e+3rwQ72333X3EYneBUK7v5E0OnmWOWba0NbRk2C04BAkAbkKh9fUDh2xeNue4nef43UPc3AFKAaCg0WY8rFl+S9DSv95aFw9cD+x5DOtWF64036fEMAy7DjBUtUH+HADL2UJHkBzBRDSGYQ79N+lFy\\u002fKuKS9blp9PXPR84iLz8hif+DE+NpdmXSTg8B6Om\\u002fVw3Cy0AWB5+Ljl4eOQ4IzAITMChyDBaFnAG0m3IsG25gUPnxfczqhNB9uD9SM44Y5hmFNSvKbnKi9flacer915vHYnABBAMFYnYQPcho\\u002fxntn6BAzr\\u002f5YkDrxcXQUAWzIzkYS7pZ7Mkjbd28Gq0Mf2rCzLPJAOyO10UfcSiTrXTaOLGrRa80KIbSHvHuCqXdc5llfzEYttW2x4iNpJ+zIA6VWVJ0tKsuvqypWKJp1OZTDoTSY9RRna65OO1F5vDpIsQXs6\\u002fe6zY2I2XLsKAH+kXVqRNMRNILBu\\u002feZsc4316VHR1nGzLh44V+15DOsV+cW1DU0aNIphGNZvGVSfMbTdicoJTjCHP5rkDSJIb4KUAZDA6BlGwVA1NFXBmPJoUw5DN5C8IbiAO4ZhNyyccMcwzCkkQa6MePp03d7jtTvqDdUAYMm2e\\u002fJ9x\\u002fvMG+czhyRwlSrsurIgIf7tY0cMFHWsuKheo\\u002fFuSQVWqZSpZWUAIOJy58axlHxRGvRoyCGTnX7KopYiHj1JYzRa+k0j8172AFftus7RtBRaETjc8yK2iisWe\\u002fNyPzhxoqizhfVZv0idv+3SlXefHBk1Iij4fEV5jVo9d+2ah0amDPD1EXJ5xU2Nv166eKGiAgD8JdL7R4xEn9nlA+eSPY9hveVcOu7ejmFAmwosHaIJ0pcgWYZDYf0CQzeatBvRKAAAEKQf3\\u002f0trnBmu9WJaVMhMM0dOFwFf8YwDOtHHF3VYBiGWSOAGOsze6zP7EZDbZ2+0kDr+KTQW+DvxfdHV8Ww64K7QDgtKnp3bg5F0\\u002f\\u002flZK9OHmqOb83KohkGAGbHxpkngURY0oKD\\u002ff2TAwJtG1lEe\\u002fWh7j9CLpdouZrRmlgqfXer3t11ljscespRT3AHhVy+O3f2o5PNRZw9hcKJEZGJvr7Bbm7uAqGYxxPxuL9dumTuRd4duvjuBMAPCxbetXXL5eqqCqXylUMHkBUiPT2\\u002fX7CQtax\\u002fFw9c1\\u002fc8hvUinHDHMIYq1da21pYUuL\\u002fDFd9h1Y71JybdDmBYzgAJ0k\\u002fks53gtP8rDwAkNxINdQ3+jGEY1r\\u002fghDuGYR3myff15LNPaodh15kliYm7c3MAYEtmhiXhvi0r09JqWdOapS\\u002f8kICAN6b0s9miSIJwFwrNE7FWKJVJ\\u002fgHoGt2pd3ede0squUHjqE9Wk52qJll1tZ+cOmleXp089LnxEywTjVogdVpcyCXv7ikS\\u002fb1k6U3r\\u002fspvaBBwuSaaJgnCQygc4OM7OzZ2UcKAtq9p1sUD18U9j3UfwvnhFTcquVKbnlGGRvsVfJRvBN19lCn9cTSE9VuU\\u002fhgaAgAAvturTmbbuwP+jGEY1r+wXzV1RZ7830t1H5qXCSCXRJ+zbbdxuvr5Wu35maH\\u002fCjk+aBuGYRiG9baJEZG+EkmtWn2lurqoqTHCw7OwsTG7rg4Agt3cRoeGoU8AAIAhAQF\\u002fXU4HgIyaWrStPxjo53eypAQAzpWXz46JRZu7U+\\u002fuuhgvb\\u002fNCbsscnm2pDYa6lnrliO3Z2eahD4P8\\u002fF6ZPIU1t9F9Jchd8u46k2nl5o35DQ0D\\u002ffx+uelmP4kEXcOOLh64Lu75vsmS3pIrtYdP5RxLza2obqprVAMDMokgPMQ7eWDIvGmDfb2aJ5JFaHXGRfd+p9YYAIAgYN3X94YEeqArsckrql391B\\u002fmZZlUuP2Xh3g8u3MAFJbWHTielXqpqKZeoVTpPd3FAX5u40ZEz5g4wM9bBgBcrt3nWsvKr7r32bXm5e2\\u002fPuTl0fzJUan1uw5fPX2hoLC0Xq7Qcrmkt6fE11s2dGDoqOSIxNhAsmUGYFZanfHMxYK0jLKcgprKGrlKrTeZKLGI7yYTRob6DIgNnDYuPjjAA32arT82nPlp3QkAiAz1+fOL1eZg5w6KWVllU15RTV5Rrfn\\u002fVbWtZY7lSu34xR9brWtj+cIRj66ejEa7n6uOslnXD8rEJZ\\u002fQNOPrJd3y84MAUF7V9ONfJy5eLTFRdFSYz4pFI8eOaJ4ogmFg79FrG3ZeLC5vEPC5A+OCViwaOSSxndm8u76FbVXXKXYfvpaeUVZc1qBQ63Q6ls6\\u002f1oYnhX3x+jI0aiunoPr42bzzl0tq6pSNcg2PR3q4iaPCfEYlR0wdF+8uE6FPcMi1R7kTcDL0ekIbLqAhAIL05YrmotEehD9jGIb1L65PuIdKZ7jxI\\u002fWU\\u002fEr9lxpTFdrcBocQtVv\\u002fC8OwvkNHaUo0uSqTnE8KB7mPQpsx7PrCIYhFCQN+unAeAHbm5DwyKmVPXq65afGARHuJovFh4RyCoBgmvaqyRN4U5u6BrtG3jQsNMyfct2ZmPD12HGvZnG7Su7tuaGBzv61ShTyvoSGGrV7N6bLS1tmibZXJ5eaFEUHB9j4b58sr0JCLuOTdf7144VJlJQB8Pnuu89l26PKB6+Ke75tEQh4A7DuW+elPB1Rqmxr3Or2xtkF1\\u002fnLxHxvO3LF41F23jm07PbJIyJs9eeCmXZcAgGFg2770R+6chKzDateh1qpB08cn2Mu263TG79Ye27I7zXyfxqymXllTr7ycWf7rv6fuXzFh6fxhblKWCkKO1darzAn3vUczPvvpoErT+m83mqiyyqayyqZLV0t\\u002f\\u002fffU5h8f8PORtT7TSnpG2cadF0+czzcaKaRJodIpVLqyyqbjZ\\u002fN+\\u002fOv4zIkDnrhnqjPZybLKRoqiORyy0wfFbPXTf7Sbb+0jXHuUXXtQahtUKo2+sUnz4Et\\u002fyxXNQ1vSM8rSM8qeuHvq0vnDAOCTH\\u002fdv3ZtubtLpjKfO55+5UPDq\\u002f+ZNH59geR1rrt1CM4PR9M0fR7fuTacol5W0Kilv+GbN0ZPn8q2DBiOoNYbyqqbjZ\\u002fO+X3vs9kWjVi5OcXxHysy1R7mzaMpwCo1h\\u002fRND1zM0y81vjnAqAPsPSo\\u002fAnzEMw\\u002foZ12e6BRwvP9GoUOkMPscdbWtjjP8H88J3CDksl1UYhvU1Jsa4vfy3N67d83PB2\\u002f+UfLW78i9L04HqjetKvmwy1FmtjmHXiSWJA80L5lT73rw888Nb7NSTAYAAqXR+fAIAUAzz0oEDrp3YswcsGzTYXDmkUad78cB+62v47ta7uy7K03OQn595+avUM7aNzX69eBENtbCUW6nXamxbmm3OzOjcdKbOcMm7nygpAQAuSUZ4eqJtDnXxwHVxz\\u002fdNErHgz02pb36+E0nsWjOaqN\\u002fWn\\u002f7ou31oAwAA3Dw72bK86\\u002fDVtjnEtiiK3n+8ueYVAMyfNtiqsZVOZ3zm7U2bdl2y99etN5i++u3wx9\\u002fvd3drPyOJqGtQAcAfG8+89cUu62w7Ijrc1162vbJG\\u002ftgr\\u002fx4+nePMP3nfsczHX13v4I0sjCaqolrexYPSj7j2KHfHQSkqrX\\u002fz852WbLvFV78fLqts2nHgiiXbbkEzzMff72d95e7YQoPR9MRr6zftumTJtpME4eEm9nQXs96PkUmFCdEB08YlTB8\\u002fAG1rcTat6J5n\\u002f0Sy7Qi1xvDT3yeee3ezRmtA22y59ih3Gm28wtBNaBTrnxiqFA0BAADJtXve2wPwZwzDsH7H9T3cMQy7LjHA\\u002fF74QY4SvfIxoxnqUuPxYFHkRN8FaBuG9XOx3t6D\\u002fPyv1lRfq6m5WlN9pboKAEYEBTvuw\\u002fvcuPHHi4satNpTpSXLN\\u002fz74oRJw4OCrFfQmkwXKyoOFRYoDfoPZ8yybup1XiLRI6NSzAXB\\u002f8vOqtdonhk3DpkJs1atPlNWmlVX9+y48dbxruvdXffgyFGP7twBAP9lZ0V4eDw+egynJauiMRrfPXb0TBn7hSgADPb335hxDQAOFhTk1tfHejeXSQEAmmHWX7v6+uFDrWu7mkve3Zy1N9H0u8eO3jV0WKBUyiGd7ZzRxQPXlT3fN51LLzqe2nx\\u002fLi7Kf+zwqEB\\u002fd5GA1yBXX7paevxsHk03J8j+O3Bl9LCoSaPR8k0RId7DB4dduFICAHKF9vDpnJkT7WbxzM5cLGyUN99xiQ73jY9mn9X8na92p1mVHff3cZs2Pj48xFvA5zY2adIzy06dLzAYTdv3Xy6paOcmTVt1jaqdh67+9PcJ80MfL2nSgGAfT6lQwG1SaguK6zLzqiiKHjciyvZ5rQL93CeNjj18OsfycHBCUEyEn5eHWMDnNim0GbmVh0\\u002fnWLqZ5xfX\\u002fvz3ySfvbX\\u002fygDWbzuw9kmFe7txBAYA1n622rAYASrXuvueay+nIpMKfPrA7iZ9M2s4kCq7l2qPcHQflp79PZOZVDU8Kmzkx0WSi\\u002ft56rryqCQBomlm37dzJ8\\u002fkAMCElZlJKrFZn\\u002fGvLWXP1HpVGf+hE9sKZSbYv1i1b+Pkvh65kNQ8M8nQXP7hy4uTRcRIxHwDUGsPRMznfrz3W0NT8Fzd2RPSHL91seS6rc+nFz76z2ZK+F4v4E1NiB8YFustEBqOpvKrp5Ln8nMIac+uZi4Wvf7rjg5cWs+X2m7n2KHcarvVxPWEo9h5UJDccDfUg\\u002fBnDMKzf6Z2Ee6lq75nqlywPF0Ue4ZE2PVxy5X+n1X2yMGJ\\u002fsXJXgWKz2lQp4vr6i0YN8npUwPGwXrNWezGr6dd63RWKMch4oRGyhbEetxPd0HMfw25w5xsO5yjT3Xie84NWRUoS38l4wLp1gNvwA9UbMxUXccIduy4tGTjwak01AHxy6qQ5y7JkYHO3d3sCZbLv5i+8d\\u002ftWpV5\\u002fsbJy6fp\\u002fPEWiSA8PEY+nNZpq1epypcLcH21KpN2sU6c16XT1Go3SoFfqDUqDXqnXV6lU5qaTJcU0w0j5fBlfIBPwZXyBv1TadibMh0el5NTX\\u002fZedDQCnSksW\\u002f1PiLRaHu7uLeDyN0VimUNSq1QAwwNe3bcK9TqNp1GmVeoNSrze\\u002fu9ZkMjftzcstljeZ313K58sEgiCZjGub0u3dXTc3Nm5ObJx5ptyvUs+sv3p1VEiwhMevUavPVZQr9fo4b59ITw\\u002fLQAdrNyUM+Pz0qUadTmM0Lvh77ZzYuGgvLy5BlCkUx4qLyhQKCZ\\u002f\\u002fyKiUz053y5hol7z7skGDjhQVAsBvly7+dsmmR7mQy\\u002fUVS5IC\\u002fJcOHDQxPMK6yayLB64re75vOnomFwCkYsHLj82ZkBJj3bRk7rCcgupn3t7c0NRclX7t5lTW3O7iOUPNCXcA2Lo3rd2E++4j1yzL9rq3nziXb0lKAsBNM4c8cc8UPq\\u002f1S2Dp\\u002fGGVNfLXP91xLacy7VqH73OkXio6m1YEABGh3o\\u002fcOWnMMPRwqzT6I6dyEuNs7uEhViwelZZRNnfqoJkTE6PDfZDWm2cnP3jHxBff35qRW2mObNuXfv+K8WIR33ZF1O7D16DLByXI3936oXUHbZIgnCy139264yi7\\u002fKBcuFIyYVTMu88vMieURw+LvP3RX\\u002fUGEwBs25cOAMvmD3\\u002f87inmlZMGBN\\u002f5vz\\u002fMy+cuF7dNuIOrt9Dcy968LBLyvn3nttCg1qE\\u002fEjF\\u002f7tRByQND7356jbmn\\u002fKnz+QUldVFh6PtaNMo1b32x05JtnzYu4ZkHpstsK73cs3zc3qMZH3y7z2A0AcCpCwUbdl5YNn+49ToW3XGUO4fSN99gw64DDMM+Tg4IR5NbdDf8GcMwrN9BL7B7hr94zKSgHwxUU0bjj3KD3fF0p6tfaNRnhEimhXBn1OkuFii2yA35U4N\\u002fs6xQotpztvoVKS800u0mDiGo1V5Ir\\u002f+sTpc2NuAjAPs9AZymVesXRzyBRp0wfv7Ql3+zSUf2ioyz+X9\\u002fvDPrYpFea\\u002fAL9px404jbnp7LF\\u002fZcKd7edWhD6kcPt35aLL45\\u002fHLUoFA0irXnQsNRALg19NFYGcsVjjffHwAaDNVoA9bb+vv3WB+xMD7hnWNHjRR1tKgIAERc7rzYOHSlNkYGB29Zfvuz+\\u002faYi2I3arWNWnTgPAAESF1\\u002fAfPGkcPbsjLRKAAAbMrI2JTR3MHT7MOZsyxlcywIgM\\u002fnzBvg4\\u002fv12VSN0QgA9RpNvQa9BmP9rb1\\u002f+7a0quZ0BuLHC+eRyPplt44ICkaCvbjrAODz2XNIAnbm5ABAtVplvutgNtDP74cFN+3IyWZN+7oJBN8vuOn+7dvkep2BopBDECCVfT1vXry3zxdnTtsb+98VLnn3yRGRC+IT\\u002fsvOQhsAdCZTqUJeqpDvzMlZMnDg+9Nntq2o0MUD1+k932dxOOSnry5hzSzHRfm\\u002f\\u002fNjsp9\\u002faZH6YmVdV36j29kTr5k8YFePnLaupVwLA5czywtK6yFC76TyFSmepU8HjcmZOYs\\u002fO\\u002f77+tGV5\\u002fMjoZx6Y0eZIQqCf+6evLl391B+VNXK0rT3HUnMBYPjgsPdfvNlcyB4hFQvmT2e\\u002fGWCREB2w9ecHORy7fWi8PSXvv7ho2UM\\u002f6\\u002fRGADCaqPTMsrbJ\\u002fba6flD6he44yt1xUFYvHWPZMH8ftwmjYg6caP7+EQp49942zrJmdLhvVJhPQUkdAJSU11vi1ly7hYdOZVuGMiyeM9Q6224R5O++ZN6w3zc07+1DJ7MdJNx\\u002f+vuEpTv8hFExrz81v+1BAYBZkxINRuqDb\\u002feaH67ZmLpwRpJQwPKn1B1HuTMYLWW8gAaxfqx5FAiCIMRoqMfgz1h\\u002fcOhC7g\\u002fbT5fXNgV4ud05Z+SCcejFBYbdaHon4c4n3fxEIwCgULnVQcK9Xndlesgad35z15LjlY9VaU7V6y57C5MAQE81Xah9x0c0dFLgdwTRPH1HavX\\u002flah2V6iPBkkmt7zMDerq6dwXb\\u002fnc1FLEsKKw9p\\u002fPd+ekF7\\u002f972NE23MxDGtPpa6YRwpYs+0AIObKOARXaWxCGzDsuuAhFE6LjLJMlzorJlbCZ+8Qh4jy9Nx0620nS0r25OWeKy+vUauUer2Ay\\u002fURi6M8vUYEBU2Lio73sXtx3rsIgAdHjlo+OGlzZsapkpKsutomnU5vMgl5vACpNMbLe3RIyLy4ePRpLtKLu47H4Xw1d\\u002f6SxKL1165eqqys12pkfH60l9fC+IRlAwfxOBzWKT3NRgYH71115++XLh4pKiqRNxlp2kMojPDwmBoZdUfSEPPHJs7bJ6uuFn2mK3Tx3demp396+mSTTschyXB3d3+pjNcy+MBI00q9vrCpUW0wAMDGa9cG+vrdmTzU5vkA0LUD15U93zfdMmcoa2LXLGVoZGiQZ2lLnYeMnEqkzzUAkCRx08whP61r7tm3dW\\u002f6\\u002f+6dZrtKqwPHM42m5hO\\u002fcSOjWSeELCipy8qvMi+TJPHo6in2zgolYv69t41764tdaIMTPN3Fbz69gDXb7jwHaVMzLw\\u002fJpNGxe48230EsLmuwlzm11vWD0vd131F27UGRiPlI1aNBCUGWhPuwQaFI3\\u002fPI0OaEe3Wt0jpuzYVbeDmzzLI8bkS0VYuNscOjLAn3zLzm3d5Wk0Kzp6WcEZ\\u002fHffr+6fYOCgDMnzb4ry1nyyobAaBJoTlwPKvtParuO8odRRlSgTGgUawfszcLi51PWPfDn7G+r7Cy4YUfdphvUhZVNbzx297IQK9BUXZ\\u002fbTHsRtA7CXcnhUpnWrLtABAknlilOaU0lpgT7qWqfSZaE+t+uyXbDgBhstklqt2VmhM44f7bW1st2XaLi4cz0o5lDbXT4wnDHDDQehnPA43aYEiiT3+lYFhXfDu\\u002f8+WSxoWFjQsLQ6P2jQsLK3jyKTTaxoE770JDLT6bPeez2XPQaKd4CIV3Dx1299BhaIN9m5ffhoY6q6O7zoUmRURMiohAowAAMDUyysEB8pNInhs\\u002f4bnxE9CGFrvuWImGAFYNSV41JBmNAhy562405HADOvfuAPDD+XMfnDgOAJMjI9+eOj1IJkPXAKBo+qOTJ8zDFNZfu8qacDfryoHr9J7vg6xnPWU1KD7Iktutb6lkglg4M+n3DafNmfS9RzIeWjmRta8rtNRLMZs3bZBVS6vTFwosy4MTgh3XP5mYEsvncc2lLTpk6fzh3TpDo0VCdIAlc6pU6Wwb2bnkoPRxPXOU7XH+oIQFobfQ\\u002fH3cLMuxkX5WLQAAHu7NHyqNrkupNye3sL6x9egjdYSsBfi1NtU3qqxabBw6mW3ZyRNGxfh4sQ\\u002f0MSMImDQ69q8tZ80Pz18ubptw792jbA3X+sC6G\\u002f6M9X1H01onQTE7cikfJ9yxG1w7XQB6l6fApt8cn+MGAEZaYX7YqM8AgFNVT2\\u002fIH27570TlEwCgoxqsnncj0qr1mVYnYdYuHWMZJ45h7ZLxPBTGRopB7+KYVetKKYYyF5bBMAzD+hetyfRV6hkAcBcIv5ozjzXbDgAcknx89BhzD7fCxuaMJGZPcIAHaw0Ka57urSP0VWr2xJ+nu3jymDjzskqjP9jS\\u002fxdRVFZv6V3r4yVNSY60bW+WXVBtWR45JNyqhYVIyIuLQpOezpg6tnmDu5v1NKTm2t+Oueqg9HE9c5Ttcf6geHqg5XrEwtYu7b7e6BeRpTo5TTOWwRyd4OQWaq3S+jxuawcvBJfbekFNUXYLdqVnlFuWRyW3c1AAICbC17JsmbjVWu8eZWt4Nkusu+HPWN9X3zJhu0WDol\\u002fescYwF+rT3VEFHLTXgzUjrQSAOI87BBz0vFnKDUEiN5qGajlje4PRoq47Z6jHrmPR0kEXGo6cbzic4j0dbQM4VLMFAOLchqANGIZhWJ+XW19nrtQ\\u002fwNfXcbkkAZdLkiRF020n2sUQ0eGt+TJ7rLN4DlJ1i+cM3X+8uSj\\u002f1r3p89hmQ7Xu3j5nykCSZB\\u002f7X1hSb1kOD\\u002fG2amEXFux1NZsl2eeATCoMCURPzrtJR8skuvCg9GU9cJQdcP6guNnOFwoA1p9bd5mjVsb+XBTtcnIL3WQiqGwyLzfI1fYGbVj3are+W4OwzNQKAGHB7R8U651jmcjXWu8e5WaMkTJepk3sdwH7GIoypNGG47QxkzYVMHQdw6iBMQEhIEgZQQaS3EiSN5gjGE9yu6tWHoJh1LThHGU4QxtzGKqYoesZRgOMiSBEQLqRnFCCG8nhDeMIxhCcTo4Y6zDGwNDVtKkEjfei\\u002fvQZu6H5uKM3UP290JumGHaj6dMXS4TDMmFcUgoAwZKpPsLuyvGJJIK1l9+X1yvl9SpFg1per1I0qBQNKnm9Sl6vVNSrFA2qhmpFV074ep69CzAMc2yS74JLjce2lf+qp7VDPJrnsGKAqdAWHqnZlt50ik8KxvnMtX0S1vuuy+8xDMNcyzL9qcrQTqGGndnZFE0DQKx3+\\u002fmdG5zjkhEdMjghKDbSL7ewBgAy86pyCqrjomyGlNEMs6+lRAYAzJvKXk8GAJRWXbZ9ndhCDztJRgcc5Bw7QanSXbxaml9cW1zW0CjXKNU6lUZvMJgMRspgMHW0RIYLD0pf1t1H2VUHhc+z220cALj2O5W3yyVbmBAdkJHTnCW\\u002feKXU3nzFl66WWpYTYuyO9bQuUPPQS39btbTPaKI0WgNS0b67jzIrhm6kTZm08RptzKRNGbQxF4B9Z+rlL+vlL6PR9vDEq\\u002fjub6HRjiJaRzAAAEPXG9V\\u002fmDTrGLrGOt6M0TCUhqGqaWMaaLcAAMmN4opX8MQrgHDBHmNFGVJNmr9Mur3AsIyhYRgVUCqKqgBDqgn+AQCSN5gnXs4VLUX+aZ3CMHQDQ1UzdBVDVdEtCwxdzVBVDO2oZ562rmO1CgWe33GFHb427B+fMayNSUOjv9lywlJVhs\\u002flzEpJsF0FuxF9tem4VmcEgBUzhwf7uqPN17s+nXB3zEswoFi5o1Z7vvsS7gDgHejh7bAi3q3xTysaWDod9C7vAHeCJFg7ufvbOVnE+qDy\\u002fGrLj5aXn5vEpVewHRUgDLs5+L7NZT\\u002fuqFizo2INANTpK1+8vJxmaAAgCc7S0Ic9eDj\\u002f0hf10+8xDMN6TLSXt5jH0xiNV2uq16an3zGE5cxKbzJtzLj27rGj5oe3DmLpZI1Z6+KUoYjFc4Z+8O1e8\\u002fLWvenPPTTTuvV8enFtQ3M32yGJIQ46mKs0esuyM1sosirx4SRnXtYZZ9OK\\u002ftp6Nu1aGUXZm8Gvw1y1bX1c9x1l1x4UxxOccjrVSciFWzhjQsLm3ZfMy\\u002f9sPz9rcqJUjKY71RrDuu3nLQ+njGXvHG0wOpvlt8dopMA2\\u002fdt9R9kaw6gp\\u002fWHamEkbM2hTBkM1163qywjCcvuBMWr+NCo\\u002fYlpq0jqDNhUYFG8Z1T8J3N7kCGehzV1DG68ZFG9RhtNog0O08YpefsWg\\u002fIwve54rXtrpyUuNqq8Nys\\u002fs5a97S3\\u002f8jGFtRQR4vf\\u002fg\\u002fB+2nS6vbQoP8Hp8yYSIAEf1KrAbQXmt\\u002fI\\u002fd58zLM0fF44R7fxImnXO14dsc+V9h0tkSXrAlrqMaeKSUQ3TmlOK6IRQLEkdGX0vNQxsAxsxluYrG+qCqkrp7R79mefjoR7fPWz3Rqr0XpHhP9xYE7K78q1STBwAMMOZu0SGiqPnBd0ZJEtEnYBiGYf2BiMt9cOSoT0+dBIBXDx9ck35pTGhYoEzGI0m9yVSv1RY2Np6vKLf0f58bG7d4AP7ObwfXYTKxo2ZMSPh2zVHzBI\\u002f7j2c+cudkibj1XNe6nsxc+93bAQCsOmM4U1iDZ1Wfusdodca3vth1LDUXbQAgCUIiEUjFAgGfKxBwVWp9eVUTupJ9rj0ofVc3HOXuOCjObJvzXL6FgxOCU4ZGpl4qBIDKGvmj\\u002f\\u002ffP0\\u002ffPGJwQZFkhI6fy4x8PVFbLzQ+njosfEBNgabVmMnU1+8+iG45yW7QxQ9\\u002f4CBrt40gpADCM2tD0tEm3G211DkNV6Rrv50nu57u91OkEty3KoPzcqPq20\\u002fluhq7Ty5816bYKPL4iyM50cmIYVaffvfv0y88YxmbqsNipw2LRKHYDS80oRkM3GBcn3CnG0Ki\\u002fZqTVRlqlp5oAoFi5i0dKeKTUjR9prsnOMFS9\\u002foqRVptoldZUAwClqn1CjjeXlMp4YSKus9O58Dnuo\\u002fzeOl39wr6y5WHSWSKuv55qUBgKa7UX5oRvk3BbT4ZuTKv\\u002fb9GLiz8zGW0mFFp0\\u002f9SYpJ6qAYd1zaUjzdVa+5QY6aDHYt9rMtZXaot0lEbAEQUIw7z4zv7ZYhiGYX3TI6NSDCbT9+fPmWg6r6Ehr4F9\\u002fnmZQPDA8BEPjhyFNmDdTCjgzZs66J\\u002ft5wFAqzPuO5Zx8+xkc5NaY7AkGUVC3jQ7fWzNJGK+Tm80L5sH+TqmbVm5x9AM89y7m63LdIQFe00fnzAkMSQs2MvHU2qdWty6N\\u002f3jH\\u002fa3PsYAoBuOct8\\u002fKN20hS88MuvBF\\u002f6urlMAQF5R7UMv\\u002fe3pLg70dycAKmsU1qXV4yL9XnjYbm9osYjP43IsE72++MgsN2nHypWIrW6wmbn8KF8vSIIQA6PTN9xFGVLRxg4yqn8EMPLdXkcbOohhVPrGRyj9EbSh4yj9SW3dXKHXnyQ3Dm3DMAzrS87ghDsa6BqNqeJw+b3WkbM1r5gXhvo8F+N+KwAYaOXh8nus17lQ+655IdHz\\u002foFeD1g3ORYkmTQ95M+sxt8q1McNdBOfdJPwQgd5PyLkdOaW73Vm0OiYD7Y+9ffHO7MuFOp1xtAY\\u002f5vunzprxTh0PayvutgnE+5mHjxvXD0GwzDsekIAPDV23JKBg7ZmZaaWlRU2NjTpdAaKEnK5Ej4\\u002fxM0txst7fFjYlMgoqcNZVbHus2hW8r\\u002f\\u002fnTfPuLFtX7ol4X74VLbe0Nxpceq4BKHD4hIyqdBSS7rBqqi0PQolS4nhbrVj\\u002fxVL2pQg4PG7pi6ZN8xe\\u002f108AQkrlx\\u002flvn9QumkLfb2k371721tf7rK8eKNc0yjXWK9DEDB3yqAn753muK6Lu5uorqXuU3x0QEyEr217h7n8KF8fCEICQOibnux6tt3MqP6N5A3kipaiDU5jGLWu\\u002fnbamI42dBZDVenqlwm91pG8AWgbhmFY30AzzPnM1rvgNyYXJ9xlvIil0RfQqC0Bx6PddWLdb491vx0JhkpnhUrRjgPu\\u002fJgU\\u002f3eQIGaWOCr67fWPo1GsP2BoJv1ENhrFMAzDsO4U5u7+eMpoSEHjWF8QEugxKrm5wEVeUa1l6tQ9VtOlzp\\u002fmsJ4MQESId1FpvXm5sKx+QkqMbTuqtKIRDXWznYeuWJYXzUpeOn+YVSNKqW4tY41ZuPwo9\\u002f2D0n1b6OcjW7k4Jbegxlwz3V0mUqp1HA4pkwjDgjyHJIbMmTIoxOFMOWYJ0f4nWhLu+cW1XU+4u\\u002fwosyIILkHI0KgNmmHY0v2EkABHdyDYdX2eUlJm0qxjrSRDcPw5\\u002fLEkbwDBCSEICTAGhm6iqVLacJoyXHJQa8Ugf4MjmEKQnZoIjTHqG+5ykG0nSE8OP4XkDSVIL4L0BCAYuoGhGyjjRdpwhqGbCxYhGLpR13i3yGc7QXbgg0QQwvaOJgAAA0bW2VwJQgLQgcJETn4A+t9nDMMwJ2QWVSs0LN8kNxQXJ9wxDHOJ3PRipRN9VXrS9\\u002fmvjfWeneQxBm2woqXUFxqPNhlqZTzPJPcxnvwOnAJiGIZhGObYLXOSzQl3ANh16FpclH9dgyo9o7kDUViw1+CE4Na12SRE+x85nWNevnCleNUtju6u0DSTU1CNRrtZYUsOEQBmTmxnqoCCklo01POsulE733W6W7n8KPf9g9J9W7hx18XPfz4EAN6ekjeeXpCcGIKu4ZzhSeEnzuWbl0+dL5g1qZ2NbJfLjzIrkjdUHHAVjVqhTfna2qloFEDg9gpXfAca7X4MVaGXv4AEOfxRPOljHMF4e\\u002flihqoyqr4xav4CsCnH2tzKKI2qb\\u002flur6INTjAo3rTX157kD+NLH+cIJtt8ibTgAQDQJu1Oo+oz2tT8ybHGUBX6xkeF3v+wPp0VT\\u002fokT\\u002fokGm3DpN2gb3oGjQIIvTeSvK5+btvqd58xDMOcgQu4A064Y1jfdPFon6snU6DKCBJGJHmMoRlKaWoScsQC0qaDgMok\\u002fzLn+SZj8wXP3sp\\u002floc95jhBj2EYhmGY80YPjwr0dzdP0njoVPbj90w5eibXkuOd53i6VAAASBka+f3a4+bltGtlVbWKAF8321VanUsvUqh6tHcSzTBancHy0NPdUVdEpUp35kLz7YdeJBELLMsarYGiaE5vT83q2qPc9w9K921hfnHtl78cNi9\\u002f8NLNCdHsc6I6Y8qYuG\\u002fXHDUaKQA4fDr7nvKxYcFe6Eod4dqjfN0i+AK3N7hidOg8guAE8N3f4gin6BsfZe1MbdL8w5c9A4QYbXCI0u0zatagUQAgeAK3t7ji29A4iuSKFnBFcw3y11lfhzKcMarX8CR3og0YhmG9rYcLuN\\u002f\\u002fz1Y01Fk\\u002fLl+EhjoLJ9wxrC9KO5qFhvqAAnXGr4Xv5SjTaIYmgIiSJt4UfHeAsHka3sM1W5uM9R58n0S3EXX6yhxl+r+l30RIEtx4nrYvg2EYhmFYZ5AEsWjmkO\\u002f+PAYADU3qzNyqE+fymptIYvbkgTZrs4mN9IuN9MstrAEAiqK\\u002f\\u002fv3I288uRFcCAACKon\\u002f46wQa7WYkQcgkQktmsLSyMSSQ\\u002fSyCYeDzXw6Zq3z0Li6HdHcTyRVaADBRdHpm2bBBzadGvcW1R7nvH5Tu28JDp7JphgEAHpcTG+GHNneEj5d0wfSkzbsvAQBNMy9\\u002fuP3rt291lzm6N2BmMJr4PJZrdtce5esTwRd6\\u002fcHhj0XjdnAEUwUen+sa70MbABhGbdLt54puQhscYDR6xWtoEAAIodBrLYc\\u002fEo3bxeG7vwWkm1H1NdoCYFR9yhUvbq8kC3YDKaiov5RTnl1ak1taW69QK9R6jd4gEvAkQr5MLIwK8ooN8R0WF5Ic2854uH6Bppkz14qOpRdkldSU1zaptQYGQCzk+XvKIgK8BkcHjkwIiwnpVDEoAABQavT7z2WfvlaUV1ZXL1frjCaRgBfo7ZYQ5jc+KWpScjSPy0Gf03EKje7opfwL2aV55XVV9Uq1zsAwjETED\\u002fZxTwjzTxkYNiEpivVXwCW6aR9WNSiv5Fei0e50JM\\u002fZW+k9qbsOG4ZhnabXGjLOsowc7HUV2qIKbZF5mQEmX3Xtu7xXn4z7yFw6Jlt5CQDujnzRnILfWv7Lqbo95xoOTfO\\u002fpfUlMAzDMAzrgvnTB\\u002f\\u002fyzymD0QQAqZcK0zLKzPHRQyO9PSU2q9qxeumYlz\\u002fcZl4+cjrns58PPnrnZB7P5qJRodK9982eztWg6KJB8UGnLhSYl9dsPDNicDiybQCgUus\\u002f\\u002fnH\\u002fgeN9pXdCcmLI0TO55uWvfjvy5RvLZFKh7So9zbVHue8flG7aQopqHj9iNFHvfr1nybxhoUGeEpHA3nSsjt1z69hT5\\u002fOrahUAUFhad9fTax69c\\u002fKk0bGsQyIamjQXr5QcOpWtUuu\\u002ffHMZ2gwArj7K1x+B+wfOZ9vNOMKZXNEik3Yr2gBA6Y90KOFuVP\\u002fEUBVoFEDg\\u002fk5Hsu3N+LKnKMMp2nARiTN0k0n9K0\\u002f6BBLHbjQFFfX\\u002fnbx25FJeaU0T2gag1hrUWkNNoyq\\u002fvG7\\u002fuRwACPH1WDY1efm0oSTZsa8zg9E09qEv0Sibv19bGRfapQKzZzNKHv50o+XhtvfuCfZ1tzw8eCHnq40nymqbLBEzuYqSq3Q5pbX7zmUDQHyY31+v2i099N\\u002fJa2\\u002f8tte8TBLE0a8fFQl4AGAwmn7bdXbtvgtavdF6fbXWkFdWl1dWt+NUhpeb+KFF426eONh6hQ6pqFP8vOPM7jOZRhNazEqu0slVuoyi6s3HLsvEguXThq6cNUIs5COrtasH9iEAmCi6qLIhp6w2t7Q2p7Q2p7SmUalF1rn3g3+RCKvDXz4isxo42N\\u002fhhDuG9TlXTucaDXbn7eldg91HT\\u002fVf7MX3UxqbjtftSK0\\u002fcKhm8y0hDwBAo6GOJDj+wlDzmlP8bj5VtydPdQUn3LEb1h+pF9\\u002fdd\\u002fTTxXPnDYxH29g8sWlnalHpfw+s9JU6lTVDrD2X9tae5pHvHILI+L8nbZpveOM+\\u002fUHM5+9\\u002f9C60AcP6FXeZaPqEhF2HrgLAtn3p5goVADBvmrOXfJNGx05IiTme2tw1ftOuSyfP5U8bnxAe7CUU8ORKbUZu5fHUPHMv4GnjEg6e7ECCsusWzEiyZE6vZFXc\\u002fcyft8wdGh\\u002ftLxHxtTpjeVXTxasl+49nqjUGAJg3ddDOQ46K\\u002f\\u002faMhTOSLAn33MKaO574bc6UQdHhPgI+V28wKZS6Brm6tl41dWz86GGRtk\\u002ftLq49yn3\\u002foHTTFk4cFfPXllRz1aa9RzP2Wk1QbEYQIBbxPd0l8VH+E1JiJo2OddDh0d1N9P6LNz\\u002f00t9anREAauqUr37yn7tMNDA+KMjPXSLhG42URmuoqlUUlzeYK0cBgIOJGVx7lK8zHOFMrmgxGnUCT\\u002fIAe8LdcA4N2ccwaqP6VzQKwBXO4YqWoFGncARu72rrZqNhAKPmb570EZzYuWGdyyr5bdfZsxklaINDZbVNn\\u002f57ZO\\u002fZrI8eXuDn2T9GSFQ1KMzJYoqm3\\u002fvz4NbjrXNlOxDs05pfdoxmmJzS2iExQTWNqqe\\u002f3pZZ3M59ygaF5p01+4+n579z\\u002fzxzmr5D1h9O+3LDMZ0TaR+lRv\\u002fTf2f+O3ntzXvmDIvv5DwiZt2xD+\\u002f\\u002fcP2Vgsq29wx62GMTR6MhW18dOwMAvlLJ8mHOnjB3Hf5exrA+59KRPlfA3cxbEHB7+JMcggMAIo7klpAHyrWFOcp0c6uR1gs5IqJl3h53npcH36dWz9KzA8MwVgzDiHg8onMd5wDmJMZF+3g1aXUfHTxeJVeizb1k17XsQzkFH988B23AMKyzFs9JNifc61vmV\\u002fdwE48bGW2zkkOvPjHvqTc3XskqNz+sqlX8teWs7SoAAHcsTlk8J7mHk3QTRsVMHRd\\u002f6GS2+WFhad3HP+y3XaXZkrnDnrx36vnLJdV1CrStZ6UMjZw+PuHAieYdVd+oXrs51XYVAIDE2EA01J1ceJT7\\u002fkHppi1MjAt84ZHZX\\u002fxySKNtrRFvjWFArTGoNYayysaDJ7NCgzxf+988B6XeYyJ8f3h\\u002fxUsfbC2rbDJH5ErtqfOdH9jqwqN8fSH5shfQmHNIXiLJS6SN6M0VhipjGDVBONUlwqTdxNBNaBQInuxJNOY0kjeAwx9OGS4gcYaqovQnOYJJSBy7QWSX1HY0225xrbDqsc+3\\u002fPLCrVJRP+hTXFWvBACaZp779r+jac5+bY5P6sB97szi6jB\\u002fzwc\\u002f3lBS3Yi22XEsveDRzzZ99\\u002fQS56u+0Azz\\u002fp8HNx+7jDY4VNWgfPjTjS+vmrFgXPv1A+3pjn2YX1HX69l2AHhs4hg0ZMuccPeXSdtd04Wc\\u002fUxgGNZjLvbVhHuYOMacbbeKxKbWW1\\u002fP2CQK3bheFdq+WEsLw\\u002fqmL5fMR0Md4S0Rj4kMA4CfTp3vOwn3QzkF1UoVGsUwrAsSogMSYwMzcluLY86aNIDLVpXCHpGQ9+mrS77548j2fZfNJaoRQiHv8bumLJyRBAASMd\\u002fcL7jHvPLEXHeZaMueNLShhUjIe3jVpJtnJwPAwPhAZzKn3e3lx+ZIxIJt+5p7IfQFrj3Kff+gdMcWZuRUXrxS4nzJhdKKxidf2\\u002fDtu7dFhdmteBsV5vPzRyv\\u002f2nJ28+5Ljvc5AIQFezmeDNm1R\\u002fm6wRFMJrkduAeJ4PBGtE24AzAMVUpwE9AwG5OmtYaDBUcwiXTu6fZwRUvaJtwBgNIfxAn3G9biiYN\\u002f\\u002fu+MSqu3DoqF\\u002fLhQ35hgH0+ZSCYW6Aym2iZVel5FTmmt9WoAkF9e98WGYy+vmoHE7eFwyAXjBjYptU0qrVyta1JqlVod23eP61U3KgHg43WHkUyxl5s42MfdQyricMgmlbawsl7eMqUHQcC4wY6SxYjL+RXH0wuss+2RgV7D4kO83SQSIb9eoSmqrD99rRjJL6fnVbzz54E37mYZgMLqo78Ptc22cznkiITQqCBvH3eJiaLrFZrc0tq0vHKabt25Jop+6\\u002fd9XA45Z\\u002fQAq6d2QA\\u002fsQ8waTrhj3Y4y0dkXC6+cyi24VlZZWNNQLVcrdAa9USDiu3lK3LwkgZF+yRPih0xICIrsUpGvrqApOuti4aUjmYUZ5WV51Y01Cp1abzJRfAFP4ib0CfIMjvaPHRKWPHFAxIAg9MmuYzKYctKLizKbe6n0KUKOSEtpkKCO0hBAAgDFsNzVJAiCBpZ4t9Kq9WlHMy8dyyrKrKgorNEodXqtQSDiS91FQZF+4QlByRMTkicmiCQ9dBufMtE5l4quns7Nu1JaWVRbV96o0+j1OiNfwBNJBX4hXkFRfvHDIoaMT+jWj1b\\u002folXrr53JvXwqtySroqKoVl6n1KkNJhMlFPOl7mK\\u002fUO+QaP+EEZFDxscHhNu9oMX6AophTheWRPl4oQ0YhnXN4jnJ1gl35+vJWIiEvGcemLF4ztB9xzLPphXV1CnVGr2HmyjAz31iSsysSYleHs0dOcODva3fqwfwuJyn759+8+zkHQeupGeWVVTL1Ro9h0N6uokjQr1HJIXPmTLQ011sXnlQfJClX3Mv4vE4zz4445a5Q3cduno5q7yiSq5S6zgcUiYVusuEESE+sZG+o5Ij0Kd1Mxce5b5\\u002fUFy7hSaK\\u002fuSH\\u002ff8daB5u7+UhHjY4zN\\u002fHTcBvvXymacZgNDU0aQqKa3MKa8xBlUb\\u002f6Y8Hvn57uWW1tqRiwQMrJqxYNOrk+fzzl4uz86ubFBqFUkeSpFjE9\\u002fWWhgV5DYgNGDkkPDq8\\u002fYsjFx7l6wZX3KWCliSP\\u002fRuVoWrAiYw5Q5XSRpZ7b1zhLDTUQSRvKBoCAABKfwoNYTcMsZB\\u002fy+SkP3afA4Awf89pw2MnJkcPigxkHTGbUVT1\\u002ftqDGUU2xVL+O3ntrrkpQT5u1kF7OCT52l02n2SaZprU2qyi6se\\u002f2GIdd7mqeuX+cznrD6eZH0pFgtumD50xMj4qyNtmPYDyWvnJq4V7U7NMFO3t7tSoFLN9Z1t\\u002fF1ISw5+6dVJ0MHqxqVDrvt50AsmY7zyVMX9s4siEMOsgq0MXcjcctvl+4HE5q+eMXDFzeNtxBo1K7W+7Uv85eMmSdqcZ5q3f94UHeCVG+Nuu65Tu2Ic\\u002fPruMsrorYNag0Dz62SbryGt3zYoPa3\\u002fucUnH69T3ZQTTM3ejrl+3xj+taGgez2sxfv7Ql397AAm61mu3f3N2v1MVlxC7a79HQ92msqh224+HD29KbbuLui55YsJ7m55Eo1YObUj96OHf0CjAj6deD41tHekpr1du\\u002f\\u002fnInj9PNLSUSnQsONp\\u002f3uqJc++cIBC54LtA0aAuvFaWf7W04FpZ4dWykpxKU0sxVpdYcM\\u002fkh993dMbvvG\\u002fzXinT5D8e9755TlQAaDLWf5r9Px2lXR72mBff79u8Vwgg3k1aZ+kF\\u002f2HW42qT4o1Bv1tepHO2\\u002fnDwh\\u002f\\u002fbYB1JGhf3wdanrCMAUFVSt+mb\\u002ffvXndbbGfZrIZIIpiwZteyJ2f6h6E+LC5XmVu349ejRLefl9U71NQ6NDZixfMzcOydIWq4Ge0xvfY+1dfV07q4\\u002fjp\\u002fcecmgM6JtbGKHhM26Y\\u002fyM28bwO144zx6V3jDu0x\\u002fCvT22378SaZr73R8VcuXppx8Q8Zrf7lxJ+fcnUtPLqvQmU7i3x+IhA+9MGcZpOcM113D\\u002f4pZ51UrVuguXy5sUfjLp2Kiwp6aM8xSLLC+781r2U5t3WR6ee\\u002fZhNyF6vgUAaoPh9zMX92XlFTc0MQzjLRUnBwf+b8q4UE+0rN7in\\u002f\\u002fOqqqxV8Pd8TY7b\\u002fuVzHUXLhfWN2oMRm+JOMHfd1HSgFkDYs2t6y5c3px2LaemTmdCSxNmvPwEh2zuimuuO3\\u002fu2YdLG5s+PnjickUVzTBR3l4fLpod7eMFdurgm\\u002ffYSzMn3ZkyzBJsd\\u002f+0reH+zt4ja85eWjky+f9mT7EEMaxfOHAi6\\u002fVPd5iXE6IDfv7I0WRWGIZ11Ftf7LJUbL\\u002fv9vErbh7leBBJRk7l\\u002f97cYOk\\u002fvuG7+wL90V\\u002fn6xJtytfWTkWjAAL3d7ji7v1eMmm36JueRKMAABxxQDpByNCw0yj9IV1D69mChcDzO65wLhptw6T5Sy9\\u002fCY0CiP3OEJwu1pWi1FUDgdGiYSAlAdeAcM0VhEm7Qd\\u002f0DBoFEPnsJnmJaLT79eJnrL+ok6s\\u002f+\\u002ffozRMHj0gIRdvaMBhND3+6KS3Xpoff40smrJo90jrSUUVVDUv+73friMsnTY0I8GpQaBQaHQBMSIp6497ZbmJh69pstHqj4+rq1pOmWrtlctKLd0xHo1bWH0778K9D1pGoIO91r6+0XOOwqpOrb33tD0vncQCQiQXfP7PUcSb6YnbZ419stq72Hhno9ddrK\\u002fn2pwyx6IF9yKqmUTn32Z+sIz8\\u002ff2tybLB1pIfFvf0ZAAwK9N98z+1oW7fBPdyxbqFR6n59a8ueP49TJhpt621iWXOei2GYHb8e\\u002feO97Wo52nHbgfL86h9f2bDp2\\u002f2PvL98zNxktLk9WpXu3MFrhVfLCq6V5V8trW8p4Nj3jfKaVqTO+jLnhcEeKV58P6VRfll+WkdpoySJ\\u002f5R8ZS7dThBErjI9wW0YANTpK+v0lWHiOPSFXCH\\u002fapn1Q8pE\\u002f\\u002fvF7n8\\u002f22OwnUbcHq1av+uP4\\u002fvXnV7y6MwVz87ncB39LnZCbXnjL29sOrbtAtPmZq8DpblVv7615d8v9y57bObih2dwee3\\u002fgl5P8q+U\\u002fvTqxvQTjrqbtZWbXpKb\\u002fvdfH+5Y\\u002fcqiGcvHdLr6uTWpgD89IXrH1eycmro4v9ZODdcqa\\u002fLrGhYlJVqy7f9dzXpu655wL49bkgcKedyzxWUf7D92sbTiq6ULrLfj1zMXcmrqZyfGzkmMO1dctv7ildyaun\\u002fuar0TNiE6fM3KJY0a7dfHz+TW1Fs9tVWDRrvij\\u002fUFdQ1DQwJXjRpKEJBTU3eqsOTt+c6OADVzcpvb9fOp8x8dPD4w0P\\u002fWYYMJIEoam04WFEf7eFkS7tE+XsuGDTbR9Ou7DkZ4e947ZoTluWSbM9HDOQWv7To4Lips1aihDWrtsfzCADcpso5jndg\\u002fH+w\\u002fhrPtmGONGu3Y91i6Kby1aMaS4Y6KPPSAPUeuWZbnTevljcH6BZPxismQCiAUSnCKqh1Xssot2faFM5LuXDLatp1FYlzgwhlD1m07Z36YmVd1gyTc+yCSF9+VbDsAAGGnqy9jU7XDHkp\\u002fEg0BEJzALmfbAYBDcoJpUx4aBpo25dvrmI9d93zcJe\\u002fc3\\u002f6tIDM+j\\u002fv2fXMXvfiLiWpN1FzILutiwr0HFFU1mBfmj0189a5ZpBPXfZ3IFAPAwMiAF1Y4yrYDwLIpyVnFNdtPXLVECirqj6cXTB4aY7UW6sdtp62z7SRBfPbYIsfZdgAYFh\\u002fy\\u002foPzn\\u002fxyqyVSWNnwz4FLq2a3Xl45qcf2IWaGE+6Y6+VfKX1z1Xc1Zc1\\u002fzH2NWCYEAK1a\\u002f+mjv5\\u002fYcQltdk59ZdObd35\\u002fyyMz7nltcYdyfAXXyt671+ZeX38xwmtyoTrzXMOhS40nLMGB7iPvCH96Z8Wa1IaDQzzG0Ay9ruTLib4LxBzpybrdAJDoPrz1JVxHLddUl9ab+6crG9Xv3vtT2rEsdKX2GA2mdZ\\u002fuunQs89U\\u002fHvL0s3Ni3XG7\\u002fjj+8+ubtFY\\u002fpR2ilmt+e3vr0S3nn\\u002f3urogBvXkTuMfQFP3XRzv\\u002f\\u002fWJ3p+\\u002fPNdYqPnt8zeGNZ5\\u002f\\u002f4R4Pn65dYgEAwKKkxB1Xs7ddyXx22gRLcPuVTABYlNRcMq9Ro31154ERYcG\\u002f33GLpS\\u002fDM1t3\\u002f3cl61B2\\u002frT41sqhmVW1G++5Pd6\\u002fOXd\\u002f799bjucXXSqrHBrSfN3lJhSmRIQCwIa0q\\u002fYS7u\\u002fuPVJQ1\\u002fDMtPH3jW09GzZSFI\\u002fTgRszzm9zu7ZfzfQUizbcvdzyOhRNG6jWATqjwkNGhYfoTabXdx30k0qWDnWUEHx154Efb1tk3gkAwCDTQTjByf1j+bb+9NDJX89cWDVq6MuzJluvgGH9QkOT+mxakXmZz+POmNDJUp7YDUWn+kGv3UqQnjjh3i7rajM3zRxi1eKIdd12ubJtH2Ssh5A8R6ccziDsdhVHB+2xoo2tOTgLkhOBhjrHzr0EhqoAnHDHnBPgJZs8NObA+RxLpG1t9z4rJsTnpZXTnckUd9oTSyY68\\u002fIPLRq7JzXLYGz9Wth1OtNBwl2h0e06YzM5xMLxg5zs9D0+KWrq8NhDF3ItkT\\u002f3nls+Ldn5mVqt9cA+xMzQXmYY1kXXUvOemf9xn822EyQhFPMNOuOrt33d6Wy7xaZv9v\\u002fwsk2pk+vb0tCH7o9+dazP7EHuo1K8p6+OfH5VxLMcgrMw+K53Bq9dFvrIVL\\u002fFFGPaW\\u002fXPlvKfa\\u002fTl3oKAsd5z0FdxkYIrpQCglmteuPmzTmTbLbLOFz4z\\u002fyOXfGJNBtPnT\\u002f751TN\\u002fdTrbblFwreypOR+e3p2ONlx3NErdayu+\\u002ffuTnZ3OtlukHct6dOo7xVkVaEPHjY0K95VKdlzNssw\\u002fRjHMzmvZgW4yS1J4V0aOxmC8M2WY9cjB+QMTAOBIXqElAgBzB8Zbsu0AMDUuCgCK6htb12iP2mDYlZET4e1pnU0GgA5l26Ej29wuH4lEqdOnlbeWguWQpKXvf0fNHBBr2bHQ8Wy78\\u002fvHvIVfHDn1w8mzONuO9V97jmRYqnlOGRsn7alZSbB+zcjW6xZjVVzeeloYFuxl1eKIxqqkoUzSziB9rPuQnEg01JMYDU2VoEEAghuBhjqFINk7CTF0v0mYYn3BqAE21cblqn5zj\\u002fDJpZM6l2V2UqC327D4EDTKxtdDOmtUa8VLADh+ucBcrYXV1mNXrcvCkARx\\u002f8IxVu3teGjROOuHjUrtoYttB7s4pbv3IWaB9zLmShWFta\\u002fd\\u002fo1Owz7ajiCJyAHB8cMj3b2lEjeRVqVXNKryLpfkpZd0qHA5h0v6hXjHJNn8SDhJLBUSBPHRQ79ePd16e7Artv10KDopdMbyDnxX9msx0sExUru9J\\u002fyEwY\\u002fGvnuidmeTsSFQFDbZd5GQ01qo2rXyr5aNmpn02opvC66VoW0dVFFY+8ryrz7b\\u002fbx59EPnmIzUO\\u002ff8eGbPZbShs7Rq\\u002fVurv3\\u002fm69VTl6agbdcLrUr34i2f51wqQhs6q76y6bmbPnl\\u002fy1ORiU51FrCHQxALByf8cvrCueIycyL4dGFJrUr9wLhRlr4AVyuqAeCR9dutn2hWp7KpUpUY4Gv90F0kBACFzu7ZWFtZ1bUUTaeEO3Xy54Dz29yuxyePuXtt5e2\\u002fr0+JCL1lyMBZibFCbufPKAYFtjOU0jHn949MwF9z9tK3x1NvHTYYZ9uxfspoojbtau0xcMvcoVaNGMaOMmbSOB\\u002fnNOtJzowmSgRO3U6+dK3Usuzvy54VxXoAwenSSWAX0aZiAJaSkibNOpNmHRp1HYalsDuG2RURaHMr0WCidAaT0GpS6L4pMtBr9MBwNOpSE5M7MOR3QlLUfydbS\\u002fwZTdTV\\u002fMqxg9nv+R1Ns8mPD4kJ8vPsQAnNyECv2BDf3LLWn\\u002fL957JnpyRYreKUHtiHmEVf\\u002f4vC7Hnmm9X1VXJFg0rRoFI0qBUNKnm9StmokterFQ0qRb2qoUbu5KyDrsLQzPv3\\u002f6xWsPzY84W8hfdOWfzQdNbCHRqlbudvRzd+s6\\u002fttI0A4OYlGZgSExjhGxTpGxjhGxjp6xfi3emi22KZcM+fJ1j7tnsHuA+ZkBCZGOwf5i2SCIwGStmori6pTz+RnX2x0MEtgR9eXj9i2kBP506sORyOxM1RDpqmGdb+0Xwhj9fxn0CXzOzaIQHCsCWhD6HRbpB\\u002fpeSPd7deS0Xv64qkwiHj4+KHR3p4y9y8pHwhV9mkKc+vuXwy51pqHm1Vq85aSXblRw\\u002f\\u002f+tqfD6MNTvvk0d8dZNsJkhgwPGrg6OjgaH83LylfyDNoDQ3V8pKcqqunc+3dM2Bo5pPH\\u002fhCI+OPmX4f5FJPB9Ort3zjItrt5SQaPjYsfFunuI3XzlBIEyBtU8jpV1oWCyydzVE3sCWJFg\\u002fq127\\u002f+Yt+LrN82zrs5aeAvpy9su5JpTrgj9WSgJWN+1+jhXlbTn5qFe3lYP\\u002fSWSKwfdoJSZwAAGdtMqh3i\\u002fDa3Kzk4cM\\u002fDd\\u002f506vyW9IzUotJ39h25f+zIu8cM79zgROspZDvB+f1TUN\\u002f43r6jAHC1slpnMnXlJoE9j\\u002f69\\u002fWBmPhp1iEMSPA5HyOO6i4Q+UkmguyzK1yvWz3toeJC3xN6oduzG9ffWc9V1CvPysEFhibHNxakwzAGj\\u002fjgawuzz82k9hTibVjR9fPsZjQuXS46lNvfm8XQXD4zDf5i9hiA90VAPYuhqNNQznKsvj2FmbafKNJqovp9wH2cnl+1CCe1VVLeWkohmrrNLa1kT7kYTlVlcYx2ZZL\\u002f4jD2TkqOtE+7nskppmiHJjl189cA+xCz6+l8UZo\\u002fMUyLzdJTE+fm1TZu+3Y9Gu9Pev0\\u002fmphWjUQDvQI\\u002fX1z7soEO6WCZc+visSYtHvrHyu4KrrX1DzCiKefSj271cNO9QbXnjF0+tRYKDRsfc9tTcoZMGEGzfVithQX1l079f7Nn5+zHWdK1aoV3\\u002fxd4H3l6KNrBJGBG5Mf8zNGqlNLfq\\u002frGvo1GA+99aOm\\u002f1RDR6Azuz5zKS4A6O8rv71cWjZg62N91odWn93x\\u002fv3L\\u002futHXHJYszey7v\\u002f+d05wYrbPhq35HN59AoAADwhbyb7p+68J7JPkF2z\\u002f5Lc6s2f3dg39+n2n7AaIr++JHfg6P9IwYEIU393Q+vbLA30GTAyKjbnpo7YtpAexMkMDRzfPuFtR\\u002fuKM2tQtsAassb37vv5w+2\\u002fs\\u002fe050R6+edGOC3LzPv9bnTaIbZn5WXFBQQ5dPaGUQqEADAzISYYaHtHJoubEUziYAPALUqlluSHeL8NjvDTyZ9edbkp6eN330t56dT5z46eLxOrXlhRk98Tan1rcP2oSP7R603vL9wlkKvf3vP4Re37\\u002ftssbMTTHUrimYo2qQzmpo0uuL6JuumGD\\u002fvGYkx85MSonydrWmAXd8OnMj69d9T5mWCgAfvaJ1nAsMcMOiPoSHMvjHDIncevGJe\\u002fuq3w5GhPtHhraXhEAajaeve9O\\u002f\\u002fPG4p9HTz7OSOZkAwFyKIDnQadTmGssmp9SCWqxsMs4fb2f6LvSspxgWXMI5FB3ujIfskIr6vh7S2SWWJZJewfwNkldRYV3sHgLgQmzHQzogLs3mKRmcoqmqICurABkOP7MNe8dWx02iITbVS1e6aj03sTEaIFU64Y67B0MyGL\\u002feiUQAen\\u002fvmukejBrY\\u002f0t8vxOvt9Y89Muntxtrmfltmarnm+5fWv\\u002fTLfdZBV+HxuQ+9d+ucVe1cr3oHejz8\\u002fvIR0wa+f9\\u002fPWjVL94G9a0\\u002fc+dJCobj9\\u002fpVYN1n6+KxVLyy0l2o38w\\u002f1\\u002ft8Xq0bPHvLhg7+yFj76+bVN4+cPFUnRG\\u002f6O5V0u+f2drWgUAABiksJe\\u002fPm+oMh2fk1DYwOe+PSOOSvHv3ffz1XFdUirTqN\\u002f+64fvj36f\\u002fzraIrw07vTd\\u002fx6FI0CcPncR95fPnvleLTBFkESExeNGL9g2Hcv\\u002fcv6OldO5fz3y9GF905GGzri5iGJ7+w9cjS3EAA0BuPNQxKtWwcF+W+9nJFaXOqS5LVj8X7eJEGklVVSDMPpQv6+O7ZZyOXePCRxzsC4ud\\u002f9sfHSVSThbi4WbymF3wnmfutIBZ7cWpupZZ3fP34y6U1JAwAgp6Zu\\u002fcUrsb7eD0\\u002fo0yWb8mrq82rqvz+aOj4m4pEpo4eE4i6TN5Zt+9IJgpCKBRRNV9YoTl\\u002fIv2I1TcUtc4Yl4l60mBMYutFkSEWjmH0TRsXERvrlFtYAQH2j+u5n1kwZGzd6aGRokJdUImAYxmAwNco15VVNV7MrUi8VWU+RGh\\u002ftv2rJ6NbXwnoe0ZsnzAzT\\u002fu1\\u002fDMM6J8TXAw25mp8n+7zE9oQHeFon3EtrmlrbrOSVodf4UR3J7JtFB6G3fvPL6zuacO+Bfdgrvjp2Bg2xqVWp210TJ9yxPufK6dyKQpbSkMv\\u002fN8eZbLuZp6\\u002fbg+\\u002fd+t69PyHxE\\u002f9drCquC7DftaRzeHzuW\\u002f88NmSCzUwXDoyaMfjZ7+5+c9V3aAOAVq0\\u002fvTt9yi2j0IbrToEqo1iTo6PU9vJncwNXoKHut\\u002fzJOXe+fBMatWPMnCGv\\u002fPHgq7d91XaWTkWDatO3B+54bj4Sd4ChmS+f\\u002fqttz3QAGDQ65s1\\u002fHhM5PZdd3NCIT3c\\u002f98LNn5VkVyJN5fnVf320867\\u002fW4TE+ymdRv\\u002fdi\\u002f+gUQC+kPfuxicGpjg7vI7kkI98cJvUTfzP57vRNoC1H\\u002f43bVmK4wpOjs0flPD+\\u002fmP7s\\u002fMIIHgczryBNt8VCwYlfH745O9nLs4fmBDq2ToEp06tkQn4ApfWKnETCqfFR+\\u002fPyvvp5LkHx7d+z5hommGYtlOD2uOqbWYAKpoUwR6tI+45BMEhyLZDCrgk6SOVFNU3GiiK7\\u002fR2Wov09gSAQzkFtw0fYo5UyBXmCj8Wndg\\u002fr82ZWljX8OWRU\\u002fF+PtPiO1CrsVcwDBzPLTqRV7RwSOL\\u002fzZ8iFfR0rTCst2zffzk7n706weCE4Ifv7IkBJdcfhlYa9PuMuqOUKYeiKoBRMUAThIQkvTncSA53AE8wlssfRRAsd98ZRt1YNYJhlAAAQHj4Hec4NxGiyZghr51pXiZId0\\u002f\\u002fSwRh9w+5K1toRpmKKFOGyZhBGTNMxgyaaq1cx9CN9RV2z8xF0gfEbq+gUTYm4xWDbp9Rf4KmKhi6DoBPcrw43ASeYJJAtMBxTY\\u002f6inAAiuQEePqfBwDKVKxRfmDSn2LAyOUmCKUP8YXTW9Zl9JpNOvWvlCkPCCGPP0wofYjH7\\u002fYbpRwO+dYzC554bYO5fBNF0QeOZx04noWu18aIpPC3n1vI5fTLrqPXEZbf\\u002fZ6DS7tgvc1oogorG3JLawsrGxqUGrlKK1frNDqDzmDSG016g8lgNOlse1v3F27dPx+1VOTsxbuZl5tN+UeVlv0bQKG26TxEEoSPu6N6Faza1nyvbjSfkHRAD+xDzMLZq2sMc+z0rjQ0BMDjc+fd1bGrwfHzh\\u002fqFetWUNlgHGYbZteb43a\\u002fcbB3suic+W+l8tt1szJwhU5aMOrzxLNoAcOHQtes74U4x1JqiDzMVF9EGWz2fcB85fZDz2XazYZMHrHh2\\u002fpr3WKaO\\u002fO+Xw8uemOV8X\\u002fLDm86yVlIKCPN5dc1DzmfbzTx93d5c9+jj099tO5\\u002fB5m\\u002f3z1k1PiDMxbedesXmbw\\u002fUljeiUYDHPrrd+Wy7xcoXFqSfzM48V4DElY3qbT8euv2ZeUjceV5i0aSYiFMFJTTDTImNNE92auEhEn60aPYTG3fe9OPaeYPiA2TSerUmv67hXHHZ\\u002fkfvtk5GO0bRdFp5pUpvUOkN1UoVAOzKyPaVSKQCfoS3p7+s+bzq1dlTMqtqPzt88khuQXJIkIDLKWuSH88v\\u002fmvVslg\\u002fbwDQm0xXKqrNr9Oo0TIA269kSgV8qUAQ7eNlLgXuqm2maXrqV78MDvIfGODvJ5Oo9IZj+UUljU2PTWLpDjBvYPwfqRdX\\u002frFhQkwERdMNau0b86ahK9mXHBKUHBx4LK9o5ZoNI8KCa1TqfZm5Cf6+Z4tb80fgxP5BcEnyq6ULbvnl72e27l5\\u002f122s6\\u002fQ1DAPb0jIuFpd\\u002fd8dN0f1hg7HuM25k9CtPzOXz8Gl8R9Fa1bda5TctGfNWDCOnaDllKgA4qFV9TRBSvmi+xP0dgrD5HScIiUC8RKf+zfwkvWat2O3\\u002frFewR69Zb1kWiBbZz7Z3dQvN5LUzGYZ9ppOuo0z5GsXbBh1SuFJPmZSUqdig26tRvCeSPiSSPeI470lTVQytpOlaed0ihm4+8zcaUo0NqRL3N4SSewBALX9Jp\\u002f6z+QmMxqA7YNAdknp+JRB17MSvE0ICPX\\u002f6cMU3fxzddyzDmWFaUWE+dy4dPXVsQptbz9iNpl\\u002fmMbHrgFprOHwp7+D5nNSMYoPJ7vxz\\u002fZpY6OxFeudwSLKjhexFtnkDtdam7qWFQmOTcBd16h8iEvBIgrAeOixXtY6vclJ378PeMj2uL\\u002fag6tiHCcPsuXaWZY64pHFx7t4dG5JDcsjRs4Zs\\u002f\\u002fkwEk87mgkuTbiPmTNk2rLOdJBZ8sgM1oT7tTMse+B6klq\\u002f35xtDxXH+AgCLjWe8BUE+gvDlKamUk0ew9BzA++IlSWhT+tmBEnc\\u002f9ZSNOqEpY\\u002fOPPDP6bbDMhQN6uNbL0y71amxwAzN\\u002fPMZS99qAHjy85WOZ1mwxz\\u002fU+\\u002f63ln38iPlivpXJSK37ZNf\\u002fvliFxPsdrUq39cdDaBRg\\u002fPyh0ztVQJ\\u002fkkI99vOLhSW+hDQC71hy\\u002f9ck5nZ5jGQAWJSUeyikwL6BtAFPjojfde\\u002fuPJ88dzilo1Gg9RMJQT48np4zzltr0dHBModPf\\u002fntrIgYAXtt50Lzw6MTRlhS2n0y6+d7bfz59\\u002fkB2\\u002ft\\u002fn00kCfKXS6fHRvrLmj1l5k2LFHzav8+zWPeaFV2ZPuWNksnnZJdtMkuS9Y0acKCjefjVTbzR5ikVRPl6f3zJvTmIcuirAU1PHCbic3Rk53584K+Lx4juYKSYAvl9+0yeHTpwqLEk7VRnk4fbwhJQFgweM+\\u002fQH69Xa3T9teYpF3996062\\u002f\\u002ffPgv9s23nNbFydu7TGljfJVv2747a4lcf7Xw+03zLGhA0MNBqquUaXVGrhcjpeHeHBC8OzJA0cOCUdXxdpHKRvuMegOoGE2DKOijNdYc9lCyaqWhDvoNOtFsufsZ88tTAbtVssDgfjW1hYbrtnCbmXUH1U23O+4aAbDKDXKD42GczKv7xxX06ZMOWr5q5Zsu4Va\\u002fiZPMM1kON2abW9Fq+Uv8AVTCbJjlxid4OUheeWJufevGH\\u002f4VM7V7Iqi0vqGJrVWZ2QYRijkiQQ8X29ZeIhXdLjv2OFRYcF4pg0MAADs\\u002fFXyJPdxBJPRqOuQzo22wa5LBhO14VDar7tS5SqbrO71h4DuvaXJ6fj0G2LbgadqHXvCXWlbzxZ5lvNEQp51Tl\\u002ff8ZEK3b0Pe8u3yxaioT4AJ9wxF2AYpjiztaioRfzwSDTkhIQRkW0T7gXXyvRag0DUyS8mBEESd3U2fR81KDRqUGjbyV2rS+u1Kl1Hy3\\u002f3I2lNJwFgbuAdk\\u002f1uAoDLTWfiZUMXBt8FAHX6yl8K3rmqSB3nOwd5VncbPSspJMYfjTqBy+cueXTml0\\u002f\\u002fhTYAHN163smE+6VjWayTdo6cPqijgyesTVuWsuX7A\\u002flX0M\\u002fYoQ2pd71ys4dPt19hdqsD\\u002f55RNqIX6gRBrOhIJR9EZGJw4sjojHPoTa\\u002f6yqa041nDp7Dkyp00a0Bs9iv\\u002fQ6NW4vx8Pr7Z7sf+zpRhd6YMQ4LzBsZbV6fxFIscv4WFu0j49NTxT08djzYAAECUj5eTr+N4m51BADw7fcKz0M7sF2ZCLtfBZt8xMtlyM8AeT7Ho7fkzkGDbf6zj\\u002fQMAJ596AInE+flcev5RJNhNlgwfZK5oj6BoWq03qPQGuVZXUNugsp0PllWDWvvgn1s3PrTCS9I\\u002fbhJgnfbo6smPrkaDWOdolV9b57K5\\u002fJE8wXgOJ5QgpQyjZ+h6kzHbZEyjjJnmFQSSOywrW+NwY3mCcUb9SQBg6AaDbqdA1M4ppUF3mKbrzMsc3gAuj713gqu2EADc\\u002fQ4AQ1seMoxcXts83osg3d19dlqaECTpjoasGPXHFPV3WjrwEoSUL5rN5Q0lSS+G0VNUsVG332S82rLyYVXjIzKv38H+tb1G+aHJmM4TjBeIbmYYo079HWUqBgAASqf+3tyJni+czRfOZhiNVvWtuTYOQyv1uu1CcQ8NqfT3cVu+cAQaxTB77NR6Ijh+HIHdUxQM67Q6ufrJL7dkFdegDVZ4XI6bRCgTCUQCnkjAEwp4BqPpfBZ6sYmZ2OrEOkbRNk9pW2DTDFmNcWbkFBumZXZuMy5b5Uys78AJd8wFmuqUBr0RjQJ0LhMaFOmHhgAoE11T2hAaF4A2dMrIaYNCYzv\\u002fUomjotom3BmGqS6tjxgQjMSvGzX6ci7Bm+DbfMHGI3kGuvk+rY8g8JbQB37Mf\\u002fN47Y4pfu1cdrrW1GVOZcZZTV2a8uMrG9vOnpp2LMvJeycH\\u002fj2NhgAAYMmjzXVaO23JozM\\u002feOAXJGgyUgf\\u002fPXPLI2jmsX858O8ZNAQwfOrALv7tTF8+um3CHQDO7rvclYQ7hrnK\\u002f82f0m6VfIaB0sam9NKq7WkZp\\u002fJLHEw2WylXvrpt\\u002f9e398WuHBjWJ1E69e8tyxyZ1098IfsvNU3XGrS7DLrdAtEitK2FULLanHAHAJ36z3YT7nrNBsuyULzcqsWaK7eQwwmzfmjbhZx0su48gqbrVI1PWLLtfNFCqft7BJKglz2t125WNz3LMHoAMOgO6tS\\u002fCCX32qxjxag\\u002fyRfOknn9bE7K84VTmmomMYwOAHTqtQAglNwrcX\\u002fdvDKPP7KptvkUyKg\\u002f3mMJdwzrEHsTGLQdyYFhXdeg0Kx+5++qBrQKmZdMPC4pcnh8aFSQV5i\\u002fZ9u65FcKKu96dx0SxGiGMZgoPrcDWWydbR5MKmTvISrk2xRy0bBlz9rFMKA12DzRrYMFbLEextLZCsM6StXIXiZS6t6BGgUWUnf2LnvKJrRXbKc52X\\u002fZnlg7Q7kbquRo6DqiozQyngeHaE4YCUiRlmo9IjHSwVKue1pj8\\u002fVnz+BwyWGTBqBRpwlE\\u002fFEzBqFRAKPBxFoiCUGZ6DN7LqNRAP9Q78FjY9FoB42dl8w62+fhTSzljPqRqpK6nEtFaBRg7NwhaKiDEkZEoSEAAEg7no2GMKyvIggI8\\u002fJYMCThpzsXH3zm3vlJCegaVg5m5h\\u002fLKUSjGIaxoakqmm4uIscTjLeXywYAkvQVSu508\\u002f6HIOwWpOILZ5KcQPOyyXCWMuXYtttg6CaDfn\\u002fzA4InEC22aW7h2i3sDlrFh5Yt5AtnyTy\\u002fQbPtAAAgEC2WuL9teahVfsUwWqt2lEj2hKULPMkJ5gtnWZoIQiR2e8bykMMbwOE2jw+jTHmWOIb1KQTZ\\u002fOWAYOhGNIRhXfbOmv1Itt3bXfLqXbN2f3L\\u002fa3fNmj82MTEioG22HXNAZ5vRbpfO0Hwf2kxipySDh9Tm0l6nN9rvV2OX1oA+y03cfh9BrBfhhDvmAqzd2wFAKGb\\u002funGMb+euoMZFJclIDtnFHq8evuw1PVy1hX0Tj+CrTQrLQzHXrd5gU03Fi++HRLpbZGKIWNal35ihk9nz9Rmp7SfcM88XaNmOeMqswfaGkjmPL+ANZbuXUHC1rLG29Sj0OxcPNw+ER4ycznLno0PC4wNZS06V5la1HcSAYX1fgJv0o6Vzvl1xk9i2R4y1746koiEMw9g4zvl2HEcobi3nwlZnvJVeuxWY5vNkvmCG3d6vLt5CF6Pper12k3mZIAQS93cdFIoRiJdzuJHmZZqut65fjyAIGVJgh8tvLd7CE4xFSsBzeM0Jd5oqt45jWN9BctiHbDKmIjSEYV1zMafsaJrNRWuQj\\u002fsfL9++cNxA1jKG1jqR7b1BNCk79nNc3Whzw8Pe7Q1\\u002fL5sMEs0wNbZPdEZVPZoHcHdiUD7Wi9r5O8QwZ7DmuQBAo2RJR7ZLrWD\\u002fjhO4aD7liAHBrH2HnWev575Bx37j4frgIwgw0PomY735ob8guFJbrDC2dtZQmppIoke\\u002fUmKSbEZMd0LiSPbJrHPTS9BQG1dOsfdoGzLeUadU5yVPZHkdhmHS+3OX7bRjWWgIwCfI0yeIPQHhPJJD+oWyzFfG0ExpbjUaxbB+YkpC1Ne3L+TZqc+YVlqZVdnc4RTDMAdITohlalOj4aTJcN62vcMEkhVANJ+X6rUbHaTLrevJCOzWk3H9FrqWQfufuUoMAPCEs0iOv207guAL51geGPUnrJpscLjo6DSSE2RZ5vAGWrUAAJBk87TbDM0+uBbDeh3BCSBIljNSyngZgEKjGNYFu8+gPZlev3tWgG1i1x6lpjOJmhtBaU0TGnKotLrJ+mF4APtVbXRQ8++XRUFlc17FeQUV6FPiQv2QCNan9Gh2DLteyTzZE9DyBhUacoK8nv1en7u3Uz8e7YpJCkVDHWSv5z7V8Uk2+pFIyQAAuNJ0xvwwRpbEALOu5IsmYz3FUCfqdjUaar357IMou0lwdFd\\u002fYEJi\\u002fHl8lqrKZXntd9UvaDOpqVlsMnvFoY6KGxqBhgAAIM+JmwF9Vt5llo0PjurqcTSzdyOtrhwXzcT6sTHRYc\\u002fMtDvN2p5r7Hf+MAyzRhDC1hQwY1LUL9Mo3qUpR1PMOUaSPgLhPPMyQysN2m227c0oU67JmG5eJjn+fOEk2\\u002fZWLt9C1zIaWiva8QUTrVrYcXitY0mN9m8ekBxfJGJdJ4dsU5qDICw9BynLuAEM62tIHtvATUZDG1n6nWBYp13ILrN+GBPiMywuxDriQIMC37ZkV1zVgepPTSptg9JmT9rLgMeG+PB5NmmH7JIO\\u002f8TnlNr0s\\u002fGSiUP9PKwjWF+DE+6YC7h7y9y8bIZ8muWzJdfalZtWjIYASA4ZEO6DRjvFVdm9G81QzwkAUKFtLhmc7DFOynXPV117N+PBly7ftr38NwAY5T3N+indzT8MvVHcUSSHZH2R6tJ6k205trYKMyvQEIBIKvQNZr+t3VFhdqYIzrvSmT+rvkCn0VcV16FRgMBI9Hq7cyQy9oR7Qw06+A7D+pfbUoaEeLLUSgaA0\\u002fn99QsBw3qY2O3l1v7RjEGr+raxZpSy8QGD7qBlItAOEUpWW5btVZWx6d4uWgrAPlrFzOVb6EImwyXLMocbY9XCjiQ9LMs0bTenQFit1qJ1F5Es5XdaL10ZuJ67uWD9Goc\\u002fGg0BAABl6NHJrrDrXm2TTe\\u002fGpKjWEULtupJfiYYwAAC4lGtzG8OxM9fQzFV8GPuFLZ\\u002fHHRZnU2\\u002fq6KX2a9gijlyymb9kcDR6W7ovsVt37obC0rUTwzohbmjE+YNXkWDnpitMP8HSXy9qYAjfRSVlfENYRvlh7QoVxzwa+26YuHk6UD4pWBXxzJqij1UmOQMMAAz1nJDi1aMJdw8fFwx68Av1LstDS44wNNNUp3Rc56S2jKXfdKCLbgsBgEDE9\\u002fJ3b6hGZ+KtLkaHkvUXlUV1DFu9wD1\\u002fntjzp93x5l2n1xrQEIb1KzwO586xw97ZeRhtAMisrDWYKD7XURYPwzAAIDlBbj5bVY2PmIyXm0OMyaDdadDuJEgvgWiRQLyca9Uvu11c\\u002fggub6DJeA0ATMZ0k\\u002fEKlzfYdhVar91seSAQ32rVxMLlW+hCjFXSXF63qLXBGYyRYVRINXYzAtjHjDZrKdqD9W3sWRXz1cGNiSOcDsoP0SiASfM3T3KfvT2G2ce+x27kzxgAGE2U1nYiPQ87fY\\u002faomnm1NXmXnQYIjWjxETRXI5TXZOPpxdYPxTwuYOj7CbBZ4yIt07QXy2srKhTBPm4Wa3iSH55HVJSZuqw5sxMHyRsU0UAmV32BoHuBQzrnLFzh7RNuJdkV6Yfzx4yoXmCI2dUFtVeOHwNjbpiTkULd7bO+JgzLNl2swhJwosDvilQZxpoXYAwzFfQgZvqLiHzbB133Gn2Pg+OE+6KBjXrXMGefs7+ZDrD08+tbcK9rqqJYZiuz8va8+orOzBAz4VYjxSG9S\\u002fjY9hrVRkpqrxJEelj98vKSZVyZUZFTVZVbWZlTY1CJdfqlTq9Uq8HAB6HIxXwvSQifzdphLdnXIBvcmhgtG+fuHVN0cz54rLT+SU51XUFtQ0KrV5tMDAMiPk8N6EgxMs93MsjMchvWHhwlI9XP\\u002fzWxFyMw410992h16zXqr6lTK0XyQzdoFP\\u002fqlP\\u002fyuOnCKUP84XO9h4QSlarmp41L+vVf3I9bFJsRv1xmmquUMfjp1jmEXXA5VvoEgyjtxRw7yTGwJ4xayeljm8l9gMEaaf7C61GIzcMkhtPcmNok01fVACgTYWU\\u002fgRHMAGJY47hzxgrHpcj4HP1VklMtc7ZPka7zmRWNbBX8cU0OsOu0xkLx7effapqUB68YNNVdFJytFho90byjJFxn60\\u002fqtI2\\u002f54yDHy\\u002f7dSb98y2Xcuu77aesn7o5SaeMaoDebYeJhULSIKgrTrbVd+QHzmccMdcY\\u002fyCYd+\\u002ftL5tYuvn1zd9uvs51jLZrH58ZQNDozerCZKYdiv70LxOEOGpnF2HRwriZclotKcIxeyTgHeI1EOMhgAAQC23OwEa2J9pgLW2UqexvprJYFLLtfY2uy\\u002frrdIubb9SMKzfifDxDHCXVclZvnkqOptwp2j6XFHZwcz8g5n5lWyvbGaiaK3BWKtUZ1fVHYMiczDIw232oNjlo4aE2ql10wkag3H4W18jwR2P38ma3G\\u002fUaH87eWHD+StNbLN+ybWUXKsrbZRbSu54S8XTB8TMHhQ7Oqqrs213EcPAa9sPbDh\\u002fBW0AAIDbRg15Zf5UfG+gO5EC8XKBeJlBd1CvWWfQHbKu1mI0pBobUnmCCVKPj0hOiNWz2PFFiwjF2wwtBwC9dpvY\\u002fRWCaE0M2U6Xusyy3B5XbqGLoKf3roM\\u002f6\\u002f0eQXigIQAAYOg6NHQj4YpXGBRvoFEAg\\u002fITkWAsvpnUIfgzZo+Pu6S8trVvVkZh+5OQAUB1g\\u002fKrTcfRKGblx+2np4+MFwsc3xKGbzafMNnO4TdvjKNRaGIh\\u002f\\u002fYZw37cftoS2X0mY\\u002f7YxFED2j81PZaWj9STuWVSUl8e4UoShK+n1DrJnppRfNOE9m9jXGecTYNimGMyT8nc1RO3\\u002fnAQieddLvnk0d+f\\u002fnp1uzl3hmH+eHfbmT0to2itTFg43IWF17ntbQnWX3B5LviN4dv5KTUaHF1eGnTsrQKR3XvanSAQsW+bXmdkycT3eVoVS14MwzAnBdlJuMu1Hf7Lyq+pX3smbdfVHEXHn2tW0aT49cSF309evHlo4lMzJ3hJnB3F3FHVCmXbhPu6s+kf7T2udfgtjahXaf49dzm3uq53E+4MA2\\u002f8d9Betn3VmKEvzp2MRrFuQfKFM\\u002fjCGTRda9Bs0WnXU1aTGRr1x+W18918NrZbrJwgRALxrTrVjwDAMGq9ZrNQcqe5iWGUBt2eltUkfNECy7Oc45otdAmCkALBs0xSKvX4mK32uiPW9yGw6w3BA0IMDDoBI22pjHRD4oqXGlWfMTTa14Q2XjKqvuFJH0fimCP4M2bH0Nhg64T7lYLKjKKqxAj2acDMqhqUT365pV5+Qw8OaFdVg\\u002fLZb7Z\\u002f\\u002fvginv2M9t\\u002f7L+4+k2kdiQ\\u002fzGzMowjrS1spZI3aculZR1\\u002fzNwDDwzDfbv33qlkH2C9EAwMXsshd\\u002f3GkdCfJxXzlrhHWkD0qOCd57tvXU5dDF3LyyupgQlxXg7Rdw5hFzmVufnH1owxlFA\\u002fr1fXTL+YrC2vvfWjpotN2rgtLcql\\u002fe2Jy6l+VXUyQV3vfGLWi0Czgc3JumMxhgzjccvipPrdNX6WktzbDPVfXqwJ\\u002fRUN\\u002fG5bP\\u002fjhr1rR3K2rLXynHFPQALHp894W5wesBgn2LQse80DMOc4Wknqa01OvuXxTBwNKfgz9Npp\\u002fJbK0h2Bc0wmy5eO5Zb9Pny+cPCuqWqWJVcZf1QZzQ9tX7n4SybipnOmxTffk2P7sMw8OZ\\u002fB\\u002f89x3KqAwD3TBjxzExcZ6CnkaSvUHq\\u002fUHq\\u002fyXBWo\\u002fzSqD9ijtN0narxYXffPdazdLISilfpVD8BMACg1\\u002fxlSbgbtDsYpvmGFl+0gCA6OS6t61voEiTpRVPNE95weEm9VUoe65tIbjRtRO8jUsZLDF1PtMwDfKMhCBlP8rBB+T7aAGBQfkHykjiCyWgDZh\\u002f+jLGamBy941SGdeT573Z88cTNUUEs+8Roonadzvxi4zGFupOdLVgxDGj1BpXWUFbThDSV1TZ5SIUSoUAs5Pe7oXupGcV3vPXXc7dPGR4fijQpNLqvN57YfMzmdI4g4PkVU8n2\\u002fp0iAe\\u002f1u2c\\u002f9PFGim7OpWh0hns\\u002f+HfV7JF3zBruJkYrMTSptL\\u002ftOrvuwEXaasQ2SRJv3TvHQe2aPmJicpR1wt1E0U98ueX9B+c7KHN\\u002f\\u002fcEJd8xlPHxkj39yx9t3\\u002fYA2AOSmFT+74OOoQaFDJybEJod7+MjEMqFea1A2qgszyi+fzLl8Mod1NkUAeOrLVQ5KaWM9ZmfFmmO1O9Bor7L+4ek0uy\\u002fi8PfSYaPLMC2\\u002fxIj+WMAdACiKQkMYhjlNKuhqES0TTb227WCN0iaF3XW1SvW9f2z++c7F3ZFzr1a0bq1Sp3\\u002fgz62XSiqs2jtmcnwUGuopDANv7Tj0j51s+0OTUx6fNhaNYj2Iyx\\u002fl5r1Wr92qanzSXMLFZMww6k\\u002fxBOPRVW1xuBE8wSRzHtxkzLBMnarXbLKsI2xvulRndHoLXYLLG2Kg9pmXKVMWTrhj1jj8lLbJUGAMRvVPfNkLaPyGwZPcbdKup60mY2hh0jXcI3B\\u002fj9uBSlPsaGOmSbuB7\\u002fYq2nDdwZ8xVpOHxkQH++SXt5bWqaxX3PHm2ukj4lISw4N93fk8rkqjr2lSpeWWn7hcUGfVsX3V7JFXCyov5pRZIu0qrWn6fusplVav1hlUWr1Ko1frDGqtwbpOt7Xnvv3PvEAQIBbyJUKBVMSXivgSoUAi4s8ZPWBScrTtM3pfcmxwg0JTUt0IAPnldQ98tCHQ221UYpifh1QmFjQqtQUV9aevFhlM6FXtzROTkqKdOg0eFhfy\\u002fIqp7\\u002f55wBIxUfSvO1PX7Dk3PD40OtjH200MAPUKdW5p3YWc0rbJiocWjRsS49R79a7pI+K+2Xyyoq51EEZ1g\\u002fKud9clRQcNjQv2kolJktAbTUqNvkmlrZdrqhuUNMP8+8Yqq9fo93DCHXOlcfOHrnxhwZ\\u002fvN3+3IgqulhZcLUWjDj347rLxC4ahUaw3nGs4DABzAlcMdBsp5kqJHulR5RjV5qeuEygj+4vwBI6+HnlC9r7nJpfOvm20u23s797H2avec8vDM4ZP7cZL9yDXFaTCsF6k0rNPWijiOfqyssbjcFaNGfrxvuNoQ5dpDcYn1u3Y+sgd3tJOduO1x1JFx2CiuphtD3SXxfn32jjWt3YcWnc2HY0CAMDj08Y+NDkFjWK9QSBaZDKc16l\\u002fNz80GS85k84WSlZbOp7rNeu57oNpqtpoSDVHONxoLn+kZeUu6twW2pZKRy\\u002fdncETjDfomhPuBt0BgWixbTt2Q+PwRxnVLCNcjaofOfzRN25XbkIg8PhUW3cLQNvzeZNe\\u002fixlOMGTPkVyI9DG9tCmAkp\\u002fyKTdRBszAODGSLjjzxgLkiBeuXPG\\u002fR9tMFgNdjSYqF1nMnfZVjtBLJow+LFbJvyx52yHEu71crV1h2XnMQyotQa11lDT2BqMCvLugwl3EZ\\u002f32WOL7vvg3wZlcwmjynrFtuNXbddCjUwIe\\u002fa2KWjUvsWTkkw0\\u002ffHfh63vVZgoOjWjODWjnUGo9y8cc9fcUWi0T+KQ5Murpj\\u002f2+WbknsHl\\u002fIrL+ezn836eUjTUzzl7kYZhTrr96Xk8Hvf3d7fRtjNIdJSbl+TJz1aOmZuMNmC9hABCynWf4rcIbeg99gqpd4hWzZ7DspcdNrM3X6tOw\\u002f5qnWPv1YR2arv3cQI7dyk8\\u002fdyGThqARjEMs8U6OygAuInQ8acOLBs5+PujqSq9AW0AAACpgB8f4DswyC\\u002fIw81NJHAXCTkEKdfp6lWaK+XVF4rKHfSOr1OpP953\\u002fL3Fs9CGrqlq6eH+8pZ99rLt4d4ePlKJh1joIRJqjSalTl9U11jWJEd6XPVi93YH2fZnZk24Z\\u002fwINIr1Hi5voGWZYdh\\u002fhRF84VSSE0ZTJQBg0O6QuL9h0O2y5LUFrujebq0TW2hdQp1hVACmjl4D8oXzNIq3GcYAAAbtTkqWz+H2uUQJ1ls4gikE6cvQtWgDULqGe\\u002fiyZ3mS1UA4+VNF90yVpJ5B8oby3f6PdfZUADBpt5m0O7miBRzhdA5\\u002fLEF6oWu0YOgmxlRAm3IpwxnKcJJpqe9048CfMXsGRQW+98C8\\u002f\\u002ftpl1bv1HUxQcDd81IeWjQOAEYnRny96QS6xo2tXqEOD\\u002fD88fllT321zdzPvV1Th8e+dc8cB9XeWS2bkhzq5\\u002fHGr3uthx045i4VvrRy+rThcWhDH5aSGP7yyhnvrT2AzC574+jYyRaGOWPp47MGjIr+6pm\\u002fSrIr0TbnjJg26MnPV3oHuKMNWO8Z5zNnf\\u002fWGbGVavCwZbeslaoUWDXWcWt58+xoh85SgISuefm5oCAAA2s5h0BWKepbcFpfPFbuxl3Lu42Re7Les5Q0s\\u002f0wMwxA1VsVVrAW5d2A2QplQsGzk4F9PXLBESIJIDg2cmhA9OSEqysfLQcEqmmEOZOR9c\\u002fhMTnXr4GVr29IyHpqcEublgTZ0QbVCCQDrzqbvuGzTqYokiFkDY6cOiB4TFcbarV5jMKaXVh7NKTySXVBc3wQAk+J6p4D72zsO\\u002f53Knm1\\u002fce7kVWOGolGsGzCMniDY75QjTMbWg0VynByyTQold2gU7wIATdeaDGkG3f6WJo5AtMRqTbu6dwsJLkF6MXQDAABjMurP8gQdK2FEcvwF4ttbetZTyob73H02EaSn7VosnP93Yf0YwedJ7jUo30PjAAAmg\\u002fI9o\\u002fpHjmAKyU8mSF+CEANQwBgYWs4wCoZuYOhahqphqAqGKud7fMwVzkVfoz\\u002fjSe5mqDKj+he0oZnJpN1i0m4BIEhuJEH6AulBkB7AGBlGDYyaoZUMVdr8x3sjw58x+yYlR6995Y731h44n9VOLYG4UN+nl0+2FCWPD\\u002fPzkoktXbkxAGhSaQEgIsBr7Ssrftx+ev3hdOvRAwgfd8nDi8cvHNd6F7xDxgyM2PDW6t93n91wOF3jcIY2sYC3aGLSXXNHecr6XxLgpgmDYkJ8Plp3+GpBJ3OD\\u002fRpOuGPdYtDomO+PvfrWXT+c3pWGttnHF\\u002fAmLx550wNTowaGoG1Yb5sRsEzCdfu98INwSVyAMEzEkZBsvQNmBHS1HKHzlI0uyG431jbXK0B4+DjKYYkkArFMqFGiHU4bqluLlHVdY40CDQF4+7v30xruPkEeaAgAAJQuvUuBYdelGqWqtJHl64XLIYM92e\\u002f\\u002f2bNqzLA1py+ZKDra12vZiMELkgd4ip06fScJYubA2EnxkW\\u002f+d2jzxWtoMwDDwPrzV1w782eVXFVc3\\u002fThnmPWwZkDYx+fOibaj2VOMAsxnzcmOmxMdNgLcyZdKqnYlpaZEoVOe9UD3tl5+K\\u002fUNDQKQBDw6oJpy0cmoQ1Y99AqPzYZLgnEy3jCqSRpr7IQo9Os06nXtTwk+QJnR4gLxbdplZ+Y+5sb9EdMhjPmOF84meQ4Vdasu7eQx08x6HablzWKN9y81xNkx\\u002fq1iGVPGXQHaKoMAChTTlPtLInbK3zRHNbLSZquNepPGrQ7GEbh5r0ebcauO1zJSqNmLUOx5\\u002fsYut6k3QjajWjDjYHv9ioQfKPqO7TBBkObCgDaFnzHmuHPmAPhAZ7fP7M0q7jm0MXc9LyK0pomhVprMFFCHtdDJg7xcx8UGTg+KSop2maySoKAfZ89aB1xLDk2+PzPT6HR7jQqMayH31HZMr5cLOQ\\u002fuWzSXXNT9p7NSs0oziuvq5OrjUZKJOQFeLkNCPebMCRqUnIMl8OSD3GeTCx47JYJ984ffTQt\\u002f2xGcVZJTVW9Uq0zkCQhEfKDfNziw\\u002fxGJoRNGBIlcjj43oGe34dtDYwM+P2l264WVB5LL7haUFlW26RQ6zV6g1jAd5MIpCJBgJcsNtQ3PtQvIdypU6Z+hOUMCcO6TtWk+fiR31L3XbEOiiSCwAjfuspGrUpvMlFiqVDqLpZ6iH0CPeKHRyaOjI4fHmGvUgfW60o1eYdrtlKMqUCVUaDKQJtb9GTCva7CqXFejtWWs\\u002fQZ4fI47t7s3bEtgiL98i6XIMHKolqGYVySENeq9U11LDcD\\u002fMMc5Zj6Mv8Q9i0vL6hBQxiG2TpTwH6FOSDAV8Dt2Lmcv5v0pbmTo329R0V25t62gMt9a9GMOpXmWE4h2gZwKDPftQl3uVZ37x+bdS3dizgk8fK8KbeNGmK7VjuGhgUN7YYJXdv17q4ja8+koVEAkiDeXDTjlmGd7BKFdQptNJwxGs4AEFxeIoc3iMONJUlvgpQCY6TpesqUb9QfpkyttVOFkhUkxyY34QBBevJFN+k16wFAr1lrLr0CAALxcpv1HOneLRRKVlgS7ibjtabaKQLRUg5vAEEIGUbH0I00XUdTlQLRAp6dJD5Besm8flXU3cwwagCgqQpl40OE3JPHH05yQgnSDRgDw6goUxllyjcX2AEAF9avx\\u002foygpAIvX7W1S02fzwwBF\\u002f2AskJ0yteB+fKQGFt4c9YuxLCr8N8ZQ+jbCufuEuFy6YmL5uabB10OZGANzslYXZKAtpwfRkUFTgoytmTlutGxy7SMMwZteWNLy7+DEmiDRkf\\u002f8w3q32C2h98ivVNG8t+kBvrZVyPRPcRbjwvLtHJu6wuVF1aj4Y6yKA31pSxJNwDI3zJ9u5XRw0MaZtwN+iM1SX1AeH2+qZ1QEkWe7Xi\\u002fjv+wzvQw81LqmhTQCY3rZim6HZ3OIbdyLZeYr\\u002fNOSqyM722O5qwRpAE8d7iWdM++dmSB7corGusV2lYa7x0WllL134OSX67YuHEXqoM01Hv7T765+lLaBSAQxLvLp61cAieuKK3MCbjNZORZYiGNZ5gkriD8xAKJavNCXeaaj4BJklvvmCGzUpO6ZYt5AkmC0Q36bXbzA9pqkar+sZ2FQAALm8oz37XFy4v0d13u7LhHspUZI4wdKNBd8BmJexGRXITBJ7f6hsfZRiW\\u002fiIYV3w7yRuqlz9HGy+jbV3SscrR\\u002fRr+jGEY1r\\u002fghDvmYmqF9sVbPkey7VOXpjz91Z04odav1ekr+aTg6fhPxVxHtVZ6UlEme0raecVZlayz+4bE+KOhNmKTw\\u002fetO4VGAXIuFbkk4Z6T1tqLzVr04M7k1\\u002fqImCFhFw+jeUOdRl+YUd6v\\u002f10Y1q3OF5Wfzkdv75nNHBiLhnqEl0S0eNhA1tLkmVW142PC0agrPDFtbB\\u002fJtrc7iOn93UfXnLqIRgE4JPnR0jlzBvWnCa+uD1zeMJL0pVkm3EMRpEwkfUwkvb+jV0lcXhKXn2wypFkifPFiIJx9kR7YQonHZwQp06nXog0dweHGu\\u002fvs1qq+0an\\u002faDfnxeFGC8U9N\\u002fAR63UcwWSR725d4yO01UwDmAXJGyDy2WbSbDSovmJaRoF0GsmN5IqWccVL0IbrGv6MYRjWj3TsRA3D2vXdi\\u002f+U59vMnB4\\u002fLOJ\\u002fX6zC2fb+LlY6OE91hc9xcv73npCX3tVT1YyzeWgIAADihkagoTaGTmIf9nXpWNbERSPQaMddOmYzQ6BF0vh4NNR\\u002fJI2Na5twB4C0Y1k44e5C5WUN2\\u002f+7dOVyaUVFk0ajFwi4MpkoNNQrcWDwnXe2U+5j\\u002ffrUP9ec8PSSvvXWLeGuuHWEdZFab3h75yE0CgAA0X7eSSEBaLSnTBsQzZpwt3RId63RUaH3TugrtSmEPEdjvD7Yc+wPtmw7j8P5dNnc6YkxaAPW\\u002ffiieXzhTKPhtNFwhjJmUqYSmq4FRssweoLgE4SU5ARzeAN4ggl84QyC6OQQDaF4tcrwpNVD5+vJ9MQWEgRf4v6+UHKXTvOvyXCONpXQjJwALkG6E6QnhxvH5Q3kCSehT2uDIGVitxdE0ocN+v1G\\u002fQmT4TJDN9BMIwEcgpCSnACSG83jJfMEEzg8PJLjhkNwQkU+W0y6\\u002fSb1r5QhFW12SEv4FdTebx0ZFFpE9F6+Qs0wjTRjZBguAR4EKSPbvdnqDJIrXsYVL6F0+43aTZT+KDDojFAOcTn8ISR\\u002fHEcwmcMfjjZaUemOF9behkbbIAnxwJAcNNq3deUzRnIjCbIfF105mJH\\u002f2Nrtz86ZeNcER0cf8dfptHf+O2xe5pDElbeftGm29b91O88WlG59fKWvTIK2dZvO\\u002fbswrO\\u002frtR8w7LpUkl15cD36s3fPa4u5vBtosFsXkfZO5hgGjfSs5WGPby3\\u002f+eeCt8f7zPMRBAg5Eg7BclhlXA801G0aaxWluVWhsZ3PN106komGAABgwMgoNNRGcLR\\u002fQLhPVXEdEj+3\\u002f0rXC6QYdMa0oyzbFhLj7xfihUb7j5RZSb+\\u002fsxWNAuxec3zxw9NdUvseO7D\\u002f6scf7zIaKUtEozFoNIbqarlWa2w34f7nmhMajUGjadi5I+3hR6ajzVjPMlH04+t2ZFeh3zNm9453wb29TksODeKQBEWjv021ym4prvrYtLF95xtCYn\\u002fqqg\\u002f3HPv95AU0CsDncr5cvmBSfJ\\u002fooX+DIng8wUSeYCIadyGrantcXhKH28Eb5D2whQAcbrykI7Vo7CFIN4HoFoHoFrTBCd5B7GP4AIAnGOMdVIZGW4jdXhK7vYRGsT6HwxXO5gpn01QxbThPGS7SxmvANDG0gmEUwDAEIQBCTHB8CNKP4ISS3CiSG0vykij9Gai7F30x+7iim7mim9GoK5C8RK7PgZqq6QzQAGBkoJah3Xz\\u002f4\\u002fKHoqt2EskRzuIIZwGjoQwXaeNF2phFU2UMVQmMmmF0QJAEIQFCTBBSghNIciMJbjTJiSb5yQTR7WlQrmgpV7QUjfYtnfyMEaQH+ko3gDmD46L9vJo0uk\\u002f2HK+StzMyiWEYEY9H9p1TLgzrz3DCHXOlw5vOIhGJm2jQmN4Z8N5PiWUiNAQAABpVL8+x83vh+1X6Uo1J6WDGVAD4cMgGNNSdTu9KC31iNhp1jkapu8iWcBeKBc4k3AFg8uKR\\u002f3zWPAWZRX2V\\u002fOKRjBHTBiHxDjm+\\u002faJWzXLEJyzs37f9IwYEhcYFlOZUIfHygppLR7OGTcZd4bqqoqLxo492mUwUAAiFvOSh4T7eUoORUsi1+QU1KSnR6BPs43JZ7qhhPamkoen5jXvSSivRBgAASAjwXdCrdcDFfJ6HSFSv1iBxndGIRLpuULD\\u002fsN6Y9dQeqYC9yvVHe4\\u002f9xpZtF\\u002fK4X9++cFz3VNrB+g69ZqNluSPTpWLY9YnkhJOicG6n7sr0OoV2HwOtfRcAQKHdI3ZZwr0FIeYIxnME49E45px+\\u002fRnrMV5S8WhpGAD8cux8uwn3z2+fj4YwDOssnHDHXCm3TdVpmYcE91rtEJkH+zBheV07v47drdFYKyCFAn4fKikDAAfWn1n6+KzOfcb2rTtlNKAz\\u002fgHAiGkD+fZ7L1qbtWLcv5\\u002fvYdoMPtjw1b6uJNwZhtn0zT40CgAA05ePQUP9zdw7J\\u002f7w8no0CrDm\\u002fe3JE+K7ODIA2\\u002fFfmjnbHhzs+cWXKz09bbpBtfmosli1avyaNScCAjxuWjQMbcN6SmZlzb\\u002fnrmy+eM1I2VztW3BI8vWF0zj2RkT1FHexsG3CXW81usJVlgzv\\u002fDdqd5AK+GgI4ON9x389wZJtF\\u002fF5391xU0qnprfF+hGarjXqj5mXCUIgEC2yacYwrF8x0TYTkgGAiWIfbdY38TiBHuKbKbrRRDdQdKOJaqCZbhl\\u002fhmEYhtmDE+6YK2nb9MKuq2xUNKjcvKRIHLOHy+cKxQKdBt2T2ZeKkEgPe3HAt2ioDyjNqTqz5\\u002fKYOUPQhvYY9MbN3+5HowAAMHmxs2WCA8J9xswdcmpnGhK\\u002ffDInde\\u002fllFlJSNxJB\\u002f49U5hRjkYBRk4fFBTpi0b7mxnLx6z9cIdajibpsi8U\\u002fvP57tufnofEsQ65cqXUvHDLklFIth0AnLkztXRZytJlKWgU6x40wxgpSmMwNqq1VQplUV3T1fLqs4Wl5U0KdFVbz8wcPyQ0EI32OBFbKXMGnLix00HDw4PRUK+SCNGE+yf7Tvxy\\u002fDwSBACJgP\\u002fDykV9bfux7qDXbISW\\u002frB84XyCdLNtxzCsP+GS\\u002fkiEx+lDA63aJeDFhHp\\u002fZR1hGIOJbmhQ\\u002fVWj+Mw6jjlJpTdMfPeHcG+PLY+vRJrmf\\u002f5HZZPyxEsPiPg8ALhSVrX5wrXzheUVTQqGYcK9PRYOS7xz3DBLnZY1Jy++v\\u002fPoiZce2J6Wuf7slYomhZ9MOiYm7MmZ4zwlrYPdGQb+PHXxn9TL5Y0Kfzfp4hED4wPRK8F238tJuy5nP\\u002fPPLsvD1FcflgnRwXwT3v2hXoVewQHAlbef4JCtXabOF5b\\u002feCQ1vbRKbzKFe3ssGj5w1dhh1n1EnPl3Ydj1ASfcMVfy8kevLkxG6tPH17zw4z1CMfqVjdkTGuuf22Y60OwLhU11Sg8fGRLHfnxlw9BJCR39gK37eFdteSMaBfAO9Bg9uwPp+xXPzj+9K71tJ\\u002fevnvkrbliEpy\\u002f6F9Gu6tL6n15tHZNubcUz10MyWuImuvXxWb++tQVtAFj3ya645PCuDA7A6lqGwkRH9+Mpoa5LyW\\u002fYXPd2xcoxQ1eP69\\u002fVpTpEJhRE+3qj0V6F9HD\\u002f8uCpn4+fs46YyYSCn1bd3BdujWDdjjHq1L9bHgkld7U2YRjWD7mJZlbLP7BUlSEIvof4JttV+hmC4PM4ATwOPj\\u002fsJKmAPy0xemd6dk5VXVyAjyWeUVFTUNNw09BEc7YdAH45dv5kbvG42PBpidFGijqYkf\\u002fRrmMaveGRaTYjlZ\\u002f6Z+eVsupZg2JnD447X1S24dyV3Oq6vx9sLUf2xf6TPx45G+Xndc\\u002fEEY0a7V+n03zaTGTq5Hu1a3xs+G\\u002f3LmlSa789dCa3uh5tBgCA5+dN0lmNDtcZjR\\u002fvOe4mEpJEa7Z9R3rWC+v3hHt7LB4+UMDjni8s+2jXsUvFFV\\u002fcvsByC8CZf9eNI2XLF7U6NYcgs259jmO1Jx2gGSb+3w8ohvYVSgCgK09PvfmJMzXFtx\\u002f8C12pPdtm3TXYq\\u002fX8Nvaf96g2+RAhhyvjCUKlHoO9AueFDRjh29NjPT8+dAIApsZGDQt16nbpxrRrKoN+aHDQkODOzxGIwAl3zJVC2KavTN17+b4xr8+8fWzSuLjgKD83bymPz+1cDZAbxKAxsW0T7kaDafN3B+5+pVumBurXqorrPnr4t5d\\u002fud\\u002f5aiTnDlz998s9aBQAAG5+YBqH6+zrAEDUwJA5q8bv+uM4Eq+vkr++4tt3NzwucWevEcSqqU752u3fKBtZhnxOXDQifngkGu2fbnpg6t6\\u002fT5XnVyNxk5F6\\u002fY5vH\\u002f\\u002fkjpm3j0WaOqrgWtn+dacfeLuPz\\u002fjkenp9c\\u002flsoZCl6zF2HbhnwohnZrYz8+11JjHQr6+dMlgn3P85d\\u002fm7I+h08QDgLhL+snrxwCC0jyR2XdKqvqOp5qFpPMFYLj\\u002fZphnDsP5GwIsJ8\\u002fm+Wv6xwVQi4EUHuL8k4MWgK2E3mJuGJu5Mz\\u002f4vLfPp2a1nYv9dygSAm4a1Tqvz6sKpYgFfyGtOtT00dfTsj3\\u002fdeP4qkgRPK6n89+Hb41ty9\\u002ff\\u002fvuVETlFaSWVyWCAAVDQpfjl2LsrXa+MjK8wv9dCUlCXf\\u002fG39CuD0e7XLTSRMiQoFgE3nr9pLuM8fkmD98Pn1eyia+XT5PMtJWqNa+\\u002fqWAyMig3+5+xZLn\\u002ffn1u\\u002fekZZ1ODN\\u002famI0OP3vunGEy7xqdWqKocvVijCpB9rMpk6nphgaAEKlHiRBduXpaJtL6SiTjjLV6tQX68r\\u002fyDk\\u002fzCfko9HzI2Ve6Hrd5sdT5wDARyp2MuH+1\\u002fm0a1U1iwYPGBLcyTkC28IJd8yVZtw2lrWkdV1F498f7\\u002fz7451IHEFySImbSOImknmIw+IDY5PD44dGxg+PuNGy84PHxG75\\u002fiAaBdj0zf6kcXEjpg5EG3pQgSqjWJOjo9ToMW4xN3AFGup+p3amvXnn9898vVpqpwK+tdO70j548FeGZvkX+AR5Lrh3Mhptz92vLk7dd6W+sgmJ51wqenr+xy\\u002f9fF9YvFPdG\\u002fMul7x7z0+VRbVoA4DEXfzgO8vQaL\\u002fFF\\u002fCe\\u002fWb1U\\u002fM+oikaaaJM9GdPrLl0NHPlCws7UT+nLK\\u002f63IGrB\\u002f49U3C1FAButIS7Tmek2T7Y2PVBJhS8vnDa3MHxaIPrMAxUK5SFdY0VTYoapbpepVHq9Wq9QWc0Nf9nMumNJp3RpDeZdEaTvRLzruUh7ltzh\\u002fC5HB6neUrhw1kFb+84ZNsOAOAlEf2y+paEgA5\\u002fiWH9kV67TaP8tOURIZa9aN2KYVg\\u002f5Saa4yaag0axG9iYmHBfmWRHetb\\u002fZo0312yhaGbX5ewAd9moqNbeu15SmwtSqYAfF+CbWlBCM4x1pZc5g+Mt2XYAmJIQdSKnqKiu0ZxwP5SRT9HM8pQkSzLdz026MHnAr7b165x8L5f7JzX9v7TM5+ZOHBHZWjRv95UcjcG4cuww6woz84Yk7EjLOppdaE64O\\u002fnvunFEyDzP15YCQKmq0cmMebW2eUBzsMRDwOF05ekAIOUKBnmhvWZNNJ3V1DyPxQAPP+sDaibisnftGuYTIuXxAYBhGLXJWKpqrNU1dyW8WFe2dP+ajTNWRfRgzr1D\\u002fGXSa1U1WTWunK4DJ9wxVwqK9GXt7eskmqKVjWplo7qqGHLTSw6uTwWAwAjf2XeMm71ygpvXjTLUaMT0QZ5+bo01aA1fmqLfuOPbVS\\u002fedNN9U\\u002fjOdV9laIZw0ax6FEOtKfowU3ERbbDVKwl3AEjde\\u002fmB8W\\u002fc\\u002fvTcqctGiyTs5WVqyhr++mjH\\u002fnWn294TMnvo3VudnC7VmsRN9PIv9z+36FNTmylYi7MqHp36zry7Ji16YKp\\u002fqN2SCOUFNVu+O7Bn7QnKhCagAYAgiGe\\u002fWe3p1+HqNH1Z\\u002fPDI+95cwjp7KgAc2Xzu+PYLk24emTIrKXlCvINJIJSN6rK86pKcyiunctKOZdVXydE1rmuFBbUbN56tq1PW1irr6pRqtd7S9MD9v1qtCAAQGur9+x\\u002f3I0EAOHwo4+23t6FRgBdfXDB9RvvlfdauPfnbr8cAYMPGx728JABQWFD7345LaZeKa2uVDMN4eUmiovxGjoqaM2cIaefryGAw7dt39fSp3PyCmqZGNZ\\u002fP9fNzGzosYu7cIZEdv+9y\\u002fSEJYl5S\\u002fHOzJ\\u002fpIXf87SDPMlbKqY7lFF4srrpZXqfQGdI3e5ibqWwl3S\\u002ff2y2VVT63fSbW5y+UtFf9+15IYP7vf+Vi\\u002fpvt\\u002f9u46rq2zewD4iXtIIMHdrUCh7u7etV1t76Rbt87df5N376xzl87qurordSpAcXeHuNu9vz9CQ3IJIUCw9n4\\u002f++yTnOeGpOSS3Hvuec6j2goEIpHAQcGEmKr12rNGfVuagM56mEwdarU5DofD4e4RJCJhXlL0X5du3yyvMdeDXy+talaonpg0wjq7rTUY993KuVhYXtEilWu0GoNBbzQBAIqi1uspxfjaHOK6MekAINdozXdLm8UAgGluHuGNPbRw8rlcK6e28dOjqTPiIx4eZ9PhMLemEQCe3XrIOmjWcrf5u5P\\u002frvtHIJtvvlGllI61HepIg7o1Yx7AdqOTWnMX3Xs4AMS7ex+a+ajNFgANasWYg9+bb2+dsopP67yo0ezDYTNj+TaTO4tkzd\\u002flXD5WlQ8AYp369bSju6ZhV0EYIMxnHE0KJXagB\\u002fCEux1Gg6mxqkWt0KrkGpVco1Jo1HKNSq6xRNQKzd0bWoXEzsIR109lr018k8mhs7gMJpvO4jKYHAaLy2By6Ewug8mhsziM1lEOwzPA3fkcX0udRC5RqeUalUJrflUdvUJpc+sfEsaq2NdaX0PbK2l9bZhXyPd060aO+6lPHmyuldw8k4Md6K76iua\\u002fPjqw76czT3\\u002f24IRFw7DD9yIKlbz4yWl\\u002ffvgvdgDAaDD9+eG\\u002f+348PXx6fHRyME\\u002fIZbBoJhNi0BmVcrVKppGJFJJGubhJ3lwrbqoRv\\u002fTdQ+PmJ2N\\u002fSrekiU6bs+0BzHABzTtDcllI8\\u002fGiByqM0mp1CYoic3zWRHASsA\\u002frNVEpIYgJKc6stETEjbIfXtux6f1\\u002fE8dFRqWEeAcJ2G5MApGglmtqShuzrhRlXy1uX1JtMfmBEWPmJmGjzokZHvrsF6u+eX5L+1S+QW888OvZg7+dixwaFD86wj\\u002fciyfgUmlkvc4obZZXFdVnXy0uycJ2ELK29o353Vh\\u002fdSB\\u002fjpktemJKY5XowK92JnMAgMmInNuTdm5PGoFA8Avz5Am5XHcWh8cyGoxqpU6r0qnkmobKFrnYld+Ig051tejEiSxstJ+IRAp3d9bmfy5v3nzZ+g+htlZfWyspLm6Y28HfV0ZG5cbPjzY2tl0sMRhM5eXN5eXN+\\u002f+9tXTp8PVPTukoU3\\u002fPY9Oos4dEPTI2JUTQejjuQjUS2a6b2Qcz85oVrbUnAxO33bJd\\u002fYtNowFAlVj61NYDWgP2OisAmBCESm4tgcfde3Tq7UaD\\u002fQ9eMnU4k\\u002fsONorD4XC4e8Wi5Li\\u002fLt0+nJFvTrgfzswHgAVD2\\u002frJKLS61b\\u002fuKmkUTY4OfWjsUG83DptG\\u002ffLEpZxabC9ND4dVFCqdHgCYd\\u002fvCm7GoNkvIOP9cLiTTaF\\u002fYdsSf7\\u002fa\\u002fpTMwQ3KtFgAeHpdivfSrWZAHz3zDmX\\u002fXfSXobsK9WiW1GeiYpUQ9gMVjklt\\u002fdd17uM1A74h0E\\u002f4wdvG7VMa2knQAuNlcfUdUl+jhVI+XPkYnkwFA4dLaIzzhbkd5bs1z0z\\u002fBRrvCqDe21NlZj9Gu\\u002f+561vkmIe+s+L6yoA4b7QpJs1zSjC2dtmvm6rEvfNPlq09kCumD7U8f\\u002fiP1t3d32y3X7R65WPnJ45uyrhQ9s3EVduxeNO+RCcf+vthQZX8+i0ykOLPz2pmd17ADvSlTegUA5vismeS5EACypNejOEMX+D0CAC26+j\\u002fK\\u002fpcjTxsr7Lt5l1w+6\\u002fmv1jw77WPMVACtWpd2KjvtVLZ1sFMRiYHPf7UGG+2KGSvHaFX6n9\\u002fciR0AAAAURQvTKwrTK7ADnVn23MyVL83BRp0wkD\\u002fHLNZ\\u002ftIxCI+\\u002f57iR2wAqKojUljTUlvXjgOHiFhXtteHqadeTPP1K1WgMALF8+UiDkWA9xOPbLhMeOi9y8eb1MrpHJ1DKZZvu2q7X21hPulEikvHr10uZ\\u002fLgMAnU4JChLQGZSWFmVjg9RoREaOCsc+AAAArlwp\\u002fvCD\\u002fUajCQCYTOqQIQECAUerMxTk19XWSlAU3bv3hkisfOedwb1SWZcQCYRgAX9EsP+Y8KAJkcE0susP1URK9VenLx\\u002fMzDchLvua7j2W\\u002fi0DBItGlag1j\\u002f+zX6zSYMcAAECq1m7YdmjnEw9i1lbF3duo9Ols3ncEwsC6PoTD4XA4F4rw8ojx9TydW\\u002fJ\\u002fC6ciKHomt2SIv3eosK1FxparGSWNoofHpbw2Z4IlSLa30pjjAnRzSlqibi14N9MZbVr5Of9croKi8Oqu41K1ZteGlax2BznmioRpceHJQR2mU535d\\u002fWl+WPj5o\\u002ft8jmsCwVx7ibclVKbgY413M2Y+7PcONTW87vuPdx2pBe9ljR5f0W22mgAgEsN5QMz4d6sVAGAa3sxuf4sDnefy0jNP7f3xo1T2S7Mtlsc\\u002ffsinUlb98FS7MA9h8Gmv7f1qZdmf66x6hHRv5p0tWQCZbxwrvkuhUjRI62vTUDzWRqw\\u002frfSDy81H5nsubjtMb1J0iT38OF9vPeFN5d8LW2xP5\\u002fDScExvh9sf4bGwB40dNWCdZPYboxvXthiaNdbphsIRMKj7y5+4Bls7cA95tF3F\\u002fsECX55a7f+7mqfOOf5+fGXLh1uHdm65Yo54T51Wlx4uFOrNVKpZD9\\u002fd7+7d0+dzO5ewv3kiaxLlwoFAs6TT04ZPyGafHfxYbVad\\u002fVKcVBwW5NKi4YG2aefHDZn2+fPH\\u002frE+slMZluu6szpnC+\\u002fPK7XG8+fy0tKDJw3f3B3aXggJb59A0QAIBEJVDKZQSHzmQwhh+XPdwv39LD0tewNx7IL3z90VqEdKF8ugw6BAE9uOVAllmIHrJQ2iV7effSn1YtI9+vkjHsYhTYaRbUI0oiiKgJQiEQhmTqMxnyAQmvLd+BwOBzuXrUoOfaTIxcuFpYDgFpvWJQcaz1a2iQGgBnxEZaICUHKm7t8aG1O4hc3toyLCLIEy5vFbVu47rmc99O565eLKr58cE6Yvb558f5eBzPybpZVO0i4O\\u002fPvuq9YKtyrnM6YN7b1hOG5UVsnE3Tv4TYDvYlDoQ0V+F9pKAeAKmUv7qLdJlZrblbVAIAnx9HUk67qxTM63P0m7WTWPx8fLM+rxQ641L6fTg8ZGzlyxhDswD0nOMbvrT+f+PTxTSq5\\u002fRq6PqY1qd0o7iRC64cGjcjQmNq6EISzh7DJbpmSK32WcJeJFAAQHOP7xZFXPnjo5+qiBuwWzkkYG\\u002fn2n+u70T3JrinLRvqFeX3x9F89rMgW+PJf\\u002fGZt8mSbA7h71eyHxkcPC\\u002f36+c3WDYJ6jtib9R249i5eLBQIOD\\u002f9\\u002fLCHh03PfSaT1lEv+D82XVCrdQAwcWL0Cy9il4OfNj1eJtf89OMZANi8+fKs2Qnkwdym4515k3ujVr2rvj937afz17FRexhUSqC7mxeXLWCzOHQah05jUilMKoVJpd69QXl62yGRyk5DqntbXl3rKlKOXSyq2Hjy4huzJ2IHcIMck\\u002fsuk\\u002fsuNorD4XC4+8PcxOjPj108k1dCAAKFRJqTaLOavReXDQANMgWAjzmyKfWm7G5ndudNjgn7\\u002fNjFXWlZDwyL59BpAKDQ6swdbCxc9VxOulJc+fO562vHDJ2dYPNPtpibGP3tqSv\\u002fXEmfkxgd4N5WPS1Sqtl0qvkw2Jl\\u002f133FjUp3o9Jlem1XStSVAEAkEHyZbmQisScPx471Jk96a75Foe+toh+13k4Bn8GE2I1bqPT6gsaWL89flmt1AJDs3+Hlom7o\\u002f3M\\u002f3D1Aq9Z9++LWC\\u002f\\u002fexA4AAACDRQuJ8+d7clkcBplqP11iMiJGg1Gr1stEypZaSWONCG23CpnFpvf2pkyOJVPs\\u002f6h7ybApcT+cf\\u002fuTdZuKMiqwY32OQqCqjG3NW5hkrkhvk+N2p3o2aB31InctWYvSfMMvzOu702\\u002f+8cG\\u002fx\\u002f655KBFe3tUOmXlS3NWPD\\u002fLVevKmkUlB\\u002f944Z2dXx0\\u002f8OvZbkxQoNIpcx+esOa1+cwOGoDck0Ji\\u002fb49+cbpndd2fHWsodJ+JyXn+YV6zlg1ZtqDo7EDuF729NPTMNl2B8RiVWpqgfn2uscn2YzdNW9ekrlJjkikvH27YuTIMOwWuK74\\u002feJNx9l2Tw57SnToyNCAeD8vf37nR+G9Om15ECERCe2XTgWAf66mR3h6LE2xf8EJh8PhBjWNPkuuOaPW39Yby4wmEYJqiQQ6mSSgkUOZtOFcxiw6xX5WrivaDtFRVCfXnFRoU7WGPL2xGkGVBKCQiDwyScCgJrFoI7mM6USCawpo+hIKJrXulkp3VaPP1hsrDKYmBFUDaiIQmUQCg0LyppIDaOQwJjWZSUshEV2\\u002foEtv6JN9o9+4sxgTIoOvlVSZUHRSdIib7dLu85Oit1xN\\u002f+jQufJmCZ1CTiurTq+oTQn2u13RtarEAHe3h8Ym\\u002f3359rIft0+NCdObTGfzSgM93KrFMss2zjyXzmjMqWlU6vRKrV6i0qAoHM7MZ9OoLBot3NPdnc0EABOC3KmqV+r0Sp2+Ua4EgONZhQI2i0WnBgv45rS+WKl+bfdxJpUSIuTvvZlj+fkAMDU2zNy0ncekf7Z81os7ji75fuuchCgvN7ZYqS5tFt8qrznx8qN+fC449++63wSx+VnieqleozDoOJTWyb4NGsWYA9+bbx+dvS6G52nZvkEtBwAvBodMJEKPH95ntKbWHgAcam8130v6\\u002fAdsCGDj2Usbz17CRju2MrnLi+c5gCfccT2lkqnfXflD\\u002fs0yTJxMIU1fOWbm6rGRSUFdTWhq1brctNIzO6+l7r\\u002fVfhXKmpLGG6eyu72+5eDiHSj4+vhr147fOfDbuZxrxdhhh\\u002fxCPd29Os+YOElA867VlEsNIh7FAwC8aH53pFflBgmX0nrkpzBKiYS++9TW6wx6rYFKpwAAnUl7+rOVcx+euPeHkxcP3O60owuTQ5+2YtSyZ2cKfHvlsJVKozz05oKFT0w+8mfq6R3XGqtF2C3s8Q4STFk2ct4jE\\u002fmeXOzYfYBAJMxYNWb6g6OvHb9zds\\u002f1W2dz9VpH16IxSGRi1NCQpAnRKVNjY4fjadl+wOHQx0\\u002fowulTRkaFyYQAQEio0LeDv0QajRIe7pWTUwMAWVlVeMK9J26U13xz5go2eldigM+GSSPHRQS7tnHh\\u002fWBKdNjbcye9e+DM1VI7c3TeP3w2yIM\\u002fLNjStAmHw+G6T6zcUit503KXAKQo3xsUklMd5DC0hqLihinWkQCPH3jMRdaRjsg1xxtlX2kN2KJUBFXrjVV6Y5VCe6FRtpFJG+bt9jqL1s0CCAKBSgASAKBgFCn+apJ\\u002fZ0JsGhGgYERMGoOpXqPPFiu3kIg8AedRIecZAqELXSJRVJdT49TRRYT3STrFlb2ejYhIpPhDrNpuNNmpNUERBQIKo6lJo8+6GyMwaSlujDkCzjqAvjvn6pK+2Tf63cLk2PMFZQCwcCh2OnKMr+dPDy36\\u002fvTV31NvUEik5CDfretX5NY1djXhDgCvzp4g5LB23cjaei3Ti8teNSpxxciEkR\\u002f+ZNnAmeeqlcjX\\u002frbbchcAXt99wnzj7fmTV49OAgC5RrfGdpv3D5w133h66qinp44GgGqJTKLSAMCHB89ZbwkA0b5Cyyqpk2PCdm9YtSn15oWCMola48agB7rznps+1oPNtGzf6b\\u002frfhPE4WeJ6wGgSimN47d+nl+qb0uvpdaV2mTMNQqwagjTw4f3mWJZ6wedX69V1vtwOfVyBTbaFc+MH5UcMJAq3GVS9bIZXwDAH7s3BNjr0NpnTh2588WHB4NChb\\u002fvfAo71kURSUHHm3\\u002fBRgeGXy79HzbU3zZu+Kt9tj0gwvvNTY+HxHbzDJPOpKVMjk2ZHDt8WvzGDX9hhwFunO484T5l2cgpy0Zio64QGh\\u002fQl3sIkUQcO2\\u002fo2HlD6yua89JK82+VleZUK8QqpUyjUmhQBKHSKHQWjSfguHu5eQcJ\\u002fMO9AiN9IpKCOHxXVnmEsGJqNeXZ0uvmNu7hnIRM6ZUdVd+uCHyWQ+ZdE52U6Jv9GKHYh\\u002fUmlUJjTribBcf4vvLjI0\\u002f+b0V6av6dSwWVBfUNlc0quVan1dPoVDaP6RsiDIn1T5oQPXRidM87tltTG\\u002fXxu78AgBNzHo\\u002fkCc1BNw\\u002fO6lfnrXplbml2debFguLMyprSJlG9VKPUGgxGOoPKYNOF\\u002fu5IituZWNlwnv9fc1fb\\u002fNCeGcifYw4QiIQxc5PGzE3SqnX5N8vyb5VV5NU1Vota6iQalU6n0RNJRAaLxmDRGGy6wIfnF+blH+4VEOEdlRzMYN9HcwIGoIhIb0JXcrUFBfXmG6EhbYeA7bnfLZlvamybYYPrKhOCfHjkHNLuArbZ89PGrJ8wsivvHg4AwIPNfHvu5NnxkQDwzYNzH\\u002fxtZ1m7JqRGE\\u002fLcjsO7n1zpzIyB+8GO31P\\u002f+bH1NN7aN5sfj04IwEY7s\\u002fefK9t+u+DuwX7vm1WBoa1fvl1SWyU6uudmzu3K+lqxWqmj0ikcLiMgWBCTGLDmycnYrXEDQ8\\u002ff98HLjbmoXvoBgra2jEDBJFX\\u002fK+R05+RXqtpjfZdE5HAZ2N5u7RkRcY34RYXGzl9xe2rdrbKmZe7sNb68D7uUBDcjETgAYEIklS3rVLo07HA7JkTaKPtKoTkfKNjUvYsQfQgVKf9ulH1uQrqUG0LVuluAIgLOE9iRAaAv941+NyM+Iu\\u002fjF7HRu8ZHBo+PDLaORHoLFie3Xa15aGzyQ2OTrcYBAOYkRM2xbdVCIMAj41MeGZ9iHcQ8b6fPFSp0d\\u002fBSzfgsRqfbJAb4dLqNWaS34PMVs7FRK878u+4rljbuNVYZ84v1ZQDAodAUBt3F+rInY1uvTqkMepVBDwABd5c87eHD+0aOpKFE3ppwH+sdbDPmOqnPrSsXSa6UVV4pr0qrrFbq9NgtOsCgUFICfB8aMXRSeAh2rGd6mnDH3edO7biadiobE\\u002fTw4X2893mX1A5PWTbyxuns1P23MPGMVOxl8\\u002fuBT7DQJ1g4dcUo7ECfGMoff7nlWJ2m3Hw3iTf2RP32UmXux3lPEoCAAgoAIzym2jyml81I\\u002fVOkV5+etz6M62EJsnnMCQtTJixs\\u002fQovV4inHv6FT2PeXvqCZZu+RCAQwhMCwxMCsQN3fZxxVpufdk1RjR24v9GZtKETY4ZOjLEOvnr98L6ybAB4MWHCs\\u002fHjrIc+Sj\\u002fzZ8ENAFgaOmTjqPmWOIKihypzD1Xk5oobJHoNg0QR0Fm+LLdx3iFT\\u002fcPDuf15nfgew3NrK11xhljc2hXq7Nncs2dzbQftUCh6qyXl\\u002feBMfmlpk\\u002f2pNv83f8rKEYnYqHM6yuDfD5Ykx70+awL37kRyDp3285qFy3\\u002fZ0b53qkSteWrrwZ1PPMiiDb6cwgC37bcLGpWuVqU7vu\\u002fW+lcdnd7bde7ona8\\u002fOGg9K06j0mlUuqZ6qUatxxPuA1YP3\\u002fdBzZwWl6oPWCJS1Z5uJdwRqfpf6\\u002ftuzEVEQieFC3pjRXnzKr2xaw0kxcqtOkNxsHBzV\\u002fu9EIlsBFWVN6\\u002fS6LEnmw6o9RkVzavCvI52+s\\u002fpLwiqqRY9Ldecwg44x529ChsaAPp438Dh7iWBdzPm1Sqp+QaColcaKgBgeVjSHwVpt1qq1UY9k0yFu\\u002fXpAODP4plv9PDhfUCsU79y7bD5djTPM1ngbzvuSiEe\\u002fBAP\\u002fprhSRqDYejnPyIo+vLksStTHJ3pUEgkBqW3EuO99XNx9wMURXd\\u002fexIbBVj3\\u002flKXZNvNpi4f1T7h3lIvRRG0q51qcD0RwAx\\u002fJuLjQGbrMuhUIu2h4Fc2V3yhNMrM2fah\\u002fPEj3fs04R7AcBPp1dVKqXXCHcO8DLd\\u002f317C7ZJx3iE7SzJnBkRiB\\u002fpQg1rxc97Vp+PGejKcbcDdv7JErcXRFndEdZgIAKiN+nWpe643VloiCkSnMOjKFeIrDeXpLTW\\u002fTnjAanNcj3R1XQ1VF1c4MPefwXXP\\u002fgz7lzQmRYV2O9sOAHJN197Ee8aL08c+MWEEJhjozvtu5fzH\\u002ftlnbLevljSJXt5z7KfVC\\u002fGOPYtWjx43LU4uVcmkaplE\\u002fduXJ7qx2El7pK6vqFxfLf7q\\u002fQNGgwkA6Axq4vAQDyHHoDfKpOry4sbh41uPdnADWTfe93sAn\\u002fWgdcJdayjS6LMY1IS2LZyg1F4ymBqtI3zWCuu77RlNTWVNKwwmbFsMGiWCRRtJIXkRCWwTItEbq5TaS0bE5hKvSpdWLXomSGBn1rIDRAKjTvK2dbadQCCzaKPplCgyUQgEotEk0ugz1LrbKNg0k9QaChukH\\u002fryP7YOdohA4rOWGxGxySQ2IRIjIjYhcoDeupyMorqK5jUdFeyTiFwqOZBEdCcR2CZUZTQ16YylKNpWpEkksN2YC6weMSD0\\u002fb6Bw91LgjmtqbNalcx8w9yTHQDWRiRvK76tNRmvNFRM948EgHp165Rf\\u002f7s9YXr48F5iQhG5XleuEKXWl20tTpfo1ABAIhDfS5nRNwfDDAol2J1fJhLTKGQunYYd7it4wh3Xffk3y2pLbY7VAMDD223i4mGYYE9EJAVhQwCICZFLlG4eHOwArjdZsu1mwazoN2N+LFPl6xGtNz1QSHNluytn+DG4mbJ6x0tyN\\u002fZTkzLnTfAJzVr2Mjbat87XlWwpur06PHlQJNx9Wdws2\\u002fS6CUXzJI3uNKZYp7aOf5554XpjJZNMeSVx0jT\\u002fSCGdpTTo6tWKG01VZ2qLV4YPtd4Y18cYd\\u002ftBRUX5RMd0\\u002fukRGNjhdTWcYwaTKa3M\\u002fhyaF6ePxYacptEbdMZOFsy4V7mz7M\\u002fnGBHi\\u002f978qe8eOI0dAEgtLP\\u002fy1KVXZ07ADtxnGExqQIgAoHV20eYfu7O6uMWa9ZO2\\u002fnrB24+34EHs9Y9OHd17y5xt9w1w\\u002f\\u002fLvdXzbBZ\\u002fbLyCEsXbWl80Nsv1X32EwB9\\u002fEhf598T1\\u002f9p687\\u002fcANn0slRygN7Z9qkvUe7uacJfY9pOhUyKZ1CTrSHs14pcwGVUOfbI37006BdvDGgCRqPY1yD42mpotIbnmtEjxpwfnUavNOqE1FGgNrYurEwg0IWeDkPMEkYg9+9MbK+sk7yq0Nn2lxartQu5zFJK3ddAuApD93b+yjqBgMpkkGkN2RfNa67hL1Ihfbp9tJxJYHpz\\u002fcBmzGdQEc9t6CxRMOkOBUntJrjmr0qXxmAuIBPtfQP2o7\\u002fcNHO5eYilRt2TMzR3YfZjcQDY\\u002f0cM3ranqYn2ZOWPeoLaUqLcWFPbw4a4178Qf2NBdVCLp05FzR3p2OO\\u002ff5aK9hGUibLPHPoYn3HHdl3WlCBsCSJoY06U2vp3idtCIXK3Q4gn3fkch0qI4SdhoX\\u002fFnuAFAlcOEe4NaCb32jXLPuNxQjg0NYNE8z3O1JQ1qhTez9ROgWNasMRpGCANSrdaHAYBDFbkA8FbytFV3c+s0EtmDzop39340+n48Sx9QeLzWz\\u002fboaJ\\u002fnnpthO4hzpYoWqdZgJzMeIuBHenW\\u002fq1KzUoUN4QAeSIkvaRL9czUdOwDw5+Xb4Z4ei4e2dVbF9dDSh8YufaibF41yM1onPy1ZOwaTbQcAx4eyNRUtzQ2tp7WDTv++eJc8e0\\u002fe93sCgcdc1iRvyxHLVAd83P6PQHD2vB5BlXKNzRzlTsvbRcrNCu0F64gn9wUvt1esI1aIfNYyFm1UefMK6x4j9bKPOIzpVHKXV2sgEXmhnrs6WrCUSg4KFv5dJdogUx+xBFHUKFZu8XJ71WpDZxGARCYJqEiXX2enpOoD1rMTzNyY83z5\\u002fyMT7VcVEIBEp8TRKXECzpMGU2Pvld53W\\u002f\\u002fuGzjcPcCTwWaSKWqjoe5u+fnFhjIAGOMVBADDhAFpTVXmCFiVqFsKCnv48D5AABjrHfLm0KnWa7f2gRhv4bG8Qmy0bxGxge4ikVz2o3CDRVUhtqsDAASEd15K0CVIu6nZZq5d9BI3GPnTuQBQrZRgB6wM\\u002fAr3fmdATNZNVwY+cwch6x4y5g4z0Xybr3ADYjJPphvi7uIPJZxLREf7mG+UlDTZjuBcrFoixYYAACApoPOJBQ5kVtk5BsABwGuzJkyIDMFGAQDgvYNnb1diJ93j+oWoqfWcMySyy98R6ddLsaHBo39ffP8++z2Dz1oG0HZNyIiIFdqzVuOdkKmPIqjGcpcAZB5zqdU4lglRNEg\\u002fto7wWQ90nFFtRSUHBHj8aJ1tQFF9i\\u002fJ3q02cRAwWbu4o234X0Y\\u002f\\u002fOYloU16j1F60vtvvUFTfIP0fJujBeTTQ4+eOsu0YFJKXMzX7fam\\u002f9w0c7h4RwOYDQJNGCQAKgy6zpQ4AxvuEwt28ebVSWq4Qw90m7GQi0YvRVnvaw4e7ULLAf4JP6ASf0Em+YfMCYx+KHPbpiLnn52\\u002fYPHllH2fbAWBsSODihNhwgVMfsL3E2SvhnaJQyWmXi\\u002fZuu15SWG80IgHBgtkLhs5dkkJs12X7uUf\\u002fKMip3XPqFS6XcWjvrVNHMmuqRADg6e02YWrs2scnYra\\u002ffqno6P70wtxapULD4TJiEwIWPzgyIdlOmxG7ThzK+PrjIyQi4fUPFk+cbvNVXV7StHfbtTu3K8QiJZ1OCQn3mjE3cca8RLwzuJPkd5e8s8Z1t1+Q3m31la3LGVsjEAmcDirfcS6hMrbONuoqFrlXPrvt8mNwwWp5ELvM06b8rCrc74jqDlXk3miqqlBKtEYjl0oL4wrmBcWsCk8mE7EXDlGAI5W5\\u002f5Zl50oaJToNm0L1ZLCjeZ7T\\u002fCOn+UUwyK09MSxIRGJaU9WvedfuiOoVBq2AzhrtFfRM\\u002fLgQjjtmyw2X\\u002fj1R3TpPFgAi3IQn5z5uNd5KbdTH7\\u002f7CPOrkTwaAvWVZu0vvFEiblAbsVP2RnoE7pq0BgAqF+Kfcq\\u002fmSxiJZiwExAcCsYzZHuk\\u002fGjn4tabJ1BADO1hbvKMm4I6qX6TU8KiNF6P9w1HDM1LDuvWbnRbgJASBbXD8zIMocMXeYieG1LstuRiGSuFS6XK\\u002fNlzQNcW\\u002fN7eIGjpRhIUQiAUHQ\\u002fPzaujqJr+tW\\u002fsBhKLVt7V+tCTg9mpaeVm6\\u002fTQ2OSCB8tXzOg7\\u002ftLGm3UK3BZHpux+E9T67y5XExQ7g+ptUazDfoXS\\u002fgGNRZ4\\u002f598f377PcMKjmATR+j1F6xRCSqPVzGTKtNHMH0k+EwppJJjmY7SVS7ELTtpI9E5Pry\\u002fms13iEmdSiPOV+qPmiJSJQ7vbgvYzLjjnmw1zCpydhoOyQil89a1qLYZIloDDkoqicQuvwH3ksk6r0Gk82FahZtpC\\u002fvfetrJ4NO\\u002f+4bONw9I5jNL5Q2SXRqBEWvNlaYUIQAMM47BACGCwPZFJrSoEutLw3huJtL1H2ZXJLVVLwePtyFPhw2M5Zvc0rej+J9vD5b4Ow3Yy9xWcL9+MH0rZsu0mhkdwFHJlWXFNR\\u002fX1CfcbP83U8esJu\\u002fbmqQffXR4WsXCwGARiPrdMbKsua6KJsOOwiCfvHhwTPHsgCAwaQKvdwkYuWVCwVXLhQsXztm3bPTrDe269Cemz9+cZxKo7z3+fJho8Ksh47tT\\u002f\\u002fus6MIgpJIRA8hRyZVZ2dUZmdUXjid++GXD1KoJOuNcXYZ9HamqGvV9k\\u002fsu+3OJTvTQLz8PShUl+29uPY+yH0UG3LO54k2B\\u002fG9qn1LmfUX956uKRrlFbR96mpzpMm2wj1TVLfk5N\\u002fm20wyxZvJadYobzVX32quTq0r+2PScvOQxWvXD+8rywYANoXmy+JKdJpiWUuxrOV4dcHVRc+2T7ifqSn+PPMcgUDwZnJQlNagVuwvzzlVU\\u002fTvjIcj3GxOZmYFRHnQmWKdukTWUiyzc1XJWoVCvK8s+7Xrh535ya+nHd1TeodKJI3xDuZS6XdEdZUKCQDE8r1GegamCFuXBRfrNNVKKZtCS\\u002fTwvdVcDQBD3H2YVv8izLQAE4q+dv3w\\u002fvIcAGBRqL5MbotWdbK68GR14frYUa8nTbHeGLr4mrsklOtBI5Gt103NEtfzaUxLhxmLqX7h+8tzNt65EMv3isfr3AcYgYAzeUrs2TO5CIJ+\\u002fdWJTz5dQSZjr3jhXMPOURgAAJvW\\u002fTSEzmhMLRpMraj6GItG\\u002fXnNwhW\\u002f7hCr2spIzcQqzVNbD+544kEmFfsNguszWo0eQbrZmUGr0WfdHKw7f\\u002f+++P599nsMn7XCOuGu0J41IRISsfNL1wZjDaaHeGf9ZFCx8h\\u002fr++7ste0bqXeEx1punVRFULVSe9GNOd9qE8cIQu7T2FgH2LRx1gl3FDXojVU0SrjVJv1JotxhfZcAZH\\u002f3r13Yb6A\\u002f9O++gcPdOwLZPAAwoahYp77SUAEAsXwvdxoTAMhE4jjvkBPVBRfryx6OHN6oVgCAP4tn9eiePhzXe1yWstz2x8X1L8xYuGw4mUJCEPT0kTvffHLk8vn8I\\u002f\\u002fenv\\u002fAMOzWAF9\\u002ffKS5Ufba+4vGTIxismgatT4ro5LHsym22vJ76pljWUwW7eV3F4ydFE0kEkwm5OKZvG8+ObJ7y1VPb7cFy4Zbbw+2LRf3bL32+3en2Rz6R9+sih3SmmMyy7xV\\u002fu1nR8lk4nMvz54xP8l8kp+eVvb5Bwdup5X+\\u002fv3pDS\\u002fPst4eZxeDTceGABoqm7GhHkAR9MhfqdgoQFRKMDaE629UIk1I88NGexOPQjdfsJXo1Hwa04ggVxsrSATireZqpUHHptDg7rQpS4V7kofvkpAhsXyv2YHRPkwuABgQ058FNz\\u002fLPHe+riStqcq6WDtP0rivLJtKJG2atHysd4j5w0WkVZ2vK23RqgR0O3MsPs88tyA4\\u002fu3kqebR2801T13a16JVfZWV+vP4pdZbLgiOWxAcBwC7SjPfTDtmPdSeATG9dv2wMz\\u002f5akPFntI7NBL5wMyHo3ieAGBEkOevHjheVeDJYL+bMt2yZbLAz1zqbi5IB4CNo+ZF8oSWDTC+y760vzyHTaF9PmrudP8oEoFgQpFjVflvph3\\u002fNe+6L9NtbWSK9fbOv+auIhIIcXyvbHFrwl2PmAqkTRN9wpB2a9y9mjj5akNFo0a56ORfcwNjVkckD\\u002fcM7CD3iOsHj6+bdPtWuVSqTk+vePGFreufnBIfb\\u002fNlrdMZcnNqr10vUat0r74213oI5zweg4ENAQCAXIudAeO8PbdyREo1Noqz4s93+37Vgof\\u002f3GswmTBDRY0tr+45\\u002fv2q+cTeKS+6h6WeyP7kDTvX9V\\u002f739IpcxOxUSsVxY3\\u002fbr0qalI0N8pETQqVUmsZenrFT1YbAgD4Bws2HXjOOlKcV5d+vbSssL6sqKGmUoTeTdYvHvOR9WZmyx8Z\\u002f+jzbd+2rlVV3nzmUGb27YraKpFapSMQCCw2zduPHxrpnTgidMT4yPYrkbrqxdfXSK6dz8+6XVFR3CgVqwx6I4NF8\\u002fR2i4jznTwrIWlkKPYBAOCiZ+\\u002f2+26tqUF2cv\\u002ft29dKG2rESrmW48bwDXAfMT5y9tJhXNszUIuMtNI31\\u002f8DAO988eC4abEmE3L2yJ3UE9mVZU1SsYpOp3j58hJHhC54cKS3X+eZbtfiMuaQiG+bEIX5LooapOqDHuyHbTayR6LeZ90HnEwSchjYmglrGn22zmhzmcSN0YWUKIs2kgAkFNo+BlW6G84nVVm04RSSsycXdEo0JmJCZZhIfzGamtT6DOsIhzGNSraZITro9O++gcPdS4Luzv8WaVVXGyvgbkMYsym+4SeqC240VRkQU4NGCe0K43r4cFzvcVnCffKM+KWrRplvE4mEmQuSKsqa9m2\\u002fvnfbNbsJ94qSxh83PxES3trHh8GkjhwbYb2BXKbZu\\u002fUqALzw5rzxU2LMQRKJOHlmvNFo2vjBwb9\\u002fOT99biLmsJJGa\\u002f0Xbf\\u002fz0t+\\u002fnHcXsD\\u002f9fk1wGLZb0B8\\u002fnEUR9JGnpsxZ3DZDLXlk6PrnZ3zy7r9H\\u002fr39n\\u002fWTWWya1SNwdvgE28nNXT1258n\\u002frbA7raEbdn93srKgDhsFGD5tCDaEc6nXo7+3uU8gkAmUW+LzJxt2xruNSOCNEdJ8aESGAdVL9M3ZsrQMycUFfo+McJ9q86jeF8Tm5Uoaa1QyPo15u6VGZdDPD4o9XJl3paFiZkAUgqJNGqWQzqKT2j7rvhhtcyRHIZLWx446Xp2fJarPaKm1Trg3a1UAIGSwzROyzDzorAdCEyx3MeLdfb4c3ZZDSRH6PxM\\u002f9v1bp642VNhs13VO\\u002fuRTNUUAMDMgypxtBwAykbghbuzxqoJL9WUmFCERulNKI9Fpfs+\\u002fDgD\\u002fGzF7VkDr+QyJQJwfFKc3Ia9eP\\u002fxVVuqSkCEsis0HspOvuatQFE0S+KW31FYqJEEcfp6k0YggQwV2zse8mZz9Mx955+bxc7UlhyvzDlfmBbJ5y8OSVkcku1HtXC+8DykUGolErVLpWv9T6lpaWs\\u002feb6dXICjKYtJYbBqTSWOxaAIBx\\u002fIl6xJCT+77Hyx5+609KpUuL6\\u002f2+ee2uLkx\\u002fPzc6QyKTmsUiZWNDTIURQFg1KiBUqE2GPFZ9hPuVSIpNuQcpU7\\u002fx6Wb2CiuneRA3w8XTnvzX5vFCc3OFZR+ffrKyzPGYQdwvaO6ouXUQZtsV5cc2nH99OFMbLRvIQiy6etT+7dds+SszaRio1SsKsiuObbvFoNJ\\u002fefYS5j0cc9ffGOd9NsPD7bvBqOUa5RyTVlRw8n96aMmRr31+XIqDTtvo+fP3nMoim7\\u002fLXXnHxet5+ZKREqJSJmbWbX7r8tPvDxrptUpYXvV5c2iZsWHL+4ozKmxBJUGk7KwobSw4cjuGy++v2jy7A4PDnsDkUB3Yy4QK7dZIhLVHmcS7lLVXuu7POZSgsOEgFp\\u002f2\\u002foumejOoMZZRxwjEuhUSqjOUGyJqHQ3rMY7waZ34UOyfTcSEzJQEu5K3WVMxJ31ICYy6PTvvoHD3UuC7mbAC2XNZXIRAIz3bsuYT\\u002fINIwCojYabzdUSnRoA\\u002fK365UKPH47rPY6+X7tk9ETsJeUps4bs2369vlbSWC\\u002f18uFhRkeNj7Jk2+26da1EpzOyOfQJ02IxQ5NnDvlh43GlQpt+o2zsJJvnZbJoAPD3z+e3\\u002f3XJ15\\u002f\\u002f6Q9rvX151hsAQHOjvDCvDgAmzYjHDA1JDgIAo8FUkFuTMtKmBQ2uveiUtiykRUud5NAf5xc+7qhWwkkntlz+5+OD2CgAy405bv5QbBTnUh40bP+NQkXmyYadM7xXTPN6wDruQw+K5Q4LYITtq\\u002f7VneoZzu7TayGBHH6upLFOJR\\u002fi7pNaVwoA62JGHq3KT60vnRkQ1aJVGRHE34lLuMEc9yxRvVzfVvIGAEPcvZlkSq1K9nra0efix1k3gu\\u002fI8rBETMVivLsPACgMOp3JSLPK+3eVkz+5dZFY22liASw3ADChaLNG1b7vijMu1pdqTUY3Kn1OYOvlT4uFwXHv3z4p02svN5RbmqqbOfmauyHRwxcAssR1QRy+uYG73YQ7AHgzOZsmLs8S1W8vST9alV+llH5x58LPeVffSJqyOsLR2fV94vvvT589k4uNAgDAqZPZp05mW0dee23uzFkuTigMGRLw40\\u002f\\u002f+fyzo3l5tQAgk2lkslrsRgACARsbwjnNn88lEKDdDBC4XVlrQlBSFy+Qoyi8uud4g1yJHcDZs2hobGmzeJO96xObLt0M93RfmIQ9ynWtd1b\\u002ffDs1Hxu1Z2\\u002feZyyu\\u002fWszA8foyTF\\u002fHHpeLlXLJGq5VL1zU2pdtU07yo6ERfusf3W2deSfH85qNXoAeOA\\u002fYz08bVrqc9r9HibNSQi9u86zXmv46\\u002fsz5tuPvTCDTCG1bQcAAJFx9r+PeuifH87+u+Wq+ba3Hz8s2ofrxjAaTA110rLCBnPNfvSQgPbF2j1\\u002f8Tx3Vkl+66wyGp0SFuXjE8Cn0SjiFsWdWxUalQ4ArqcW\\u002fvDxkZc+WGzzSFc8O\\u002fTgfTf76r0Dpw+1Xm5xF3DihgayOXSZVJ2bUSWTqFRK7dcfHBA1y1c9McnmYVaKcmuvXSgoyq0lEAjhMT6BoUIyhVRTIcrLrEJRVK8zfvnufr8gj8hY+6+\\u002fl\\u002fBZD1on3DX6OzpDMY1iU8GGodbfxpQku7OWW99tT62zSarSKFEd9inrAJko1EFbUtVgsvMt3xE6pQufkAQi9i8XRV3c6bTbNPos2wCBSRtpGxl8+nffwOHuJZYS9Yv1ZQDAJFOG3W0DCwACOive3SdbXH+sqvWIrqMK9+49HNd7up\\u002fvwPD152MigcEC843aanH7hHt4NDadh1FR1gwAQaHC9suuksnEwBBhYW5tWXEjJuHOYtN3b7m6\\u002fa9LFCpp48\\u002f\\u002fEXrZWZOqrLjBfGPV3K9tR9pIxWpsCNdOyuRYKo2i1xkw8T8\\u002f2C\\u002f0dR8zNwkTd560RfHHB\\u002f+e2XkNOwAAAAsfn0zr+gpXuB5KbTpEJlAmChdgBwAAYKTHtKP1W8417u\\u002fjhHsQmw8AdWo5AFysL\\u002fNlcYe4+0S6CS\\u002fUlcLdFVMxl3DVRsORyrwrDeUl8haJTqM06HUmo3nVUAx3GvOzUfNev350T+mdfWVZIz2DFgbHzQ2MwdRxW4tut\\u002fo2m9y6sQlFbEe6xsmfbG7W1qJVWSIA0KRVAgABgEPp5sSdIlkLAIS7Cdqvr0ImEsO5gjuiugJpEybh7uRr7oYkc8Jd1DA\\u002fKC5LVE8iEBI9fKy7umMkePgkeMx9f9jMwxW5v+ZfL5OL3r15Qm00PB4z6M927gEBAR7f\\u002f\\u002fBQ+u2KS5cKs7OrW1qUKpWWSiXz+ayAAI8hQ\\u002fxHj44ICbUzoQrnJD6TEeUlLGhoxsTFKs2x7ML5idiCCQcQFP3seOqFwjLsAK5jL00fV94iPpuPrQ4GgP87eCbIg58U0JqOxHWKSiP7BXr4BXqY754+lOFk4tU3wH3x6tHWkR2\\u002fpZoT7pPnJIZFdXJWkjI6PGV06zwbpUJryRrPWz6ifQuX3qBR6fZvuw4ARCLx9U8emDjTpmYIQZDCnNrUkzkpo+1UC\\u002fX8xdPolMVrR5cVNsxYODRpRKj1KkoqpXbjO\\u002f9ev1AAAKcPZz60YarA9uSr588OPXjfAeDI7hvmbDuZTNrwxpzZS4ZZpuGaTMjB7dc3fX0KQZDNP50LjfIZNdHmMMbi2oUCAAgMEb7+6TLrvaUot\\u002fb\\u002fnt0qFauMRtP2Xy+8\\u002f+3qtsf0PiZ1KI0SqTMUWSIS9V5vtzetNsGS2Ja3m3+CdaQ9TKaYSg6yvusMEtFmlzAhcgDEyd7lNLKdXXow0hlKrO\\u002fSyMEkp3udD1j9u2\\u002fgcPcSXyaXQiQZEJO5IcwIz0AK0eaa9BS\\u002f8Gxx\\u002ffm61oNJTBP2Hj78HiZRa\\u002fhM7LVYC4VOd6awtFoic2PQx4UGhQlar1u4kMsS7tR208xpdIq5okqrwSZkAYBO7+QYS6PWAQCTaT89xGRSAcC6A6NZQU7NlQv5AGDQmzb\\u002ffuHld+wkB1VKHQAQiAS\\u002fgA5\\u002foXQGdkYkrj02jzll2cgTW7FT5PQ6w0eP\\u002fDr7oXEPvjhH2JWehiiKFmVUntl17fSOazqN\\u002fZIE70DB8udmYqO43lerKeNS+BSi\\u002fb9cEoHMJfNrNDZHk30giMMHgGaNskWrypc0LgtLBICx3iF\\u002fFKQVSZvNDdytK9wtbcSJBEI0zzPJw8+NSmeQKal1peUKOydvcwNjRnoG\\u002flV4c2\\u002fpnWuNFdcaK\\u002f6bfnptRMoz8eOs1xe1YHeci+8hJ3\\u002fyZN\\u002fwHSUZx6rynxsyztykHgD+KrgJACO9ghxcKnBMZdCDVbocw\\u002fxjFQYdJu7ka+6GADbPg87KEtcBQJaoPpInZHbw2qzRSeRlYYmLQ4a8fO3w4crcb7MvrY1MsW43dA\\u002fYf+AFbMiht95a8NZbdr4onbdmzdg1a8Zio12XnBKcjC\\u002fO0WtGhQW2T7gDwMaTF0eE+HtxnZpAINdoX917\\u002fGJRBXYA5xCBABsfmL3q913t3wK90fTM9kN7nlzl4zboMy+4XlVd0WIucAmP9sFk2wGASCTGJATEJARg4i60ct1EbAgAAFhs+hufPPDQrK\\u002fkMjWKoHdulk+dl4jdqP9o1Pq\\u002f76b4n3lr3qwlKdajJBJxydoxBCLh143HAeCnT48OHxdBItlP9rG5jE9\\u002ff9hdYPOnGhnnt\\u002f7V2Z+9uRcAbl8rNRpM7cv2e5U7a3m99CPLXalqn7fb6x3lK1FUL1Mfso50tlwqAIAREVnflah2SlQ7rSNdh5gQOYnIw4btad8lZpAymGyKQqiUe+FCQv\\u002fuGzjcvYRIIPiz3MoVYnO9oHVDGLMpvuHfZl+qV8vNdzEFhT18+L1KrTdM\\u002fuGPJD+fR0YmTwwPwYymVdY8u\\u002fewVNOWUn4wOeG92VPa1xf2hMsSDTotNquuUevN85edr1+wxmLRAUCtxmZwzNRqPQCw2i3aKRIp5y0ZNnJcxIev7zl5KNPXz33lI+Mw25hfDwHgj10bXNVq\\u002fL615vX5Fw\\u002feUiuwVz5QFD32z6VT26\\u002fGj44YNjUuNM7fL8zL3ZNLtiqKMeqNSrlG3CBtrBZXF9UX36nKvV4iaW79FLCLSqe88\\u002fd6vLy9XxhRo9qkRFCEaK8JOAqoyqQwoXbqxHtVIJsPAC1a1eWGcvTu8iBT\\u002fML+KEhLrS8z51It3ygao+HJS\\u002ftEWtUor6Bvxiz0ZLTlmJ5Sy+0m3AFAQGe9mjjppYQJl+rLd5Vmnq4p+iXv2tXGir3T\\u002f0MmYn8VhC5OpXSekz95qn\\u002fEdP\\u002fI0zVFM4\\u002f+Ps0vgkOlZbbUZYvr3aj091NmYLd2mrk0Xmm0fxnMnI5vXz7v5GvunkQPn+uNlXK9tkwhWhk+FDvcMTKR+G7KtMOVuWqjvlDaZO5Og8Pd2x5Ijtt8Nb39wsLNCtV\\u002f\\u002ftzz9Yq5MT6emCFrWoNx182sPy7falbYzJ7BOYlBpfy8ZuGyX3a0KLG\\u002fQJFSvWHrwe2Pr2BQ7VzExeHMSOTWNG5zk0yvM7avc+pHdAZ12LiIc0fvAEBzgww73K\\u002fOHslUKrQAEBgi7KhL+8KVI\\u002fdvvdZUL22ql14+k9f+eobZkjWjMdl2s3FTY78kk4xGk0FvrKkUBTvsmOpyPObSBumnKBjNdw2mBqX2Mps+wXarVnLNaeue5kQC3Y250GrcLtSyLqsLIaiGBDxs1B4ikYUNDU5GRGJ9l0SwqewenPp538Dh7jFBbL4lHTHeB5sdjnf3EdJZ5hXmaCSy0CqPYdbDh9+TLpSUqfWGq+VVEUIPTMK9Vip\\u002favdBpc4mubEzPYtOJr81w36FQfe47HCtpkocGWOTtqiqaDHf8PXvsJDcgZAILwCoLGs2mRBMrYHRiFSVNwNA+9VQA4MFz74+BwBefX\\u002fRx2\\u002fv\\u002ffuXc77+\\u002fInT46y3CQn3AgAEQUuLGsLvdhXEdY+Ht9uL3z708WO\\u002fm9e1wzAaTJkXCzIvFlgiJDKRSqcCihoMJqPVykXOoDGo7215KmxIL9bv4BzwovnVaMpuSS6McJ+CHQO4LU7VmtR+DOyHe28zt5Rp0aquNlQQCYSx3sEAMFwYyKHQLjeUmTuGW5qU3WiuFmlVAPDl6PnW2XYAaNZgkyAYJAJxkm\\u002fYJN+wHHHD6rPbskT1R6ryFgXbPyvrRwSAH8ctWXNu+42mquPVBSYU8WJwHgxPejpurDM96DsSzfcEgGJZS\\u002ftlV40IUiJvAYAoXp82\\u002fUjy8DtXW3KgIgdB0Y4auHfEncYkEYgmFDHZ++DC4e49YZ4eM+Mjj2cXYgcAKkXSZb9snxwdNic+MiHAx5vLJhGJKAoKrVak0mRW110vq75YVC5VY6+sx\\u002fp6Pjlx5HM7DmPiOLu83Tg\\u002frl7w0B97dEbswU9BQ\\u002fNr+0589+B8l5bU4O4pweGebnyWTKKStCj\\u002f77mtz7413y+otb\\u002fKQODpc7eyoYNKqf5iWeh13PQ4Qgd\\u002fYEQicdy0WHN\\u002f\\u002fFtXijtKuI+zPZ20oFDJ7kJOU70UABQyNXa4l5FJQg5jilxzyhKRqPZ0lHCXqvda3+UyZnfa1cSEKAAQbLQPEQjYYo5BCkVtvkMxrVQGo37fN3C4e0wQhw\\u002f1AAA+TG44t7U7twUBYKJv2N6yLADwY7m1\\u002fz7r4cPvSdcqqs035sRi+8V9euaiOdvuxqDPi4tS6fSHcwtNCLLlZsbKlIQQDz5m+25zWcL94pm8KbYHKOdOZAOArz\\u002ff6+5BWJcMGxXGZNGUCm3q6dwps4ZYD50\\u002fma1R6xlMavII7FwJi4nTYpvqp\\u002f3+\\u002fZmNHxwUenFjrWZZevvyIqJ9igvqd22+8vbHD1g9CNcd4+YnP\\u002f35yh9f34EinaeuTEZE064RkDO8gwRv\\u002ffFERGIgdgDXV4Z7TK2pKfu35tdGbVUSb5wXPYBKpBkQfZOuNkNy6XLLMQAY5j4Z+7Be5s3kUIkkiU5TrhAPcffhURkAQCYSx\\u002fuEXqgr4dOYYFXhrjboAYBIIAjpNtn2CoX4jqjOOuJAvLt3itD\\u002fQl1plVKKHRsYPs44e6Op6qsxC5y8HmBZvFSk6\\u002fBEcYJPKJtCk+u1RyrzFtr+2IMVuSqDnkWhjvXu08stSQJfANhSdBs6XjG1Ixfry0woQiYSw7gDKGGBw\\u002fWqF6aNuVxcodDaSYeZEPRMXsmZvBLzXQaVojcaTQ6\\u002f0\\u002f35br+uXcSm0SgkksHU13ObBqkEf+\\u002f\\u002fLZ7xyp5j2AGAM3kl35698sI0F3Rnwt2TSCTiupdmfvnuvwCQmVa2bvF3w8ZEzF6SMmJ8ZB\\u002f3MLGLfLcAf6BdxS7Jaz26c1xiFX63aKwgu\\u002fXkHINKIwcEYfMXFpYJB0ZjP3wY8lnLrRPucs0JBFUSCdjqRSMiUmjOW0f4rAet79qFSRPjug0F278Ne9OFBxd838DhXOu9lBnvOZyP\\u002fvnIeZ+PnIeN3tXDh2N4MzllK9\\u002fCRjtW\\u002fKCj5UP6S0FjMwBwaLQEv7bFVwCgtEV8qqDYPLT3kZVB7jwAGBUc8MbhUyYUPZid\\u002f8KkMdbb94TLEu5XUwu2\\u002fXHxgTVjaDQyiqCnjtw5tOcmACxdZbNCkfNYbNraxyf++s2pbz89SqaQxk2OIRIJCIJeOpv3w8bjALDq0fEstqOL3svWjmmokx7ed+u9V3Z999djPlbNxJ98ccZrG7aknskjkfevfHhcUKgQAOQyTX2t5PqlIhKJuGad\\u002fdIAnF1zH57gFeD+5TP\\u002fSFtcP7OMTCHNf2zSmtfmMznYDkK4vjTSfVqxIitHlnap+eil5qMAQACC9eFjDDdltIejT\\u002fneQCQQ\\u002fNm8GpVMrFXNj4+1xKf4hR+ryr\\u002fWUEEkECyV3eYSbARFtxTffiRquDl4taHirRvHscfBAACwoyRDpFVP9YuI4gmJBAIAGBHkTG3RjaYqAIjje2EfMDDsKs0kEggBLJ4RQdo3vWmPRCCGcT1K5aLf8q4Ncfdm3+0MY0BMluVWOBTa80PG\\u002fS\\u002f97Ds3T1CIpJkB0SQCwYSix6vy37t1EgCejhvbvqVMr0r08CUAlMpFfBoj5O7K7NauNlT8WXhjml9kitA\\u002fhONu\\u002flU0aZSnagq\\u002fuJMKAEtDEtyo+KcK7n4R6M77asXcJ7fsd5xJBwCNHtskECNU6P7Hw0sFbBYAJPh7366sxW6B68DchKjSZtHPF9KwAwC\\u002fpt4IE3p0aQ1b3H1l+vwkOp3y06dHJSIliqA3LxfdvFzEcWNMmZs4d9nwwJBen2SGomhhTm1GWmlFcWNjvUwhU6tVOoPOqNcbDfp+SDQ7Qyppnb\\u002foLsAmoK1ZRqVi+\\u002fMd3fisAduGlMOYRiYJjKYW810E1cjUR9s3Z5eqDlg6zwAAlRzApneeUGhfYM5lzGLTe3pp8J7pzO48IlCtq8GRXmjG0sfwfQOHww1wNVI5AMR4CzFt2XfczjKfC60bnWLOtgPAooTYL89faVaqblbVtG3aY65JuAuEnMUrR\\u002f3+3entf10SCLkKucbcL2\\u002f8lJh5tqvTdMnSVaOaGmT7d6Z99OZeBpPK47OkEpVGrQeAeUtSlq\\u002ft\\u002fAP96VdnNzXK0y4Xvf3C9u\\u002f+fIx9N2M7ZGjQG\\u002f9d8uV\\u002fD547kX3uRDaZTAQCwWhoPVjEFNTjnDFsavxvV9\\u002ff+vmRE1sum5d16jkmhz5txeglG6Z5BeBVqP2PSCCuDX75WsvJS81HRPpGsCrW4FOF4wRzxwpm223v3tsC2bwLdaVguzzIZN8wIoHQrFV5MzmWrHEo12NhcPzBipz\\u002f3j79V8ENAZ1Vo5K1aFXJAr9VEUM\\u002fzThnebhZnUr+Y+6Vr7JSaSSyJ4NtQhGxVq01GQFgXlDsFL8IzPbOO1NTfK2xQmHQKQy6MrkIAGpV0kcv7OJQ6FwqjUOhPRk7mtvdXPDcwJi9ZVnLTm+2RBhkih\\u002fTbYJv6FOxoz3odrphPhM\\u002f9sWrh1Lry4bt+8abydEjJrFW\\u002fXLipMdjRlq2eSx6ZJ1K\\u002flfhzWcu72dRqB40pkinNndvXx2RvD62m9dWu41DoYVyPUrloiQP++XtBhQ5V1tyrrYEAAgADDLViJj0yN3Peb\\u002fw\\u002f0uZbvMAHO5eNy486OPFM\\u002f\\u002fv4Jn2XU2cNyo04KsVc\\u002flMhuUunnDvkmenjClvkZzIKcIOALx74HSQBy\\u002fB36YMB4ezGD89btjYiGN7bx7amdZYJwUAhUxzcPv1QzvSxk6NefylWV6+PMxDXOXK2bw\\u002fvz1dWyXCDgxgCILoda2fdVQ6xXbQBv3u6lBqpZ05QABAc\\u002fjw\\u002fkUAMo+5pEXxmyUiUe2xk3C37SfDZy0DJxbaIRLZBCCh0HZBhU6J8GA\\u002fYrUJzikkItd6idHe6H7ex\\u002fB9A4fDDXDmeb1+bjYtvAwm08GcfACgkckrUxItcSKBkODrfbaotFwkadu6x1yTcI9LDFi2ZnRQiGDP1mvFBfUmoyk82mf2gqFzl6T0sBzgqZdmjhwXcWjPzbysmuZGGYfLGDo8ZO6SlOGjw7Gb2kMkEt7+39KX1\\u002f9dXFD\\u002fwWu7P\\u002flutWXe5cRpsbFD\\u002fA\\u002fsvnH7Wml9ncRkRLx8eN6+vJHjIiZMbauTxTmPw2c99cmKB1+afWbn9YsHbpVmV9tt7N4pT3\\u002f3xHFRI2cmDJ8eT6UN3APc+xABCGMEs8YIZkn0zS26ej2ipRLpHjQvd2p\\u002f1nqb27izKNRkYVvilU9jJnn4prfUYhqXfzF63jCh\\u002f67SzAqFRKxTB7L5j0QNfyxmZImstTLI2rKwBI3JcKu5ukohrVPJCQRwpzFH8b0WhwyZFxTbk4+2C3Ul20syrCNqo8F82cBsVURy9xLuJXJzm3UCCsAiUwEABdAYDSXylhJ5y\\u002fGqgmNz1rUv614YHM8kU\\u002f8qvJkjbqhVyTkUWjTfM9wNe6Hr3ZTpU\\u002fzCNxfdTm+prVPLeVTGGK\\u002fgVRHJE3067O7Vq5IEvqVyUUf9ZIZ6+L41dOrVxopSuUikVWlNBiqRFMjmJXj4LgqOn+Ln1JcIDnePWZAUE+kleH7nkSqxFDvWGTqF\\u002fNSkkevGDzfP+DEbGRrw4\\u002fnrVlvhOkEgwCdLZlaLZbl1jZghndH49LZDe55a5c11VI2Lu58xmNSlD41dsnZM+vXSUwczrp3P1+uMKIpePpOXfq30\\u002fW9XJQxzfXu3XX9c\\u002fOv7M+bbXDfmsLHhoVE+Xr48NpfOYFBpdOqB7ddOHki3fVD\\u002fIxKJNDpFpzUAgF7rqBhIq9GbbzAdTp4esPisB60T7ipdmsFYQyH7WyI6Y6lGn225C0DgMZdZ3e0QAUhkktBgarBEjKbBdNFl4CCTvHTGcstdvbHKanBQwvcNHA43wJlzkRSSTee9CyXlMo0WAKZFhfEYNlkRDxYTAOT22m92W08T7m485qkb\\u002f2e+PWJsxIixnZd8fvfnY9iQQ8kjQh30areYMS9xxry2CxQWdAblx82PY6MAACD04j7+7LTHn52GHcD1AF\\u002fIXfbsjGXPzpA0y\\u002fNvlJVkVdWWNjZWi6TNCoVUpdcZTQYTgUggk0kUOoXFpbPdWHwhxzPAwzvQIyjaN2xIgMC3rfkPbmDiU4V8aq9PXnbSe8NmvDfMTiubvTP+gw0BkAjE1RHJqyOSMfFYvlfZKmyfskA2\\u002f51kpz4fmGRq+4ebRfKE7Yc+GjH7oxGzMUG7uvSTi6TNS079TSaSfp+4fJx3iKWfDApQJG16LHV3nUq+vzz74bvtdKxN94+c7h+JjbYz1juk017tXXrNXbJx1PyNo+Z3dHeUV5D1D+dS6etiRq6zKtLH4XAAEO0jPPzsQ4fu5P95+VZ5i1NFHO4sxvzEmMfGDRNysFNkkgJ86BSy1tD9kvn7EJ1C\\u002fmnNguW\\u002f7GiUKzFDLUrVhq0Htz++gk7p6SE67h5GIBBSRoenjA6Xy9RH99zc+\\u002fcVlVKrVuk+ennXX0dfYLGxl9V7oryo8e8fz5pvL1w16rHnp7evhmEN1K6PfA92Q60EAETNjgqKxXdH+e6D8loXnRLJoCZq9HfuBlCJer8n91nLBjL1YcttAGDTx1LJAdYRB2iUCOukqs7YVh2Ccx6NHKrStV2c1hmKUFTXvivL4ILvGzgcbiBj06gStUaq0VgHd2fkmG8sTYyzjgOAuaLIhLhyOWj8aB7XW\\u002fhC7pi5SWPmJmEHcLguWrR+6qL1U7FRXDt\\u002fFt5QGw0vJYyZ5BtmHScARPE8p\\u002fpFbCm6XauSWw\\u002fhcDjX+mHVAmxo4KGSSQ+kxC9Njs+ta7xZUXO7srZaLJNptFK11oggdAqZSaV4cdn+fLdob2FKkN\\u002fQQB9SBwtCUEikjP9rS+v0BJNKyf\\u002fvi9hov+IzGb30kjw57Auv2i8HweGcx3Vjrlw3ceLMIc+v+VUh08hl6qvnC6bPT8Ju1wMXTmShCAoA4TE+T746m2DbCNVMKbc5mx04oob4mxPuxfl1YzuewVySX2++ERlvf87cwOfOerC2LeEOUmzC\\u002fYjlNgC0bzjjAIOaqNRestxV6zNR1EAgYC+64ByjU+PAaoEAFIxqfSaLNriLQvB9o49dK6la98c+TDBEyD\\u002fy0sOY4ODy7JZD5\\u002fK6drWGQ6ddf28DNtpX9EbTL+fSjmUV1kvlDAolOdjvuRljon0GSjEiziLYnSdRa\\u002fIami2RCrHkUmkFAPjxuGNDgyxxM3NtO5XsyiS5K38WDofD4fpRg1oBADya\\u002fUKzCoUYADwZg7J6C4fDuRyBAPF+XvF+Xo+MTcGO4XC4DlDudqcEAI1ax2C2tv\\u002fuR74B7pNmJRzelQYADTVi7LCVbrx4c8IaAOKGBtnNtgNAboZT\\u002fTG68ew9NGJcZOqJbAC4dDr3P09Ptfv6EQS5fDbPfDt5lE29wiDixlxUL\\u002f0AQbXmuzpDkdaQR6fEAoDOWKo1FFi2JBE5XIZTkyzNOPRJzfIfLHdRVKfUXeHQJ7VtgXMCmzYGE5Go9g72hDu+b+DuT6\\u002fvOn4qp9h8W2HSpRaU3Sir\\u002fve5NYEePJvtcP1teKB\\u002fRk19jVR2LK9wTmyUzmh87\\u002fg5BEUBYO2wpPYHBFUSKQDwmfZzKd1jv14Jh8PhcINONM8TAP4pvGXOrVs0qBXv3zp1qb6cRiLPDoy2HsI5T9qiKMutbf+fpBmfNIDD4XD3CxqdYlljs7wI24i\\u002fv6hVrZlWDrd1QWO7uvHiLQ1kpGKrAl0rZw5nOrmYajeevYcmzox3F3AAoLZSdHTPTewwAAAc3JHWVC8FAL6APWFGPHZ4kCAROVzGLOuITH0Uc8PMjbmQSOhCNoFJG04meVpHRIo\\u002fre\\u002finEGjRNDINi0ZZeqDJsSprm4DFr5v4O5D5c1iS7bdQqM3bL4y4BYywS1OiDV3iXl5\\u002f\\u002fElf2yf8sOf18qrAEDAYj6YnIDZWGc0FjeLAMCf54YZ6gm8wh2Hw+HuEetjR52sKSyVi6Yc\\u002fiWY4+5OY6KANmoUdSo5ADDIlI2j5vnbriKLc96fHx86vTsNGwVY\\u002fdLsNS91oVgMh8PhcINaXFLg7WslAPDnd6fDY3zc+Ni1DVxux6bUhhrJhJnxQ5KDqTSb0zcUQc8cybxwPNt8N3lMJ0uCd\\u002fXFR8b5nj6UAQBpqYWVpU1BYW35NRRBTxxI\\u002f+lTm3yuY1199h4iU0hPvT7nf6\\u002fuAoCfPzuGoui8ZSMIxNayNgRBDu1I+\\u002f3Lk+a7j780i2xVgz\\u002fo8FkrpOoDlrsyzVEvt1cBQK45bgkCAJ\\u002f1oPXdThGA5M5a1ST\\u002fxhJRaM8rtRfZ9AltG+GcwGetaJB9armLoOpayduBHj9ZbTLI4PsG7j6UWdXaggwjq6ptPQPcABEmcP\\u002fPiOS\\u002f0m6bUDSnvu0y\\u002f7uzJjOp2OZXN6tqdUYjAMT7eGGGegJPuONwONw9gk9jHp716Oai22dri0vlomqlhEQg8miMUV5BY7yCl4cl4v1keiLjUiE2hMPhcDinSURKhUyjUmpVCq1KqVMptVqtwTx0+Wx+XbWYyaKxOHQmi8Zk04XeXDLZJvupkGmkYpVKqVUptWqlTqXQippaJxhlXC9FEJTJprHYdPMPEXhy2q\\u002ft6SoLV40yZ41LC+ofW\\u002fBtfEqwu4Cj1xmUck1Lk3z+ipEzF2HXZu8hjUp\\u002f8kD6yQPpVBo5KMzTN8CdxWGgCCIRq4pyasUtrWt+znlgeGBIJ21ku\\u002friJ89J3PLTeblMrdXon1n5y\\u002fhpsQEhQiKJ2FgnvX21uLFOymDRVq6bsPmnc9aP6khXnx16\\u002fL6Pnx734LoJOzddNJmQHz85uv331PihQRwuQyHX5KRXSkStCxcvXj16yhxsvdvgwqaPo5D9DcYa812doURvrCQASaNvvRgDADRKJJOaZLnrJAHncZHyTxNimc+HVoufD\\u002fc6QiEN1pb3\\u002fcKdvbZZ8aMJaf1rBQCZ+lALNUnAecJqq0EG3zdwPRfkwQvw4EmUaqVOjx0beBRaHTYEAB3Hcf3rtWnjqSTiPzcytEYjALgzGa9PmzA7JhK7HcCxvNYz\\u002fUnhNrOReghPuOMGh7PVpY+d3QsAMe6exxc8gh2+1x2rKNxw4QAAjPQO2DVrFXYYh7uLTaFtiBuzIQ7bKRLXQ9XFjS31UmwUh8PhcE774IXtBdmt2UCMvf9cxkS+\\u002fOuxuKE261n9\\u002fNnRc8eyrCMWpw9nnj6caR15+cPF0xcMtY640IjxkavXT9r26wUAUCq01y+0NcgGALXK9Wfdlqp2vc5YnFdXnFdnOw5EInHhypGPvzwTE2+vqy+ezaH\\u002f39cr339hu1KuMeiNmLdA4MV96\\u002fPlIeFeW345b15b1bGuPju44n1\\u002f+JlpAk\\u002fuH9+e1qh0khblpdO51qN0BvWhDVOWrL0HDpwIfOayJvnXlvsKLfYqiHtXlku1IBHdvN3erpW8bokYTc0ljfOCBJuYVGeXANEaciWq3WzaBA5jKnbs\\u002fkAiunlyX6yXfmgdrJd+aDA1eLm9SiQ4agZloTdWmhApg5qIHegn+L6B67lX5kx4Zc4EADCaEIlaI1Hd\\u002fU+tkao0pU3i41kDqOaJS6dhQwAAwHNp42+cq5AIhJenjHty3IiyFgmZRIwUepCI9tuqB\\u002fDcnhgznEYmjwoOwI71AJ5wx+FwOByuExmXB9ChHg6Hw+H619qnpiSPDj+652b+nSpRswJBUBab7iHkhER6xSS48lTNbM2Tk4ekBN+4VFSUW9tYK5HLNHqdkUQmsjl0\\u002f2BBfHLQ1LmJ\\u002fsEC7MM60NUXH58c9Nu\\u002fzxzYdv3mlaL6aonRaOK4MfwCPUZOiJq3bDiDRQOA4HBPJ9uyd\\u002fXZXWLe8hFjp8ae+Pd22qWiuiqRWqljc+m+AR7Dx0XMWpzCF9wj8\\u002f\\u002f4rOVN8m8AWq98qHRpCNJawg8ABCDzmEssd7vEnb1aob1g3Z3GaGoubVzEZcz0YD\\u002fCog0jENonoVCDsVZjyFZqryi1l3TGUgAYOJnijqEIqjIhSr2xEjOgN1aRiO4kAptIZAO0X2+vcwLOOrnmlEp33TrYovhNqj4g5DzJYUyjkUOth8xMiEStz1Tpriu1FzX6bCF3Qzd+jSiYEESFIHKDqcl2BNEZS0kEDpHIJhKYtkNOuZ\\u002f2DVzvIpOIQg5LyLFpNXanqn5AJdwTA32xIQAAGBkWiA3hBgwWlTrEt5NGMU+N65VVrAko2nkxAg7X7\\u002fAKd7zCHYfrR+8\\u002f8lva6RxsFADwHu44HG4Ae2f1z7dT87FRe\\u002fbmfcZyuN4mDocb+Mqalqt0V823yUQPBNUgqNp8l8uYGST4o23TLkJQbUXzKpXuBnYAgECg0imRJKI7ichBUC2CqEyIVG+sQNDWtXwtAjy+c5D0l2tOVLass47EB1QQnC4QRMGYUx1sHQkSbMKsJWtNb6xolH1hQuQIqjQhchOiQFCFCVECINhNsQhEIptEYBOJHBKBQyKyiUQOj7mYy+h8iokJkZQ2LdYZSrADAABAIXlTSL4kkjsRqAiqMiEynbESs7aqkLvB2+0t6wiGXHNaqt6HIAoTojChCvMNBLW\\u002f6LE1ApCIRMs\\u002fikMksElErif3eRolAruprT7YN3AAcK2kat0f+zDBECH\\u002fyEsPY4L3kjtV9at+3okJcui06+9twAT7zEvbj57MLrKO+PK4u59ZxWfhB1E4LGe\\u002fwHA4HA6Huz+ZjEjWNex69DgcDofD4XADijv7QUvC3YiIrIf4rOXWd7uKSKAHC7dViZ5SaM5ghlBUr9HbL0oYyAymJutlZrsCRRAFAgowtS2fSCNHOpNwJxH5ocK9FS0PafR2GiUZTA0GU0+XXtQa8mTqI9ioE1AwmRCZCZG1rq0BAAB81opOE+733r6Bwznw6fJZwQL+0TsFDTIFl06bFBP6\\u002fIyxeLYdZxeecMfhcDgczpGC9AqN0k5jWRwOh8PhcLiBg8uYQyK+bb0ypxmZJOh5g2wigREs+KtF8Xuj7AtL4XxXEIgEm2YR9ycySRDmebBB9plIsQkFI3Z4cML3Ddz9g0omPTdjzHMz7oGVP3C9zn7DeBwOh8PhcGZ4A3ccDofD4XADH5FAd2MuwEYBeMylzvdmcYgg4DwR6XNJwHmcRORgBztAJQd7cp+L8rniTA34\\u002fYBAoPjw3onwOctnrSQSnF1rkUTkuLNX85nLsAMDBb5v4HA4nA2XfO\\u002ficH2nOyvU4HA4XA9kXCzAhnA4HA6Hw+EGHj\\u002f+Z378z7BRl6KQvHx473m7vanQpqp01zT6bL2p2mQSIaiOSKASCSwSkUslB1EpoQxKLJs2jkL2x\\u002f4Ie7iMWUMCarBRpxGA3KWHs2gjurS9y9HIYf7uG335Hyi1F1W6NI0+12CqNprEKKoFAoFIYBAJLArJj0oOpFNiWLQRDGoSgdB59saT+7wn93lstK\\u002f00r6Bw+FwPWEwma6WV92sqq2TyWVarRFBp0eGrRmehN3O1Tr\\u002fyMbhBhQSAZ+WgcPh+o5aoS3MrMRGcTgcDofD4e5jBAKVy5jOZUzHDuC6gkhgchmzHCzuOhjh+wYOhxs4dqZn\\u002fXAprUmhtA6GevCt7755+JRIpfbisv87Z5p1vIfwhDtukKEQSQCgNRm3F2YerSgol0uUBp0HnZXi6bc8fMgEvxDsA6x8f+fqlxmXAODmimeEDBYAFEiatxdmXmuoqlcpUECFDFY0XzjRL3R5RAKJYL+YXmcy7ivNOVNVki9pFmlVNBLZl8Ud6xO0IiIhii\\u002fEbm2rSiE9VVWc1lBVIGkWadV6xMQiU33Z3AQP7wWhsWN9grAPcJpUp115cke+uAkA4ty9ts18kEdzdn4iDodzIOtascmIYKM4HA6Hw+FwOBwOh8PhBiQTir5y4PjR3M7bw7Jp1H13cgFg7bCkSE8Bdri78IQ7bpBhUijlcvG6s\\u002f+WykSWYL1KfqRcfqQ8f2Fo7Jfj5pKJnVTBN6qVQgbrm8wr3925gqCoJa4y6CvkkhxR48rIRKvN21ytr3z1yrFapdwS0ZtMhfrmQknz3\\u002fm3H4kd9tawyXYz9TVK2ZtXT1yqq8DEZXqtTKzNFzftKs6aFhD+w6SFdFKX\\u002fyqlOu3qkzvN2fZEgc\\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\\u002fzbz83Z2rAHCkPH9ecPSsoEjrR2HsKc4+UVnozeS8PXzy7KAoS0W80qA7XVUSwbNzRatGKXvx0lFztn111NA3h020zmvvL8194+oJncl4uDx\\u002ftHfgqqgky5AZg0x5LG54nrhxWfiQsT7BVBLJMqTQ6166dOR0dQkA7CvJeSlpvA\\u002fL2YXdzdl2cyp\\u002fhJf\\u002fX9OWWX4nvUqib27R1esRHY1E96B686md9NLB4QaplnppTWkTNorD4XA4HA6Hw+FwOBxuQNqTkW3uZvHR3GkLh8Rgh20xKGRfN26NVFYulmDHemAQJNxrJDLLbROCNMgUeML9PjfCK+CTMbOs+7aQCISXho6vUkgPlOUBwM\\u002fZ1x0n3I9XFnozOYfmP+TJYFvH2RTa4rA464jF5+kXlQYdAMwNjv7f6BmY0cVhcRKd5sMbZwHg2ztXlkUMMfeat\\u002fZMwmhMxIxDpX07ccHYPT9LdBoERa81VC3p4DWA7ZqxCr1u7aldWS0NADDaO\\u002fCPaQ8wyZS2TXsBCuj1llOpzYfF+tZqfTMPqtcE4fxRghkEsNNOB4cbvPDydhwOh8PhcDgczkkmBC1tEhXUN+fXNRXVt4hVarlGJ9dotQYjhUSiUcg8Jl3AYQV68MK9PBIDvBMCfMikTvrBupBYqb5aUnWzrKa8WVwjlil1eq3BwKRS3Zh0NyY90J2XEuyXEuIX4SWw1ybWNaRq7aXC8rTS6pImUa1ErtLqTSjColEFbFaIkD80yHd8VHCYpwfmUb30Wxrg7xfOLoVWd7u8trChuaihpaJZotTqVXq9UqsHABaNwqRSWTSKD48bLOAHCnjhnh4Jgd40sutzv6VNom1XM6+XVjVIlSQiwcuNnRLst3T4kIQAb+ymd5U2iXZcv3O9pLpeKicRiZ5cVojQfeaQyKmxYQyqi3NZ18qrACDInddptt3Mk82qkcpalGrsQA+4\\u002fpfucgHuPMttGpnsz+e2jeHuS4\\u002fFDbP79fdc4lhzwv1OS325XBzCdcduYeX\\u002fRkzFZNsdaNaojlW05t1eS5loO9hqZWTiF+kX1UZDo1p5ua5icgd9aexikimT\\u002fEP3l+YCQL2qrUF8e5YCdqVBt\\u002fb07jst9QAw3jf496lLu9H8vUsQFNla+VWOLM0SIQABBRQARPrG\\u002fbWbSpQ5q4NeJFpdEsDhBju8gTsOh8PhcDgcrl\\u002f8377T+27lYIJjIoJ+f3QJJtgT8776p7xZjAk+Mj7llTkTMEEHtAbjleLKs7klqQVlUrUWOwwAADqjUWc0yjXaKpE0vaLWHOTQaVNiw1aNTor397LdvAse\\u002fm3PzfIa60jqW08IOCzrSFZ1w58Xb53LKzUhiHUcABRanUKrqxHLcmsaj2cVAkCAB++xCcMWJsdSydgqup4ob5b8cu76qZxivdGEGZKptTK1trRJdCa3ZOOxi4mBPo9OGDYtLtyyAYvmyons\\u002fft+DWQHbue+vfcUNtqZvx9fNjzUHxt1NQRFT2UXH7tTcKmoov0uZCZVm8xvaHGj6GJhuTlIJZMSA31GhQVOjQuP8MJey+nIrrSsDw+ctdydkxi18cG27sc\\u002fn73+87nrJqRtQcTyZkl5s2TvzZy5SdHvL57GtE2gmxDkm5NX\\u002fr5023oNxfJmfXmz5FxeqQ+P8\\u002fGymSNCA6we0VP1ciUAJPp2mP3HoFPIAKA3GrEDPdC7GTqXGB0WuHJk4pE7BSwa9ZWZ47kMfDXI+91o7yBsCAAAQt3cgzi8SoUUADKa6x0k3N2odMcl8BhX6yuNCAIA0XxhEIeHHQaA1s7sXuaONzcaa7qUcAcAX1brlSSlQW87YoNNoQKAyqB\\u002f6NSezOY6AJjkF\\u002frrlMW0Xs62A0Ca6HSOLI1III0TzE7ij\\u002fei+VGIND2ibdTWZEguXRWdzJZdTxOdHi2YiX0kDjc4oSiaebkIG8XhcDgcDofD4Xrf4mFx7RPu10uqGuVKL66zpWOO5dQ0ts+2A8CilA6nXGMU1jdvvZpx7E6h1tCdRJVCqzuYnncwPW9KbNg7C6Z4ubnm31VQ3zzubsJdpdN\\u002fcvjCgfRcq0RfJ6pF0vf3n\\u002fnp7PXPH5w9PMQFiVQTgnx\\u002f+tpfl24ZTdh0v113quqf33p4RGjAx8tm+vA4AMBjuiYPNjDfL1ynrhZXbjx2saihBTvgBL3RdLOs5mZZzfenr8b6ei5MiV2YHMuhd23lv\\u002fy6Zsvtz46kbr6SbjVo42hmQaNM8dujSyxl9SYEfWn70TO5JbYbtqmXKh7btO+7tfMnx3QtjeaAzmgEADrF2cJ5jcEAAOwu\\u002flocGxy1qO\\u002fOn5L2zoZzr66bkxCFHcPdZ9zpTC61w7+ByLvt14uljj6Jhgi8iV2ZJGYuJAeAKL6jZuWezNYv9VqHVep2Ue72kXeMTaEaEeTJ8\\u002fvTm2sBYHpA+O9Tl\\u002fRBth0AbojPAcBS\\u002fyfm+f7HnxFKIdIAgEqkBzDDF\\u002fg9stT\\u002fCcs2ONy9oTy\\u002fTtqiwEZxOBwOh8PhcLjeNzTIN1jAxwQRFD2SkY8JdtuhjDxsCGBIgHe4E2WwN8tq\\u002fvPb7iXfbf33Vm73srfWzuWVLvlui6WSuocK7iYHaySylT\\u002ft3H+7C9l2iya58rFN+7ZdzcQOdJFMrf3Pb3t+v3DDyWy7xY2y6ge+33qzrAYA+Mye9lUeyO8XzgG90fTuvlOP\\u002f\\u002flv97LtGHl1TZ8euSDrYFqDA5UtEvNuczSzwEG23exWee03J65Y7n5z8rKDbLsZgqKv7zpR0eKyFuoeLCYANCqU2IEOVIilAODFtpkZ00N9kafD4VzIjero0q47nWm+IdM7+gSxbOakJo3KfONgWd7BMjsHJRhSnQYbAgAAFOBOc93l+soCSXOtUibVaZUGnc5k1JlMepNT33lsCu2Nqycu1VWY725IGN2+WXwvadLV0kmMFP4k7AAAAAxzn3ykbnOTzmYqHw43qGVcxPvJ4HA4HA6Hw+H6zcLk2G9PteWtzA5l5D82cTgm2A0mBDl+x87h7qLkWGzIHolac6vclflWqVq7\\u002fq\\u002f9W55cEe3jqMrNGQX1zWDOmP++z3pRwK4yIcjHh8+zaBTnS\\u002f4xpGrtI7\\u002fv6XaqVKrWPvn3\\u002fh8eWjg6PJBOIfckUT6Q3y9cRwwm0zNbDl4pqsQO9MDo8CB\\u002fdzdstDMIihY1tIQI+Z8euYAds2fr1YzlIxNChPyMyrq\\u002fLt3CDtuj0um\\u002fOn7pu7ULsAPdEuUpqJcrblXVKnV6dmcdmdKr6yRqDQAMC3TBjBYLp4pqcbiBg0ZylF+m3520onbYmMXJcnILhV6HDTnUvjEcAJyoLJry72+Ljm75Iv3ikfL8jOa6crm4WaOS63U6k9HJK+77S3P3lmRb7j51\\u002fkCTxtlLdj1EJpCZJG5HLdoJQGCQ2CSCo3cHhxtc0i\\u002fhK6bicDgcDofD4frNwuTY9jOzSxpFubWNmGA3XCqsEKuwhWI0MnluUjQmaNe0uPBADx42ag+ZROQx6c4s26jWG17cdsTcC6InCuqbTAjy\\u002fNbDDrLtZBLRjUknEbG\\u002f3vb+e\\u002fBcaZMIG3WC0YS8sPVwp9l2JpXixqS3e59baQ3G57ceLm0SCXpWezuQ36+Bg89iBAl4bkx6+7+7fvHpkdROs+1EAoFJpfBZDGfeMgBYMTIBG3JOYX3z5svp1h8aNDK5oydFUPTvS7cB4OND5zHzS9g0ake\\u002f3vP5ZXXSLreLsGtqZBgAqPT6jecuYcdsGRHks7MXzbdnRIfbDvaI\\u002fV8NDjdg6RxWgmvuftYz7y4u6hJMcmvjpwSBd5LA13bQjjA37BS8n7Kuf56ear7NpzEm+oXEunv5sbluVDqLQmWQKX\\u002fm3dpdnGX7IDsa1AoigbA2emhaQ3WBpLlBrXj87L+7Z6\\u002fqg64yfozQSnWhCTWSCHaey4Do5QZxEAtv+jS4mYxIQ1VLdUljdUljY7VY3CwXN8pkIqVWrddp9Qat0YQgFCqZQiFTaGQag8Lls9082G7uLIEPzzdE6Bfi6R\\u002fm6ebBxv7cQcigN+beKMNGcR0wGk0lWdVVxQ31FS11Fc31lSK1QqNV6zVqnVatJ5NJdCaVxqDSWVShL987wMM70MMv1DMmJZgvbF2+AidpVlQU1FUW1VcW1NeUNakVWrVSq1Zq1QotADBYNAaLxmDRWW4M7wAP32CBb4jQN1gYFOVDZ7ry+w7Xl1AELcmpLs2trSlprCltaqwWqVU6jVKrUeoIRAKDTWOy6Uw23TvQIyDcKyDCOyzePzjKB\\u002ftTHCKS7J\\u002fPDGT4N5GZXKK6eTY343JRaXa1TKxUSNR0Fo3LZ\\u002fGEnJjk4PiRYYljIhnsDrssOkMmUqZfLMi8XFSSXS0TKWViJZFEZLLpPAHHP8wzNNYveWJ0ZEIgwYlsVH\\u002fB95aO6DT6isL68rzaquJGUYNU1CgTN8q1ap1Oa9BrDQiC0ugUGoPK4tCFfnzvAPOXckhEYgCV5my720FtEB23eLmxR0cEts+4HUrPj\\u002fPr6aqVB9PtTN2eGhfmZHNnIoHwn3HJ\\u002fz2I7SlKJZOGhfgP8feO8RVG+QgFHJZl+UStwVjSKEqvrN1\\u002fK7ejNHSVSLrlcsa6ST0q4a9skb6z91RWdQMmnhTkOzk6dFxUsI8bx41JBwAUBYlak1FZd7Gg7GB6vsFkZy1KrcH4\\u002fv6zW9Yvxw505vszVzGruVrQyOR5SdGzEiLj\\u002fb3M6xSaEFSiUpc0iS7kl53JLamXtnW2VOn0j23ap9I5qins1EB+vwaOidGhE6NDAcCEoDKNVqrSSNQaqVorUWnMt\\u002ffeyFb27I1wXmZV\\u002fa60O9goAAAECXizhkQND\\u002fUPFvC93NiW\\u002fLXeaBKr1CWNoqKGlvy6puul1WKl2vqBQg5rckyodcR5p3KKC+qaAIBAgNWjh64anWS+hJNRWfvfg+fa7yGncoonRofk1TWZ7\\u002frxuU9OGTU9PpxDp+mNpmN3Cv536Lxab7B+CIKiZ3JKHhqXbB3snkUJMb9cuVErk++4nSVVa1+eMjaQz8NuBFAlkb577GxGTT0AJPn5jAkJxG7RA3YSZzhXGbgHp4OZWIu9CG+t5W7vFx7NUeeZrvJgtLagSRT4fDhquu1g5wokzV9ktF4xeyQm5fVhk+jt8uMOGtNbEzJYv0xenOLpV6eSLziyuUWjutNS\\u002f9qV499OmI\\u002fd1NUmeS78o+x\\u002fV1tOjhfOxY4BpDYfMqKGicJefxk4lxM1ynLTSvPTKwrSK0pzagx6R9e0AECvNei1BlAAADRW21lkydPfPTo5OCYlOGViTEB4T88E+p5KoSnPq7t9IV+n6aNjqcGrJLv65rm87OslebfKHfy69CZErzOARAUA1cU2BVnegR5xw0NHTIsfOS2OxrgfE8fFWVVXj2ddPZlVVYQ9J7SmkKoV0tbD5bybbZeCSGRiaKx\\u002f3IjQuOGhcSPC+EKO7bZ61wABAABJREFUZQg3YOk0+ktHMm+ey828XCSXtB63tGcQG+ViFQCU5bVNAPfwckuZFDN8SuyoGUPIlM5nldGZTh1d9Dv8m8haS710z09nTu68jvlcVcrUSpm6rqI572bZvl\\u002fPMdi0mQ+OXrxukqe\\u002fu\\u002fVmzqiraN7789kze25gf9UGk15rkLYoKgrqLh\\u002fN3LzxqMCHN3v1mHn\\u002fGc\\u002fl96iy0oXwvaUjiAnJvVl2+0J++sWCkpwaFHE0gVaj0mlUOmmLora8bR08MoWUMDpi4oLksXMSWdyeNowegAbpccvilLj2CfdjdwpemzuB1MV529YUWt2FAjvFJYu70jtlUUrcj2eumSteWTTq9PiIyTGhYyOCGHczthh0Cjne3yve3+uhscmnc4rf3XdaobUzlXzr1YyHx6eQSd3\\u002f1yEoesi2032El8fbC6YMD8X2iyAQwJ3FmBobNjU2bMPU0W\\u002ftOXm9tAqzDQCkV9Tm1DTG+3fhIyKvrumvi7ewUQAAGBEa8L9lM3x5NtdvSESCgMMScFijwgJfnj3+z4u3fjt\\u002fw9JDplnR4QGD8wbs+zUAkYgEdxbDnYX9JDybV9pnCfffL9xov\\u002fYAk0p5e8HkBfbmvgAAlUzyduN4u3HGRQYDAIpCYUPzlaKK41lF+XVNALB0eHy3PzeuFrd+EH2ybNb8oTGWeHKw3z9PLFv63TZMcbpco31t13Hz7UhvwabHlnqwWxNrVDJpUUqcB5v15N\\u002f72x4AAADXSqpcknCnkcmfzJ\\u002fx2I79BpPpeH7R8fyiSE9BlKfAPHqntuGDE+fyG5rv1NabUBQAODTaZwtm2vyIHsNm\\u002fRwzmpBh\\u002f\\u002f1BbzQBwH\\u002fGJL8+ZyJ2C4Cntx48f\\u002feDOzHAZ8f6B23HAQB+S73xzekrAEAlk269+4z1X6YJQYb837dtm9ry4rLPv\\u002fY4NtqBXy6kfXfmKgCEe3oceu4hc1Cq1p7MKTqTV1ItkTUrVCiKujHooUL3YcH+S1LiurTYd0mT6GhW4ZXiinqZQq7RubMYfny3ydGh8xKjzT+HQu78RAjXVTK9VqRVe3TQhL1Q2nrIGMUT2o70SJLAZytkAECeuPXqXJccLMtDUBQA4j28\\u002fm\\u002fkNDufi501nbcIdXNP8fQDAF8W97cpix88sUNvMh0sy4vkCZ5OGI3d2qUiOYmL\\u002fR8\\u002fULupVlMW7zbCnepFJlD0iLZZV58lu5YruzHDe4UXPaBZV4eCzdeCJ83P+q5jJdnVz87eiI3aIhAI+wo+Z7BcmUR4dem3OWml2GgHdmV9wnV35WnnGyt+uHOlCBu19e3RVyITXXa51WREcm6U3jqfd+t8fkVBHXa4Z5pqxE014ouH0gEgINxrzKyE6StG+YW48k\\u002fShVAUbawWl+XVlubWlOfVluXV2j0bd2DbV8e3fdV6JNFLJixIfvOnh7HR\\u002fiNqkJ379+aZvTcc54id0VAlaqgSnd13k86kjpweP2PFqOQJTs1i7ksmIzIv+AVs1NZXB1+MSQnBRjum0+iPbrlyYNOF5roeLQ1kMiLFWVXFWVUHNl0gEAjRycFj5ySOnZ3oHYidaIUbCKqLG49svnR2702VwlH1gAOiRtmpXddP7brOE3BmrRo9Z81YoS8fu5EVJseV9QeudQ9\\u002fE+l1hoVhL2Ojd42bm\\u002fT2r49io3ed33\\u002frx7f2OLOHaJS6A5suHN18+ZE35y9aN4lg78S7PRRB9\\u002fx8ZsvGY0ajnSrO9lrqpVu+OHZg04WH35g\\u002fZ81Y7HBfuYf3FpeoKW06tev62b03xE02yY6uMhpM6RcL0i8WfP3KduyYK3x96KXo5GBstPcN9uOWqbHhXAZdrrE5WxSrNJcKKyZ1t1IVAE5kFZkTO9a83NijwrtwxkGnkFeNTjqbV7p8xJB5Q2MsldHOmB4fEektXPnzjvbrNzYrVDfKqsdEBGHi3TYnMerT5bM6zTN6ubF\\u002ffWTxyzuO2l3gccuV9M9WzMZGO\\u002fblsUsme9e9nHkxFBJp\\u002feSR46NCHvltjwvTu4Pl\\u002fcIBgFKnv1xUgQkSCYTvH1owKszZP1ICAaJ9hNE+wscmDi9rEh\\u002fJLHhg+BDsRl00JzHKOttuxmXQX5kz\\u002fqXtRzFxjd4AACQi8bMVsy3ZdovxUcHJwX6YpXezquut7\\u002fbEqOCAb5fOfe3gCfMfUVFTS1FTaxl+Tn1jTn3b9VR3JuOHB+aHeDg6qO6GriXcySRipJcgp7YRAAoa2i6GW8uoajsGyqtr0htN1HZ5Z8tjo72FfXAdrFIkNSEIiUg8cqfgv4fPYS7KaQ3KRrnyWmnVLxfSHp8wfMOUUXavFFnT6A1fnbq8I+2OOYtq1ihXNsqV6ZW1P5679vz0sWtHD3VjDNyTnEHtYm354jA7F96LpC21ytajzKFCX9vBHhnnG0wiEEwomtlcV6mQBnF42C0cqlbKzDeGe\\u002fp3tG\\u002fdarQ\\u002f18yBZKHfZ2Nmv3jpCAB8kX4xgieYERiB3ch1vit6vUlXi6BIuuRiuuQidhjgVMOuUw27sFGAzxP3YEMdC47xpVDJjouVUBStKKjrUm7LMaPRVHTHTiFDR\\u002fLTy0dOi8dGe6DcqnrRLjKZFBLjgl0aRdDstJLUg+mXj2WaCyd7W3VJ464fTu\\u002f+8UzKpJjFj0\\u002fqg3OSTmnV+srC+rK8WvN\\u002f5fm1GqWdSg2cXQ1Voh3fnjy774bJiGDHekar1qceTE89mB4W7798w7Rxc5OIvX944ELNddKYFGzQLo1Sd+jvi\\u002ft\\u002fPy8TKbFjPYOiaP7t8vzb5Zv+eyB8SMDGfc\\u002fj3WYGDlGjbPPnR0\\u002fvSXNcc+o8aYti53en9vx0Zt5D49e+MqejWlS2m\\u002f14P8K\\u002fiUpz7B\\u002fyoSj687t7D\\u002f\\u002fdSadRDIPe+NsH+29dyH\\u002fnt8c6rUVQytQfPrYp+7qdRJJjCqn6+zd2ZV4ueumr1X35wYLvLZ2qKKjb9tWJK8fvoO3LIHH3ynELlUyakxi18zq2s8TB9LyeJNwxBeBmdlvGO\\u002fbE5JFPTR2FjTonSMD7ZNnMDf8cxA4AXCmudFUCd0Z8xKfLZzvTqx0AyCTiRw\\u002fMyK1ttO7oYpZaUI6gqJO\\u002fn4zKOruV8nH+Xp8s6yTbbhHr6\\u002fnTw4se27TPbqOb7hn47xfOLLem0WjCfnBNjA51PtuOEerp\\u002ftyMMdho123oYP+ZGhvuzmK0XxYCABanxEZ6t5aWY0yPD8ck3KVqrVipdm+Xne+eaZFhhx9f++X5y8fzi+0utUgiEObGRb08ZZwP1\\u002fUThbuWcAeAeD9vc8K90F7CvaxZLLW63mUwmfLrmxIDfKw2AQAwz2UAgCH+3rYjQCAQxoQHSVo7JWl6sgqzhcFkqhbLTuUWm8vqO2IwmX46f71Jofxw0XTsmBWN3rB+8\\u002f5btvuENa3B+Nmx1LIm8ZSYMOwYzhU25d5cEBpLavdV9\\u002f2dq+YbyUK\\u002fwC7mxB3zZnLmh8QcKMszoehbV0\\u002f8M3052bnvSDNLA5kWrU3\\u002fLIt9pTnl8u4UOS4OiyuWtfyUdR0FeOHi4X1z1sS4e2I3cpEajZ0phy5nzix3mv4uz3dlwr00u0avNWCjHStIr3Bhwr25TuKgpYBZSKwvhdrlj2trTbWSkzuundx5TdTQevmnL6Eoeut83q3zeSkTY554b3FgJPaTv1e11EtLc2vK8+rMGfa6imZXJbzuK+Im+eaNR8\\u002fsSXP5KStGaU7NJxv+Do72febj5XEjun8a2cecLFRPO5Pzw5u7W+ql2AFX0+sMfZkUwzlgLije8e1JrdplFWoWJiNy8M\\u002fU8wduPfz6\\u002fNmr7ZxEDZw2IHB\\u002ffxNZa6gSqRVazOQDFEW\\u002fe33Xie2th7JdlZ5a8P7Dv\\u002f13y5NUeoflinKx6s2VP5TldngK06lLRzIkzfL\\u002fbdvg4FlcBd9bOqWQqn\\u002f74N+ze2\\u002fiqXa77rHjlsUpce0T7hcKyhRanZP91jFqJLKMSjufBouS7ZS1OeZkIrsjE6NDhwR4Z7drtp5b01Z82hNCDuujB2Z06UVy6LR1E4e373Wu0OpKm8QRXk5NItx+LRMbAiARCR8tndGlktOUYL9HJqT8dv4GdqC7uvSraK+33y+cRa2ktZbU2ogwbE+kPhYi5IcI3bFRAAAgk4gjwgJOZNmZuL8gORYbuive3loU1WKZqxLuAODH4361eM6b01XXK6qz6hqalWqlTsegUDxYzARfr7GhQV6cLnQ66ZIuZ3CG+HvtvAEAIFVrmxRKT9tXZilvp1PI5lx5ZnU9JuGu1huqxa1HTu0X+iASCJseXmK5qzMaixtFy3\\u002fu6aS231JvHMpsvYQb6+s5MSrEn+\\u002fGoFBEKvWNsupzBaWWyT57b+WMjwyZHhve9mBbb+47aZ1t9+FxZg+JChO608hkkUp9u6I2tbBcZzTuuZVdIXLq9BvXVbnixudSD\\u002f1v9ExLo3YTin6Teflweetb3BvNVV5PmXixrkKsVV+pr1x+fPtbwycN87T5sNMYDbebas\\u002fVlCoMuo1j51gPJQi895ZkA8DZ6pIiaUskr+3iHoKiu4qz3ks73bZ1F72aPLFUKj5ZVaQ2Gh47u+\\u002fw\\u002fP901G+nh96L+xMb6h0RiYHOJNyxoR7ItWqI7IyC9ApsqAecOfWNSOjmdWwAuHUh\\u002f\\u002fBfF2+ezxsIWebbqfkbZhSufWXu8qenOTn5vSdO7by+6aMDls7XuG47vTvt1\\u002ff\\u002fVcntFCz0koqCuleXfjtt2Yh17y4aUBnDjrTUSbEhW+Yq1CP\\u002fdK12tdtceFEQ1xPiJvnnz\\u002fxz52oxdsCl5GLVd6\\u002fvvHU+76WvV7M4NiXtXPfeOoXokvv5m6g9FEXL8mrjR9rU5Wz\\u002f+kS3s+1mWdeKP37qr\\u002ff\\u002fegI7AAAAGpXujRXf9\\u002fzwKSet9NOn\\u002f\\u002f6\\u002fPx7HDrgOvrc448rxOz++tVvSjK3AxZnde8ct8f5e4V4eJY0i66DeaDp+p3D5yATroJMOp+e3v1KTHOwXJOBho71v6bD49gncSpEUE+mel2ePZ9G6XIKwIDl247GL7as\\u002f71TVOZNwV+n0dpvSzEmM7qjO14FHJwzbeiUDs7ZkP+rV9wtnoTXYeceZ1C7vzK41NMjRtPuEAO\\u002f2CXcmlZIU2OGjwjzt\\u002fEG5ZMUCDCGbNT8+en58n05c63LC3TpFXljfgkm4p1e2HsYtSY7bnnYHAO60679TUN9s6cQypF3CHYNGJgs5LvjGOpCRBwAcOu3jpTOn2haerx6VlFfX9OTmAy3K1jf199QbHSXczxeUnsptO2taPjzhzbkTaeS2X+Pa0UNrJfJXdh+7U13f0YLUuJ6YFRSZ1dJwtKLgXE3pCK8AXxZHZTTcaKhuULcecS4Ni58a4Pq5BT4s7i+TFz92dq9Cr0tvrn3g2DZ3OiOY486kUDRGQ5NaWauSm3fsKf7YZ18UGvd1xmWJTqM2GuYd\\u002fntOUFSYmweZSKxWyi7WltcoZSwK9ZmEMV9ldCcRQwD4esK8B45tzRM31ankT5z7d8fMlVQSCbtdj7HIrp9iY1dkQuBRbAzLegW5nrNegdAZhZmVKIISelYdYFGa2\\u002fkHRTe6txuNptSD6Xt\\u002fPuvyVqc9ZDIif396OPtayZu\\u002fPIxJDLmcqFGGZ9t7SNwk\\u002f\\u002fqlbbcu2Jl03NtQFD29O+3OlaK3f3usG38Cfcxxhbu0RfH+w78VZlZiB3rNiKldrlDDuVxOWun\\u002f1v8pbemjjNjVE1nl+XXv\\u002fP5YaKyfJejWC3kf5+HfRB0pza2xTrhnXi7a9s0Jq\\u002fFuSjudc2zrFbud1r95dUfPs+1m105mH\\u002frr4oJHJmAHegbfW5yEoujmz4\\u002fu\\u002fP4UdgAHAPf0ccuilLgvjmG7eh7MyO9ewt1uP5lFKR1Wofaq5GA7ybgWhcr5\\u002fi0d8eVx5yXFYKNOYFIp8f5et8qxp5xlTY6O9ywuFVa0748PACtHJWJDTuDQaTOHRO6\\u002fnYsd6Ce9937hrLHtTV6pbHFqD+w9EQ6vGPm787AhgDg\\u002fLwfzKrgMOplExDTPse6bMqh1YTKLWZjQg05pzS+37yqTXlkLACQicemw1tIqOwn3u49iUikdTUboDSQi8feHl2Cy7Waxvp4fL51huZtT29jRFZWfz6dZbk+ODntvwVTrbLuZH5\\u002f7+8NL\\u002fPhcTBznEgtDY7fOWBHm5qExGlJry3YU3TlUlmedbf9s7GzbR7jMCC\\u002f\\u002fg3MfsnSHF2s16c21l+sqbjfVVitllstI3kxsYppLpf06ZYkblQ4AepPpQFnelxmXPrudur0ws0Yp82FxNk9f\\u002fljssG5\\u002fPzHJlD+mLhUyWABwu6n2zWsuOGfrRxFOHKG69mQs71bXEu4apa6yCPvJ1m1OVbg78TvB+GzDP188v8W1vygXup2a\\u002f9aDP\\u002fZl5RGuG4qzqp+bs7Ffzlotmmolryz+5tjWK9iBAcZBwr2uovnFBV\\u002f1Zbad7caMHRaCjeL61q3zeW+v\\u002fqnPsu1m9ZUtry39Lv92uSXiJsAek\\u002fQl\\u002fJuoI9Zt3DVK3cbnNruqmnvTfw+0X\\u002fr70F8XzSuCusqfHx8SNbbOV3YVfG9xhtFg+ujxP\\u002fBse0fu7eOWBUNj2jf+zqysq+56ZXFmVX1Vu0fRKeRZQyIxwb4RInRvfyKMoKiuXYF5V81OjGr3g50V52enlxRm6dqOXCmuwIYAfHncxEBss2UnTY+PwIb6T++9Xzhr3m52JikezSzo37kOAe5u2JAVb66d1xzmcFIIgQA8JvZitkqnw0Q6Nfab31b8vfOl\\u002fce+On9lT0bO1fKqKonUaK9pe1\\u002fCfmR3ikQkxPp6mm9jEu5ilcY8kSTc0z3a25NNowJAvVTRpFBab2Zp4B7r69n+D7X3rB6VlNCuZbzFuIjgYAHfcjerBjtHBgCKG0Xm\\u002fvUAQCISXp89oaOXz6ZRn506BhvFuUKKp1+om\\u002fuxBQ+\\u002fN3LaME9\\u002fdzqDSiL5sLjzQmK2zljx5fi5Xequ3lWhbu77567dOmPFmuihUXwhj0YnEQhMMiWQw5vkF\\u002fpq8oQTCx\\u002f9eMxM7MMARnj5n1782FNDRkXzhUwyhUIkCRmsEV7+bwybdGbRuhRPPxaFat1qpqt8WNzfpyyhkcgAsK8k59ectitDLqdDNDmyG6cb9xyu+\\u002ft0w+5s2XWtyZVFxEGR3p32BlUrtO3PJ7unvrKlGxNyXdhVptMKdyqdEhTZ5YOzKUuHY0MDTNGdqrdW\\u002fqjX9ecRA86By0czX136bb80z8Uw6I3fv7Fr88ZOp770p+YOWsrUlje\\u002f9sB3DVU2c8B7W8qk6N5bug3njMvHMj945PcurQ7iKiqF5u3VP+XeaL2QzBPYOfPpM\\u002fg3UUesE+47vjspbrLTp7V7NCrd9m9tCi+aaiV\\u002ffnzIOtJzOo1+65fHsdGewfeWTpmMyKcb\\u002fr56Igs7gAOA++C4xYPNHB8VjI0CHLRXq+7YofQ8bAhg5pDIbrRecQkigWC3mFdnr0i8S2YndP8SgifXzhQxhdapPGDG3cYP1sZGBmFDThsW4uegRriP9d77hbOWEOBDade3oFGufHvPyfaLqfYZT3spdQsW3c5nSKe1yDQy9p9pd4KIY81KVUZN\\u002fZHcwl+u3Hj76OmHt+2b9uNf8Z98N\\u002fG7Tas373790MnvL17bn5V3o7KmTqYwte+o1Tuw1dnOiPP1MreOKWxosY5bGrjH+noRCBDn55VWVg0Ad6obrDu0FNS3punbr5jaqx4c0clkq6QAn4q7EzRa7FW4XyxqqxhKCvQN9OC1jbUzLTacRibrjPhVPteYGhBW8fDrlrs0EvmRmJRHYlKsNuncs4ljnk10wYWQcb7B43yDsdHOeDLYr6dMfD1lInbgrhMLH8WG7poTHGX9z7crSehbuPZlbNTVrracONmwU2Oy+QOhERlTvZZM9FxIABccBxBJxLA4f+sCPbvK8mq9AlwwRaar\\u002fWTM8m9XzFrlgn3JmSsHYXH+JHKXc2ejZsQHhHtVl7ReIxyYiu5U\\u002ffj2nhe\\u002fWIUdwPW349uufv\\u002fGrgG1DtuOb0\\u002fqdYZ17yzCDgwM0haF0WAiU2yOF5vrJG8s\\u002f77vT\\u002f7xBu79Kyet9LOn\\u002fzF2\\u002fVTBVTRK3Ttrftq47\\u002fnwIQF8QScnOb0K\\u002fybqSFVRg\\u002fkTo76yZf\\u002fv57HDPXNu7801L80W+rYWEv3+wX6dRm+7iQuc2Z32n9fm8lw3hQLfWzr17Ws7rhy\\u002fg412EYVKptIpCILotYbeXk20L90nxy2LU+Iu5GPPXA5n5D89dXRHhYDtGUymE9nYJssAsDilP5vRsWnU9sXjPXxD6RRylI8QG3Uah2EnpyzXdJ5wV+n05c12Jj46bn7tGItGDRa4lzb1aQGHA73xfuEw6BTyuMjg8\\u002fmlmPipnOKGX3e9u2iqpRK6L3k4XMu0ff8PAOi0STi1XcK9G1cUSESiqV09O4Ki9XJFvVxxs8qmqQCZSPRx4\\u002fjz3Px5XH83rj\\u002fPzXxbwGY5\\u002fVHqFDu\\u002fjk5ZlpEtb5HojSbLb8fcTwYAzG\\u002f8EH9vc8I9s6rOknA3IUhxU2ua3u5ytL0kwJ1nXcBul\\u002fUyuHJ7ly5z69oOAceEdXJ9kkmlxPgKM6tc1ncCh+t3x+q3Xmg6CAA0IsOT7kcjMnSIpklbq0M0x+q3ifXNS\\u002fwfxz6mWyISAjpNuJfn146eOQQb7bqurphqlp\\u002feyctzUllebaeHJt3rAkkgEJaun\\u002fLNqzuwAwPMqZ3Xk8ZFTl40DDuA6z\\u002fn\\u002fr35\\u002fZsD66zVbN8v54hE4qNvLcAODAAoirbUS70D26ZMKmXqd9b83FIvbduoTxBJxJRJ3WlXinOJ+sqW\\u002f67bZDT0W7bdTKvWf\\u002fjYpu+OvcITcKh0Sr\\u002fU2gP+TdQxo9FUWVgfFu+\\u002f+4fTLt9bjEbTv7+dX\\u002f\\u002f+EgC4c6Xo8rFM7BauYDSaTu26vvzp6diB7sL3FseOb7t6eneXJ7CSKaSE0RFDRodHJAT4BgsFPjwKte30XylTt9RLKwrqi7Oqbp7Pqy7urasdXD7LJ0jgEyxw9+yVS4D3z3HLpOhQPoshUdm0NqoRy9Ira1OC\\u002fayDDqQWlMva9Uf243OHhfhjgoNdmJdHTxoqMCh2plwbTJ1\\u002fYlfYy7YDQE+y\\u002fwAQ4e0xcBLuuL7x8PiU9gl3AMiqblj+w7apseGPThjW7T5F3cOxN7nBgmJviq0bk44N2WrfLMvSrtl5uW8+J1KpGxXKBoWyUa5sVLT+1yBXNiqUKr1N5YERQaolsmoJtiiKRib7unFOPvUwJt5t3Uq4361MNyFIabMoxqf1uoplxVRzwt2ST79jtYRxSZPYMjugLxPuUQ5b+5tZz9dof20EAKyXBQ\\u002fz7Ly0NkTgjifccfeMMmXehaaDVCJ9sd9jSfzxJELr34sJNWVKLu2v\\u002feO66FSc2\\u002fAoTpLNw7rFmRSzq9b+yrvZndR5TUmTSq5hcbHtxrqq034y0K0G7mZTlg7fvPFoTyaqe3i7eQd6eHi5uXu5sbgMGp1CpVOMBpNBZ5RLVOImWX1FS0VhfQ9TOZs+PDBq+hAGy9GXN67PXD6W+eWL27rdTZhMJgVGeXsFeHj5ubPdGDQGlUIj67UGvdYgFSmbasR1Fc21Zc3dPive89MZ\\u002fzDPGStGYQcGAOuEO2JCPt3wT1WRnfZ0vS06OZjbr+tk3s+0av17\\u002f\\u002flVLrEzS9JJXD4rMNI7MMLbzYPNYNEoNLJWrddp9NIWZX1lS31Fc1Ot\\u002fdP49prrJB898cenu5\\u002flCzmdTqXqPfg3UUdKc2o8vN3O\\u002fXsLOwBAY1DjR4SFxvlx+SwCgdBUK864XNilZGjqwdtP\\u002fN9iApGw7esO1\\u002fUhk0kxw0LC4v35Qg6FSpa2KAszK7OvlyBOl5Wd2XvDhQl3wPeWjlUU1v\\u002fyf\\u002fuwUYc8\\u002fd0XPz5p+vKRDhZ6Zbsx2W7M4GjfSYtSHv+\\u002fxVVFDUc2Xzqx\\u002fZpB380p2gQCwd2L6xss9AkW+AQJfIOFPkEC3xCBg9fQc\\u002ffVcQuZRJyXFL3lSgYmfig9z\\u002fmE+0F7\\u002fWQWpcT1IDU9QEV6dZ4C6g3mNsvtdVoA6pgvr1euV+EGsmEhfguTY+3+zaIonMktOZNbMiTA+8GRCbMSoixrbfYeAgGYVDtNYxzrm15VRAJByGYJ2ax4Hzt5ZpVe36hQ1ssUjQplvVzZoFCYE\\u002fGNCqVE3XYJU2c0loucPdJ2RnfekmAPPptGVer0AFBQ32xOuOuMxry6JgAgEgjR3kIAGHJ3lYm8ukajCSGTiABQUN\\u002fawN2NQQ+wt4JtL\\u002fHsbBaDM6wnEHlyHLUuMuOzevHYAofrY5dbjgLAAwHrk3jjrOMkAinFfRKZSN1W+fV10SmXJNwjEjpPMbsk4a6Sa6qKu5MRQ1G0IKMiZWJPy0idWTHVmcsPdlGo5IWPTfzrk8PYgY75hQijk4NjUkLChwQEhHsxOZ1cjgYAxISU5NSkpxac23+zS+kAC3GTfNf3px5+Yz52oMd4Ak432t+baTU6J1NUbh5snofL5tTbJfTlYUO9ozSnZuOzW5zPtliExPiOnpUwfHJsWLy\\u002fdfWcXSqFJv92xbUTWddOZnVjBYXv39jlGyyMHxmGHehv1uum\\u002fv3Zkdup+VaDjnB4zIjEwMjEQJ8ggaefu8CHR2dSaQwqhUpWK7UqmUYpV8vFqorC+vL82or8+pqyRgd9AEZM7c8p4fe5Tf890L2GGJ5+\\u002fBkrRo2YFh8+xJ\\u002fgMOchapTlXC9NO51z5cSdTpOGuTfK1qS8q5LZ1EL2MfybqCPplwpKcqoxDcG57qzVL86evnxk+2Tu7dT8H97c7eSCEJJmRXZaCY1Ozb5egh0DYHLoy56aNmfNWK479vyooUr087t7b5zNxcTtqi5urKto9g3uUc2mNXxv6ciPb+12vnc8mUxa9eKsB56a2unXMUZgpPeGj5YtXT\\u002f1x7d33zxnJ79jV1RS0MSFyeb0uneQB5Vmpyi499yHxy2LU+LaJ9xPZhe\\u002ftWCy3WYOGFK19mIhttiIQICFybGYYM+pdPqKZklFi6RRrmxWqGRqrUqnV+sNWoNRZzC2\\u002ft\\u002fYetuZyvGu8rK35mQfaJIrsSEAHpPew3yo497ZPdfv7xfOrrcWTC6oby6825e7vezqhuzqhk+PpM5OjFqYHJvUmwXvVBLZ4VGqfc58NPU2FpUa6uEe6oEtmzaYTLeqao\\u002fnF6eWlNfLu\\u002fzx3qnu\\u002fMsJBIj19bxRXgMARXfbuOfUNJr\\u002f6sI9PRhUCgD48DgCNqtFqdIajAUNzeZ6dksD974sbwcApisuqiislso1\\u002fxsdYzqxDQ43WFSoCqlEWoLbaOwAAAAMcRtFIzIqVXYaAnZDQLgXg0XTqNr+4tqrq2jWafQ0Ro\\u002f+tPNulXe7YqXgtgsS7p1WuDPYNP+w7ndnm\\u002fvQuJ3fn9IoHf0m2W7M5InRwybFpEyK6cZUXyKJGJkYGJkY+OBzM3LSSrd+eezO1WLsRp05\\u002fM+lFc\\u002fOaJ9f6KHZq8fMXt3NVvsZlwrfWvkjNmrPvP+MX\\u002fPSbGx0EFLJNR898Yfzp\\u002fQAQCASJi1MWbRuUpcuC7E4jGGTYoZNinnqowdSD6bv+fFMZVEXZoMZDaZPN\\u002fz9y7k32W6Oegj2PUvC\\u002fdb5vL0\\u002fn7UdtCMg3Gvc3KTRMxMiEgKwY3fRmVTLX+XI6a2d2fVaQ9a14lvn82+dz6stxx5\\u002f4wn3\\u002fpJ+seDolsvYaGe8Az0eeWP+uLlJTq5z6+HlNnFh8sSFyWqF9tTu67u+Py1tcXR6IBPZOe3vY\\u002ff5N1FHUg+mYyKxw0Le+f0xvtD+Pz9lYsx3x159a+WPJdnV2DF7Tu1KU9ibbBGZGPj2b495+tkvtPQO9Hj\\u002f7yd+eHP3sa1XsGP23DiTu2jdJGy0B\\u002fC9pb3Ug+k5aXZaCtjl5sF+\\u002f68nopODsQNO8wpw\\u002f+Cf9bt\\u002fPPP3p05d+airaJ4wP9nD2w070Pvuz+OWKB9hjK9nfl1rFaOZQqs7l1c6OyHKOmjX8TuF7ZsjjwgN6HRVQycptLorRZVXiisyKusrWsTdPc1yDZekgLpBbNvzx6zntZhcez3le2hAvV84u9g06t+PP\\u002fDyjmNXiyuxY1YUWt3utKzdaVnBAv7ilLiFKbGddk7vhvbN1p0xcNb7tagQSy6WVlwqrbxRWaMxtH2JCNmsCWHBbdv1WHcS7gAQ7+9tTrgX320jlX53xVTrtSASA7zP5pcCwJ3q+taEe0PrmWFc3ybc23cF6gbrxJwz7cDarymMww1eGpPSjSIg3u0kg0EkEFlkrsxgs5BytxGIhLB4f8dnFyiCVhTWRyUFYQe6onsrppoVpFdgQ11kMiKddpyIGBLouNrRMRaHMWf12H2\\u002fnsMOAHD5rNGzEsbNTUoaF0nu1ndne\\u002fEjwz7d\\u002feyFA7d\\u002femePQqrGDndMrdCe2XNj\\u002fsPjsQO4voKi6MbntjhZO2mWMDriyQ+XhsR0fwEoMpk0denwSQtTDv2VunnjUa3aprmeA6JG2c\\u002fv7nv1u7XYgX7VXCcFAEmz4osXtjq+kjd8SuyidZOSJ0RjB5xDpVOGTY4dNjkWYGlDlSj1UPrp3Wm1ZU0AIPTl9+QdwXWbTqP\\u002f5pUd2KhDBALhgaemrnlpNpXenfoMJoe+6LFJs1aO2f7Nib2\\u002fnO12O4U+gH8TOSMqKeijbRscJ3A5POaHm9c\\u002fNe1TZ66jnN17AxsCiE4O\\u002fnjH046fhUAgbPhoWWlOTWGmo3N7s5y0Utcm3PG9BQNF0a1fHcNGO8Dlszbuez4gvKdn2QQCYcUz05ls+k\\u002fv7MGOtaOQqr99bceHm5\\u002fEDvSy+\\u002fm4ZVFKLCbhDgCH0vOdSbgfyrAzd2GRK5ZLzapu2HIl\\u002fWxuqc7YzZZELsfqp\\u002fJHpb3lAB13vnZGz3+CtQH4fuE6wmXQf3tkyY7rmd+duqqwt3dZq2iRfH3y8vdnrk6LC18zZmhPluptr3t5ie49yuXUesPViqpLpRWXSitrpG3d2wkAcT5ekyNCJ0eExPl4ufa1djfh7tv6RW5ZtyG7pjVtNDSw7R1NCvQ1J9xzalvn6xU3tubjhtxtBD+IcOg0raH180ht23TfLo2+C9fbcbgBjkFiq4xyBEWIBDuXrxAUURnlDJLLZrpFJAQ6TrgDQHl+XQ8T7g5WTHXzYK98fqaDjpkFGZUoivbk+6OquKHTLpkRiR2Wvjpp0bpJB\\u002f9INd5dPINIIqZMjJ7x4KhR04eQKa45X8WYtCglamjQu2t\\u002fMWcAnXRix9U+OHHFdeT4tqtpZ3Kw0Q6QyaSHXpv7wFNTe7L\\u002fW5DIxMWPTx46Puq\\u002f6\\u002f6oq8DWa3fk3L83x81NcsnKya5irnD\\u002f\\u002fs1dDnJhobF+6z9YkjA6AjvQXd6BHiuemb7imel5N8tO7U7Du7f3l\\u002f2\\u002fX7DuKdQpGoP6yjdrxs1Nwg50EZ1JffStBcOnxH785F+OS937F\\u002f5N5BiLw3jr10cd58HN+ELu+veXfP7sZuyAE3gCznt\\u002fPu7Ms5DIxPUfLHlp4dfYgXY6XeK+G\\u002fC9xdqNs7k1pU69KiKJ+PZvj\\u002fY8224x\\u002f+Hx1SUNh\\u002f++hB1o5+a5vJvn8oZPicUO9Kb7+bhlXlLMF8cuYXp6XCmuFCnVHmymdRCjokWSZbW6nhmLRp0R36PDkiqR9JPDF9p3qul3zvQk6A122610rzTYmquqOQfs+4VzgECAVaOTZg2J\\u002fOnc9b03cuzuY9aMJuREVtGJrKKhQb4bpo4aE9GjnInFwCtV71xBY\\u002fPF0opLpRW3q+uMVkt1smnUsSFBkyJCJoaHCFiOPjl7wk7izBmW+vRGudLczN2SUrdJuAe09g8yp+Mlao1lfk0ft5RxCS6jrS1gi7LzIgiZRosN4XCDVgAzXIdocuV2CqYAIEeWpkM0QaxI7EB3OZNoLs+rxYa6wmg0OajeCov3dzzbVClTO3kK1JGyzvrJAEBkYk+\\u002fHQU+vImLUgCAL+SsemHW5hsffLj5yXFzknrprNXMJ0jw+d7nvAKwXdIcKMutdX4xQJxrSVsUzvfMpTGo7\\u002f\\u002fzxLIN01xy1moRHO371cEXu9Rz\\u002f7cP\\u002frVkZAaCuormQ39dvHYiCzsAAAAEImHl8zO\\u002fP\\u002f6qC7Pt1mKHh76wceWjby3ADuB6n1yicqaJkAWFSv7gn\\u002fU9z7ZbDBkV\\u002fsW\\u002fzwt9+diBAQP\\u002fJnJs9UuzOurx0t6kRSnBUV34qLTY8NEDPAEHG+1ATEqI46MgM3GTvKVeio32DL63WDv010VsqAMLH53o8u+Xde8s8gkSYKP2\\u002fPP5EWyoN93nxy08Jn1STCgmaEKQo5kFmCDG4Yx8bAhgdkJkT3qLH0zPW\\u002fztlp5kb+kUsjuL4dI3p5UzPQl6Q\\u002fumPeCKjgvmNRF7aCC\\u002fX7hOubOZ7yyYcuLVR9aOHerk9aSMyrrH\\u002f\\u002fz30U17SxpF2LH7w4Lft35x7nJaZY0RQehk8ogg\\u002f6fGjdy85oEbLz\\u002f1\\u002fQPzlibG9V62Hbpd4R7g7sZj0qVqLQCUNokC3HkNMgUAeLCZAe5uls3i\\u002fLxIRKIJQcpbxEqdvqxJbI4L2CyvXl7zoTeECd0tFf2lTaKpMWG241gVLb1+EIbD9ZmRHtPy5bf3VP9kQk0JbqMtde4Iarojvfpvze8AMMZjls1jeiDSiXVTy3qWcC\\u002fNqXGw4lx4fEBYnD+JTHSwOGFBekVPKolKcpxJuHf+e+jUg8\\u002fOGDo+auKC5F49WcVw9+S+\\u002f\\u002ff652Zv7LSK3yLtdE4fFIvh2vvtg\\u002f1KWedXkQGASqP8b9uGuBHY0zyXcPNgf7r7mefmfOFkpXBDlejYlisLHpmAHegn1cWNP7+7FxsFAAAWh\\u002fH2b48OHd\\u002f5XG\\u002fcYLT7h9MqRWtBSacIBMKbPz2cOMbFeTG\\u002fUM\\u002fP9jz7\\u002fNwvutQWoy\\u002fh30Qd4Qs5cx8ah412jEAgzPvP+B\\u002fe2o0dcCgqKWj8vKHYqEMTFiQX3anCRtupKm4Q+PCw0Z7B9xYzuUSVebkIG7WHw2OufXkONtpjVDrl0bcW\\u002fG\\u002f9n9iBdkpzatIvFnS7VVpX4ccti1PiTudg1x44mJ730LhkTNACRe0n3Bf3oJ\\u002fMHxdvfXW8kzkQnlx2jK9nsIAf4O4m5LLcmHQuncakUhh3\\u002fzOnxYe998M90x7Abm4dcdhssG\\u002fg79e9wduN88a8SU9PG73\\u002fVu7OtDuVLVLsFu2klVYv\\u002fX7rU1NGPTF5RH9diOpfHBrtgaT41cMSAvk87Fhv6mbCHQDifL2ulFQCQEWLxNJIyLq8HQDoFHK0tzC3rhFFIbe2sUYiM8eH+Hc\\u002fRdWP4vy8TuW2frFdL6t+YuII23EbJgTNa9dbDYcbvGK5w5L5E9IlF7dXfrOf9LsXPYBGpOsQbaO2WmNSAcA4wZwITgL2Yd3lGyJkcRiOUxgVBfXYUFc4buAePsSfSqcERfo4SOsXpFdMXz4SG3VaWW6HP9mMw2N6B3pgo13nH+bZk5VXuy04ymftK3P+\\u002fPgQdqADWdeKe\\u002fXEFWdX3s2y8\\u002ftvYaP2EAiEl79Z00tnrWY8AeftXx99ZfE3TpaA7fzu5IwVo+jM\\u002flkUy0lcd9b\\u002ftm0IH9L5rB3cYKRWaJ1cXtJsyROTR89y2XelNZ8gwZs\\u002fP\\u002fL26p8GZj93\\u002fJuoI1OWDKfSnKpTsxg\\u002ff+jP\\u002f7fXQUFAe0uemIwNdSZlUsym\\u002fx7ARtupKW1yeZoV31vMrp\\u002fMRuyVyra34NGJDHbnzYK6YeycRP8wT2fmdJ7ccc3le4Jd+HELAIyLDBZwWC0KlXWwoL65uFEU4WX\\u002f3OF2RW2tRI4JBgv4Sd1t8Xw8q9BB9jbW13NhSuzEqJAADx527F5Hsdc9xuDc37IDSM++3PH36x7DodMeGpf80Ljkm+U1\\u002f97MOZVTbGl\\u002fbZfRhHx\\u002f+mp2TcNXq+bSyN3PAw9SCp3ur7Tbf6Xd9uFyRgUHjAsNGhsa5M7s6VLGnbJz8c1Jlq4yNRJ5fn1r8zJMwh0AEgNae7UXNbZUiqTm2\\u002fF+g6+BOwCMjwi23L5ZXlMnxX5jWbtWWom3lMHdY5YHPD3N6wEKkaoxqSpUBYWKzApVgcakopMY83z\\u002fs8DvEewDeoBAIIQP8cdGbSllaicrSuzKu+loMp05O+a4wLyHrUsdpPLNIhw++6CwaN0k56fJl2RXY0O43rft6xPYUAfmPzx+wvyuFUh2Q9TQoMVOJ4YkzYrTu9Ow0YGEwaJ9suMZPNt+Dzuz94ZG1ckCVhbB0b4PvzEfG3WdoeOj5v+nF9N\\u002fg9QA\\u002fyaasKDDitSOcPms6ORgbLRjXD5rzJxEbLQzQRHeTE5bO82OdKnv+cA3oPaWm+fsrG\\u002fZHoFAmNGD+g\\u002fHCATCtGVO\\u002ffDrp3I0Smc\\u002fDHsCP24BABKRsGBoDDYKcPB27v+zd9fxTV3tA8BP3Jp6U3d3o7i7DhgbjDFj7r53rr\\u002f5+465uzCYMMZguGsLpS11d2\\u002fTuMv9\\u002fZGS3pxGbtKkTdvz\\u002feyzT\\u002fKcBEp7m3vuc5\\u002fzHDh01T9FZg6ndbkpcIiYAan81d3H4CgAAIAQb88vblv\\u002fx0NbbpqZPTmzt2Y3azW7k6pdFBrHK8rRz2sCy4sOe2vj8tPP3\\u002fPK+sXGJK0lJ6san\\u002f39EByd0M48ctebq5euTEnwYjEBAF1iye7Syif+PjBj2xfrvtn+3vGz+c1tNnviO8zxhLtx19NOobi222LCPetqpL6H33K1xcp4bOAOAEgKDkgKDjA81un17x44bTo+RKfXf3DEjnInBBkXyCTy0qBNzyd\\u002fsTni4QW89TP9ly8KvHZL5GPPJX8xN2A1\\u002fOoRI5JutpmztqKy0GKFO4fLMpSWJ1jdlLWltpt4ngXS2z5gczGs9XT\\u002fuECjU9ffRfQipLuV77bNECaq2iutRadtdPw0CIkKGLX+4Dc+utwvcKg9nXV2FRePMhKZ9J+Pb4lJDYUHkAlk308W68WGu\\u002fvl9a5ukXHLf1b5BHjC0cnNnc9EHE9WvEM35Ozq1p23KJVqruLSOhKZRKRZfJ+ze7iPLbc6WqqKCBV2JGRF8MJ84ajzENxwQq3SXDlPqAHOSKB5i5HZVjD\\u002fXqkx27pErdUdHtaChkwiXZPjYML9ixMFxiYHeCkhvB333zAnMQoemEx8zG1da+jGPBJmv+EEoZ\\u002fXhMdh0K+fmv77gzf+et8Ny9ITKJa3Nz1UVrsj\\u002fwocnbgCuR7XZaV+cO2qgsfv\\u002fWPr5kfmzcwJD6GQyRgAld29X56\\u002fdMsvf0753+d37fz7x4vFDf0D8PtHxvGEuzFp3i4Q1XT3AwDoVEpqKLz6z1jhXtfb33a1pYzNGy9u674F042PD1fUvbHvhHrY8jGRQvnojn2onwwyUbGp3GyfOSuCb1wXeseyoM2Z3jOZFDOzipGLJ9DGvamqEw4R093KH+i1uEglNi3MsLGS9ZS3Xqcn0t7UrAZb\\u002fWQAsUb27m\\u002fRdVOJJ5ja6nvgEOJKOz4kWuNw+\\u002fPXMFgjXQFNEJNNJ57vaK7utN4eagxde9eC6UvT4SgygZQXNLTVEf3UmrIgZRT6+HO4rA33LISjk57bnonSpsaSLF8VW0FkjmQ0dZGZxBwRYQQ2qhnoHry+mzDc5Gjh94j4xL632bNd+8ESGh1AsOqfYMf5kUDzFqMYnm\\u002fG1UyLUa9Yml9v5trkXF3z8HzrzPhIx\\u002fbVU2q0ey6bqZfnMhmf3LrWn8uBB2zRY5jKajeM8SXA3HeAL5WNsIqWL3Hw9h76eU0qmRHB225c9c9jty5LT4DHrvr06IVJ2IKfTCJlhgY9MGfazls3XXzi3k+vX7NlSma0nw8AQKHRnKpveuPwyRVf\\u002fDjvo2\\u002fgd46A4717Aj09\\u002fD04\\u002fVJZTXe\\u002fTKUGAKSFBtIo8Owk3Nfbl8MekMlruvsxDAMAhPp4+nJY0MvMwjAgU6v7cL3JdHqsVyL1YDDY5tbpjIIlKXGLkmOPVTUYnm7PLzlR3bgiPSEmwJdFownkirL27mNVDYbz2Yr0xANlNSbvRxCEMOvJbgOHE+4VVme6xm42UUkhdAZNrbJ4Qqq63OTY3neNFQR2TLVaXz9ecL3ZqXkxV87DZTVm9U+sQjk319M2UHCkHI6ak5gVOWuF3e0IRmLFlpnb3z9AcAXJ4d8LUvJc2KHVMSFRAbc8tQqOTnSfni94\\u002f8x5OArAnzffkBViu1R2hG8ffWf\\u002fLYFDlm18YDEcco2VN83a8dEhmdjaJiiTjdueiWJtdc+zJCbFjqUzDvewDgy1XTfN7yGUFB5H3ORosbnTj5Fd\\u002fYUck5AZ2dthu4tjnYt77KB5C2RdbmppWzcU3FtcNTMevoI4WGrmXojD\\u002fWQKm9qlKjUcBeD+xdMdy+CL5EqzhfnjVIS5xiwYBtoHRNEBtj9ULbHe0NgK9POahKL8fbbduOpsberTvx0YvrpCIFP8XVS5efqofki6FS6DsSghNjssZHNORk1v\\u002f5nGlvzm1m6xFADQJZbArx4BxxPuAIC00MCTNY3iq53Kh\\u002feTMcgMDzpR3Wi8hZIaYrFW4suTBWfqmiVKlUSplipVMrUa+kXul8rmv\\u002fM1AIBCJrHpdA8mnctgeDDpi5Ljts7ONXmpy7xz\\u002fYq7f\\u002fyrqGUwzdcpFH97xsy2LXfNzbtxehZKuCOIw4Ii\\u002fDx9OGKByXZAkCZHW8pYLy0xNlymUMnRKSE1xS2m40Oqi5rhEDENtr5ynwBPvyCiq1PdXM68pDG\\u002fcEWGO\\u002fbnRcONcJsc2G1vhNhc5rQlaSf\\u002fvgwPmHPxaAWGYYZVKe7j7lfW05ljUxyAjJoLh8vgkAUxqaHp0+PgqGuwPBiLr5u657tT8MDk5p5nothUBxPuvFAfKo2i1diul+SF+RJvdgHxD\\u002faGQ8O4tKfKWHGHo6WnjQ+HLIgk0PlnhKKSgs\\u002fuL4GjwzRV2ZjcjhCat0BWZia+s++USmtSa3ysskGp0TJpQ3kejU53sgq+8PFkMRelOHhWGp7lBwBQyKRVmUlwlBiBbELdIY72N78ipKarfyQJ9+ar\\u002fZnthX5ek9bshKjv7rzu5i9\\u002fN1RI452paZ4kCXeJStUmELULxe1CUYdI3CmSdIrEfVKZQK7QETuhjMSIEu6poYEna4Y+uy0n3INPVA+9zNj8fbiqrj5jIts6nR6TKFUSpaoLSAAAEb7e8Ctchk2nfXXrtf89ePqPS2Vm7+yx6LRnVs67fko6AMCDQTd7OxFBxp39Xdv96IHT\\u002fMwX6Okw7dn+\\u002fTxGWLKn3Xt\\u002fWRGXEV50ylqjxo6mPrVKQ2fYndWykXBPG2qompgV6ZKEe7mNCveELNsF\\u002fuMF\\u002fvtpnbDfmbeUEeuO7boEh8zxC\\u002fSatTILjrre3DU5BC9cBX3iutI2ImtiRk1iVuS0xWlwdBLYOiVneWK8QKEQKJQDcvmbx0\\u002fL1HbMgkb49lFWX9bW20601ePyzTPhkCvNW5uDEu4Q9zwThUQHwCFiSGQSL9S3s7kPHhgmOtn8BRoRXn62ix9VCrVOq6dQHe9T6obc4WghUlEOACBTyIGubOBuQLBHvEKqEgtknj52d6ggCM1bIFwmY1Fq7P4rJhV+MpX6VHUjvpvE+bqW4RmJVZmJdPu3djBo4wvhEAAxAb5+5nqXE3GlrQsOjWdebGaoj2eHAC5IL2ntXJ5hscuHTZUdvXCIGPTzmswSgwMeXDzjnX\\u002fhOWHphPsh6vT6TpGkTShqE4jahKJ2oahVIGoTikRXC8Sto1EooV6eET5ekU7NLY8o4Z4eZlKrbtwfFQLFx+mOqXhsOu3laxbdOC1r35Wqc\\u002fUt3SKJRKn25bBCfTwXJcetyUry9xicZ0QH+Ja1m7mjiCDjzsnev2M8Uiwl3Ckk6qGuncGsSOcm3BMyIqwn3PU6fUtNd3wG0esiA5lY0VJn8ReTwaKHxQ5tR2F9NiziS7ta+oMj\\u002feEBq2Rihc00zcRo4G4QRfhqX6WArwcQF6m81EgkUwMAmLMme0wyKZkz48kUsl6nhwfMuXiswvqv6ijb9NBSODQ5sOm0WL+hvMz7Z87blTEf4dtH2cVjFXDIAhKZNGvlqJYRJedG88J8bZ5oJhX3PBMFR\\u002fjBIcJ8eFwiH+ORCY6XP3t4EUrHyKVKrjehV44X7nC0EGzg7u3n4dg2AHbxDfSEQxbwu0UuSrijeYtZ63NToYQ7AOBAaS0+4X64vB43OMjsnqsEiYe1gwcABDjUnMSgoMG1zYhGX3ZkyPCE+9laiyVcNtV09Q3vwk8Q+nlNcuunpP53\\u002f2moVlgoV+j0egp5DD4qXSTt7Y91ekIfvywaLdzHK9LH25Bej\\u002fDxjvTxDvbikke86mi4ESXc5yZEV77+GBwdZmp0GJGXAQA+2LwaDo3AvfOn3Tt\\u002fGhy17JHFMx9ZPBOOWhYf6PfY0tmPLZ0ND+D8du9mOORmMADEKqVCo1HrhlZUeDKZPkymyesQxBYO1ZOvspjFdkw8gZloU1WHvQn3qstNmN7M8hSDmJRQ\\u002fKWLzX3Jqoua7U24NxBq4G7j7x1HfAK4FCpZp7V9ClQrB5uPIa5GvBXGnFVZcGhUsLnM2NTQulJCc3rra1ZGmU+Ap8NbFCLjiPW9QPBS82J8eUTTVc6SMzfp4K9mGuJPWm54JvIJ4I5kU0cff0IHVWiMg0X0AACOF6Ftt+SSiZZwd4ejRSE3kyMbzmNUvvNsD6IXhhKhtVaQI4HmLWZNj4sI8uJ2i0xWWpypaVKoNSw6DQCg0+tPXN1\\u002fzigu0C\\u002fVtHTSLhRzaSmH99iTqzWna5rg6Dg3Mz5yXwlcNNbUN1Db3Z8QZN9lo8GJYU2BiEM\\u002fr0mOy2T4cFh8qUn\\u002fNwwDcrWGy2Tgg+Oa2Wy7F5MZ4etlSKkb0+sBV8ujR8GIEu7IOFXL7z\\u002fZ3FzS1VXV39clkaiH7Zd975S8\\u002f8yeAwURxDoKiSrRC+HoyBDJpDdVEupDhWc9S2LcMdUgPC6Q5cFQSC1e9lQXNS9YPwWOWkVkIyybif5xhEQieftzidRqqVx24YpAik7DlwFmcTxZybnRcHS0xKWHE7xwJfiy0bHourwxqa1DRhOmx4i3FMuekwiHXC9zZjxKuOO54ZnIZ2S3YVgehK6TeQQ2PrWEQWwjCq3GpIX0BOAOR4taQehPdqCtogOI\\u002fy1qlasOBjRvMYtMIq3NSfnyRAE+qNRoz9Y2L0mLBwBcamoXDdsycSTl7QAAb7aZW3EE+zYMt\\u002f188fCvcLybnxRDIZOHZwC3ny959Vrz68Wt0GPY7stEF9UNh35eyPAe7mAEN13cU4AHJ8LHO9LXO2Kwet07wtfLa6zLiFHCfRJRaLU7y0p\\u002fLSttGBiAxxBkZJQ6hVgzQCc7XqhlVkCIj08AV9BnrT+mAxs0VV6ydlseatxJIpPi0sLL8s0sxjSoumztTzPLZoU7L8yXSOPUcYRJrIhPR2wdLjJCIr6U4J2qtGmxo7BW3ZKI+CA4ZIFUJO9u5QeNoDmDE81Ymg6HkAmnqbpTLiF6wZkxIx4OuV7GzDH4S92cu52JfANGlnDnEEu4h\\u002fnAIcII7vxMpBJ83Bnzo0WtIpRwJ4\\u002fKOZr4TECnhQu5nALNW6xYlwsn3AEAxyobDAn3o8P6yVDI5DXZyVDQLj4eZhK49T18DAPmaqmt6RJKvj9DqPH9+OLFZs5Lij5eCa8t+LuoYuvc3CgLu6pa8m9JdfuA7ft\\u002flqCf1yTX0MtXDrsvHuTFnUj9ZAAA5x69Gw65AZRwd4QOw8wuzHFnf1ZU\\u002fPf82T6Zc1b5\\u002fVpW+s7ZM\\u002fjI07Pn3JiegY8gE4BYI5DrhpLdar2qW9mKGx8k04pP9u7RYppIluP7wFgSlx5+6XglHMVpqiI0\\u002fzbSafU1JS1wFCcuHS6rT8iKsJJwb6rsVCs1BC9KDWxWuE+kBu4GNMK1UcgoKD5Tg5nb9Hu4tKmxcGgUhcXZsd65vrzNKReuI8ThshKzo+AoMuFY2UwbQqNTk3Ki4Kjr+fI8fQI8BX1wD9nJzN3ORCOscKfRCV3H+YwgrU+hELoa17omxzq2xvxoIVhUrlHDaRRXIN45h+CNCnuheYsVEX7euVGhl5tNLi7O1DTpMYxMIh0f1k9mbmK0w7tlGiQEmmmKIpAprrR1ZUXYsWmEQq154Kc9E7VcevP0zOEJd61O\\u002f8Kfh3+8+3riuc4+iey\\u002f+0\\u002fDUXugn9d4pMcwjU7HoBI60Vv3y\\u002fkSOARA2giaSiHEOeHnNwn9UVH+2aWCBdEx16ekpfF48LCbEatUjx7cf7LJ7iJcK5bFxr184jh+kdSOslKUcJ94jvf+db7\\u002foPFpu7xhW80TuHHYbP9VcGjE4jMjrCfcxQIZv1vkF+QFD1jQWNluZYcrGp0akQgXp1hPf2u1urrSttSpMfCABVqNrtXylq0G8Zlw0n+8G50KLISgysJGOGRBTEooHBpFdrW97mlzi8Vb6TPiRrmfjB7Dsj74VK7WZAQH\\u002fnXLjfihoo7Ojb\\u002f8Znj89XXrFsSarLLf+vtfZ5pamFTqlccfxJcRtAlFR+rqC1o7avr6+XK5WqfzoNNDPLlpQYFrUhJnRlr7PJw8WmttfIwbRSYGU2kUODoqopNDUMIdz93ORCPse06h2j6uaHQqwUJ4s8jEEu4TssJ9zI8Wgg30FTKLPQ+dSC4lmmJjsh0\\u002f3qxA8xbr1uWmQgl3oVxZ2dFLJpN6RFJ8HACwLjcFithrRlwEiQSG3wH59OiFr7ZeS7AusV8ie+zXf2u6+uCBiWJmfGRmRPCV1i4oXtzS+cKfh1+\\u002fbhmFwIcMXyrf+vWfUPdte6Gf13gkU6mXvPPt6qyka\\u002fPSUkIcTznuK6n+vaAUjgIwP5lo7gIZCUKzKARyqqW5XSz++UpJ\\u002fQAfHnMzHWLx+p2\\u002fOjfbDgDwY7MXRJlct1f09lb09uIjyASwgLd+Q9g96V7T2VQuPGaKxwjdHPFwqlcePDBi1pPdBnZ1lbHewD0qKZg67Ao2ISsSikCId\\u002fIFALTUdGk1NmrBEjJt\\u002fI0IMhKNlUR\\u002fZaKSQ+DQKPIJsPHJg9ffKYRDYyEuzWQTiFFAJpFSeDwAQF0\\u002fX296OVXUMXSZV9IJX\\u002fJV9\\u002fYDAJIDA4zZ9naR+Lbf\\u002flrw5XdvHj99rL6hXSRSaDQ6vV6kVFb19v1RWn7Lzl1379qj1I5GQaWba2\\u002fogUMWRCXZUT7mXGP4VyNEcLiEMqqWkCm20ySeviPaGYxghTsAwxI5yIh5eBI6PKz3XXSWgR6it+48iN0nsBeat1i3PCPBsEUqXkFj2+lqOAngw2HNTxppos3Xg50WBtcnAQDO17W8d+A0NBUZDsPAscqGDR9vLzK9STDxPLlijtl09j\\u002fFVfd8\\u002f1en0Mav1aGy2hs+3dHUN9IbM+jnNU5JlKod+Veu\\u002f3j7+g9\\u002f\\u002fvjI+aLmjuG7AljRL5G98c+Jp387AA8A4M1mrsgYg+2FJiFU4e6I6v4+AACVTF4cM5Zr1mzqlcm27PqzVSSEB5xhTWLS0UaTRVL7amtS3b7eH7GLF813mt\\u002fiaX6LdZj2hbKbw9lxd8Y8D78IACqJRibBSWpnScgiknDvnLKAaLGG9QbusaYN3A2CIvw8fThigcWOTHa1cbfZwJ1EIhHZLRZBHNZcBedezWJ5MOyq1XI6rrcdqaK+TgEcGgtjUluXFsQrbO9QaLStQlGUj7cxXtQx1HHrSqdJRTZfLjd0mUsPGlpS6sdml\\u002fcM5pFZNGoyjxfh7cWkUXulsoLWdplaDQA4Xt\\u002f4yuHjb69canzX5NTeSLTIIDp5DA4Jg5DoADiEuBO2p8v38uIQS9oibojgXj4qhVo8IBvhnRWbetoIFZmRyCRemC8cdQY0b7GOTactTYvfU2SyJri0tWtApsBHAABrspKpRG+kWXPX\\u002fKkP\\u002f\\u002fwPHAXg+zOXy9t77lk4bXpsxPBcc4dAfKG+dfv54trufnycTadNi404Maz7zXiXExW6eXrWrxdK4AEALtS3rn7vx9VZScvSE9LDAz1ZTAAAhgGJUtnSLzxb23y8sqGy02SmkRcdVtnZa3b3S5vc+eeFYUCuVkuVaolSJVWqpCrDg8H\\u002fN\\u002feb+TVRarTv7DvlwaR7MOkeTIYHg85lMjiG\\u002fzPpXCZjhNuBqrRamVItUaqlSpVENfjFGL8qgbkFB7+cLz5f32L6xdA9mAwPJt2DQecwGEQWNFhS291f293\\u002fxfECLpORGx2aEOQfH+gfF+gX6OnhwaTj2xMNyBRtfGFlZ+\\u002f5upaztc1qCw3fHloyk0lDqeDRgL7LjjBco4Z5ennQXdKlzim0ev0D\\u002f+51UbYdADAzPJxkWtBytrXlaTAHF0AmDgqJGsQMJ5PIdLLLLw4hPgGefoFe\\u002fB4RPIBDcBslg0qrFe5x6earU+MzIi6fqoKjV9lV4W6zgXtIlD+6QkZcp7d9QCaBL8DM8g\\u002f2hkOji0qjkEgkgm1b+7uEcGgsRCSYqSFyNWPSvLq3b3jCfWlC3OHa+itd3RgAxsl+Ve\\u002fgomB8wp1Fo96el1PZ03ddeurMqAg6ZehOqkSlenLfoWP1DQCAv8orH50zM4hLKBk0IWnU2p52ohVnwc5o0esYXqhLMl+Is7A5Lp9TuaihNjIKiN++barqyJyVAEediuCGSX6BXgS3FrALmrcQsX5KKpRwv1DfOjzdNvJ+MgaLUmLTw4PK2sx0V7vU1H7p23Yuk5ESyvPzYLNoNIlSJZQr2wdEZmu6KWTy+1tWe7NZzkrgupUnVswpbesqbzezKk6l1e4qLN9VWA4AYNNpdCpFolRbKmEO9uZ+eNOah3\\u002f5p7DJxoWkWW748\\u002frf\\u002ftOHyuokSpVMpbZZZQ\\u002fR6HQ\\u002fnSuCozhkEsmQf1+QEvPcmgXwsDmFTR0v7josVaokSrVGB\\u002f\\u002fi2HS0oh5UwEE8Fp3GZTI8mPS9j90KjxEmUapOVjWerDLJZrDoNBaNqtLqlBqNTm\\u002f7Ozk\\u002fOWbTtEw4iriG88+IkwGZRAIABHm49aXmF4WXLneanxsFc7nzIqPSAwMT\\u002fPw9mQwunTHzm6\\u002fgF9nix2Yn+PvX9A\\u002fd7azs7RUolT5Ml188IGMimBU1oDYzVxgF8ZkR\\u002fMNlcBSH+DrTnrYB67n74TumGsRnhltJuPN7RH2dgoAQH3jAHJsV7vGZtov6EcRhLYR7TwcEEzqkXYpKoxDcEU4hH40+tjb5BXnDIddLu5o0r+3rX54Yb3jcKhT2y+QAgE2Z6Ydr6yUqVSN\\u002fINZvMANb1WMm4Q4AuH\\u002fGNPxTIy6D8cE1K+Z8\\u002fo1QodRjWH5r27rUZPhFkwa\\u002fW4QRuKQxcFG9JxEBId5wCHEndm237hgXNdRGRoGlGelwVUXNrk64Vxc3wyFzopJc0s4FzVuImBIVFubr1T4wdJkzvBQ6OYSXGOy0lU\\u002fbbly16dMdA+aqfQEAEqWqoKENjg5Do1De2bR8dkKUHsO4TIZE6czviTtg0qif3LL2li9\\u002fb+UL4TEcuVojV2vg6FU+HNYXt633YjOTQ3iOJdyB+\\u002f28ukVSswl9p9BjmKEmvUtItOmWTKWy\\u002fjMaIYVao1Brel3wLzb8yXDUgrzosPc2rxq+mgFxEZRwd0SwB7dugC\\u002fXED2sR1+3VPrZxQI4CkAqj\\u002ffEjFlzo6IM9wxGaEZYOD7hjgFQ2NGxJNat2+wgDlvIWy\\u002fTEj1jOVd8Rni+1YR7e2OPVqMjsiud9T2XyBRytIXOjzabqlddbiaYcLdZj5\\u002fgfgl3hVTF7xGJ+FK5VKmQKuVSpUyiVCnUaqVGpdSolRq1SmN8fPX\\u002fao1Kq1IMPrbZth4ZNcSXMBedrl4R9jAcdVdq5diflDlcFpM9BvWk0b4+bDpNrtZU9w2dlC+3dwIAAjicWVERdApFrdMVd3YZE+7VvX0AABaNFnM1YhOLRpsXE72nogoA0CUem9OBm7BrJ9KAUEKnBlcgeFYaLybemWgUEu6j8Fe4pwlwtNjsZ2h0+WTVDQ8thaPO01zTxe+2Vq1ilDbNJZeBaN5CBIkE1uWkfHL0AjyAsz43FQ6NQIi35yc3X3PvD3+LFUT31IVwmYz3Nq+alRAJACCTSFNjwo5Vjqho2j0FcDk\\u002f37Px3h\\u002f+rjJtEUNQsDf3s1vXxQX6AQCSR7B5Jvp5TXIkErh+asZza+bTcAtYEVdDCXdHTA8Prxvg1w\\u002fwtXo9FdcyyX18U3R5+J5m90zJe2LmLCd+wYn+\\u002flCkfoCPEu4TlT8j2J8xNtuv2UxA67T61trumFTba2+t75gaERdIZ5i\\u002fNLXZSr66qHnummw4Okx3K9\\u002fmqth4AvvEuk5fp6Cltru9oae9obe9obevQzDQK1LK4RoZZPwieNk87qgUY3+UetuzW5oTkUmkVB7vUntHDS7hbtgxNSUwgEomJ\\u002fH8S7t6Sjq7rksfvNiu6usDAKQG8uy6AR\\u002fiOfgPNPRzn7SE\\u002fVI4ZAGNTvX0saOlr3OxuUwyhazXmV+f7s4myZloFLLhFKrTpv1uawIfLZmzEs7sK4ajw1RcaiS+ztIBp\\u002f6+DIcsSJvqkstANG8haG1uyqfHLlhqzkGjUFZlJcHRkcmMCN718JbHf\\u002f3XbK8S63KjQt\\u002fetDzEe6jn\\u002fvS4iImawPXncn6974b3DpzZkV9CpOmH0cKU2FevXeLLGew1OpKEO0A\\u002fr3GFRaNtmZm161K5UgOn9RyQFhb4\\u002fDULM8LHoO\\u002flJIcS7o64MS1je+kVuUZzoL5uTUIiPDzWtHr9X5VwB6nbsrKfnj0HCo5QjA9cFtcoINrSFBlD1Z191bgb7OumOLPYwRVsVpcDABorO4gk3K03cI+1vHrXL9DLl+c5YHkZGME27ja735DIJOKLiJ1Cr9PXlLSU5ddXFzVXF7XYVbmJjEfWuyqNXyqnVoo5huH69JklaUGBl9o72oQihUbDotHA1QbuqYGBAID0oKDSrh7jvqlqna6BLwDD+snYRLt6z96Oi8WJSMQnWuDv4cWGQ6OIRCKxPZhSkfn1425lcp6JXNHtGkJ2xu6I7mbyHC3z1uYQSbhjemz\\u002fz+dufXo1POAMapXm8G\\u002f5cNQcnwDP5NwoOOoMaN5CUIi357SYiPyGVngAAADA\\u002fOQYb7bzW7+GeHv+cs+mfSVV35+5XN\\u002fDh4fNyYoI3jp3yuLUOCg+PXYsS45cjU6lPLtm\\u002fnVT0z8\\u002fln+ssl5r6154blToXfOnzkmMwgdjeb50KmV4a37i0M9rvKBSyM+tWfDQkpkHS2uPVdZfamx3IPPuwaAvSo1bnZU0Iy7SngIbxGlcPs+bkBL9\\u002fR+aNv3D\\u002fAtvnTk1LTSMxxmz2iWzzre1CpUmC4WivH2enzsPH3GKGB+4kqJJQHTFHzKG9hZX\\u002fXB6qFbFgYS7QN3Xr+pS61UMCtOPHuRDd1o3QLM8fTm8UJ\\u002feDmtHV1OVjUQ2AEAuUTbXdMFRHEs7phokZEVa6WxTX9ZGpK1NQ7mNBu4RcUGj05JCpVBfOFRWcLT88skqiXAcpGMQZ5molWLuUMZLY4zZtCotiAcA0GNYbT8\\u002fMzhIqlLX9fMBACmBAQCA9OBAUAxq+\\u002foN6fj6fr5hV660YDMJdwyA0s7ucy0tNX38DpFYpFRKVWqVVqvS6dTDFs9NTkI+0Qp3D68x3gHbw4vlzgn3SX4mIpNdfvlLcd7C1jE3CY+WvIUpbC5TLrHd\\u002f2HPd6fW3jHP29\\u002f5q6z2\\u002f3zOSrkJ3tw12S66wYPmLcR9e+cGOOR6VAp5XW7q2pzU0rauwqb2y82dbQNCkVwpVqgwgDGpVC6LEeztGeXvnRkRPC0mPNzPG\\u002f4jAAAAxPB8K956DI4S88Pd18OhEVuZmbgy08m1lfGBfttuXDUglZ+qabrU2F7fw+8UiqVKtR7DOAy6P5cdE+CbHRkyLykmOgDOtAAAKGRy8f+NtGmSO\\u002fy8AAD\\u002f27zyf5tXwtGxMy8pZiT\\u002fHBfhMhnXT02\\u002ffmq6Vqev6e6r7upr6h1oGxD1SWQDMoVUqZKrNFq9To9hTBrNg0H3YNK9WMxYnl9CsH9icEBGeBCD6vi1yaZpGZumZcBRq\\u002fy5HAe+jXsevQUOTRSOf\\u002fcnuUemzQAY+Kjgwrqd21+cO39ZXLxdi7Jd6mIHnHl8ePp0V0y4\\u002fdhsKpmsxW2l3S+30SsDcQdSR7c3wQCW33\\u002f4VN9eaPdUP3rg3IA10\\u002f2XkoCrfgviMyJsJdxtNEYHAFQVNVnf5i4uzVppeUJmhJWEu0atbShvT8y2UYxvc8dUm71rRq7iYuPh3\\u002fPP7CtWSB08EpBxTUKgISzimFGoV7UEv29qZnBQcWeXHsMAAFkhwQCAzOAgAIAOw0q7eqZFhFX19hlePLzC\\u002fVBt\\u002fX9PnmkWCKE4gke8D8DYVrgDANhc59czOgU6EwEAyC6YnE9Ik\\u002fZooTNoc1ZnH9phrSu3gUKm+uyFP5\\u002f7Yis8MDJ9nYKf39sPRy1YesN0OOQkaN4yLpBIIDMiODMi+A7nl\\u002flNNL4e7PW5qc5tqW8v9PMaX6gUcmpoYGooPG9H3NyYXRmOa31yWY9UlhEYuCwu\\u002fmB93QP793kxmGmBPF8Wm2516vzfpcvhkAuU95gkQylk8pIYlzTUAwBwaHSRaqjsQqYhegmKjCGp0pEfkx7T\\u002f9KyrVw0tBkvCZAwgAEA+Oqe3R3f1EvLt0Q+RiZZ+xVwWHxmxLkDV+AoDpGEe+WlJjiEQ6aQY9OsVbjbTKZXFTXZfE1jBXw\\u002fDOLSBu4FR8t3fnSYYPcbZKJy+hJmxIg0drfeo319OHS6TK2u5w8AAEq7ugEAwZ7cIK4HACDWz9eDQZeq1KVd3dMiwgx7q3Lo9Ghfk\\u002fqpL\\u002fIv\\u002fu\\u002fUOcNjbxZzXnRUciAv1JPryWRy6DQmjfZjYdEfpXDPuklIoyZa6c9gjVmXIQMaze2m+uhMZERyfYX7eIeOlo0PLD7yewGRUugz+4r\\u002f+T72mq1z4QFHqVWat+77gUh9PQBg6qLUmBTbrR0dg+YtCIIgyHjkdrPwcWHa119CEZFKea7VfLs0vNFJuLeIhPinmYGBHLqrOlRw6DR8wl0+uXdRGy8kDlW4F\\u002fCPlIsKyCTKbP8VWT5zAhmhNDJDrVf2KNuLBWfO8w+VifIL+Edm+C+D3+kM8RnWas8BAMJ+iaBP7BPgCQ\\u002fgWN8xNTo5hMVhwFGcpOwoEplkpUa+6nLzujvgIJ5EKO\\u002frtFanD1xW4V51uenT5\\u002f+w2dAGmQzU6MJ1IiKTSCmBAZfaOhr4AwAAQw17dsjgTtdkEikjKOh8S2tZdw8AwNBtJjWQh8\\u002f2Vff1bzt93vD41tzsp+bPZg5bhcpluGm59CjTqom2T6VSbfQZczUqfYy\\u002fADx0JoKMQkuZ8QsdLQYhUQGLr5tKsIv6Fy\\u002fvYjBpyzbPgAfsp1Zq3rjnu6rL1kpVjEgk0o2PuvAiF81bEARBkPEIvpRCJoABhUlfl1BPaynIETLszGak0hG9BEXGkEzlyH2RiwPHAQAbwu7O811oDNLJzHB2XDg7LpgV+Ufb5xcHjrso4Z6QaTsN3VTZ6TPP4tGu1+lripvhKE7KlGg4ZIrNZYbHBbbWdsMDV9mswGq01U+GSqU4vT5IKpJ\\u002f8\\u002fqewzvzMczirQJkUlGr0IXrxJQWFHipraO+fwAAUNnTBwDICQ0xjmaGBJ1vaS3v7gUANPIHgKGxO86+ympDF5rUQN4Li+ebTQSKTXeImbSIV7hTbG3s4WpjnvE3QGcihDh0tEBufHT58V2XtAS2ScT02AdP7Wiu6dr67Bo6w\\u002fHlNZ3Nfe888GPtFdvFZAZLb5huc4nnSKB5C3EdXYKzBQ019d0NLX0SiVIqV6nVWjqd6sFm+Pt5hAX7xET5pyWFpiQE0x1tgnfqfO13O851dYt4AdwtG6atWJQGvwIZLWfy655\\u002f82985MTuJyiu2UphfFm68QMl7kbds4+sQAcqMiYc\\u002fJyd5B6fMQsOuRO5xmRS4sN04YZdMtOSdgbFLa7rEOskDrWU6VV1MCmsXJ\\u002f58AAAAIApvgv2df7Uq7KRUHaYhxc7ONK\\u002fq6UfHsBpqu7MmZcER69qqOhQyq39w1OmxMChYZJzo60k3HvbB6xX2TdW2ugnE5Uc4twe0PVlbW\\u002fc8113K6E96JFJAl24TlSGNu5dYrFIqWwTCoFpwt1Q7d4qFPbL5F1iCRjWwL1NNLgt3pSwULPZdgBAYbuND7FJQquxnfkyGPN895hn\\u002fAE6EyH2QEfLcIHhvlseX\\u002fHju\\u002fvgAQv+\\u002fubkxaMVtzy1au6abHvbFskkir++OvHXl8etz5nxvP25dzy\\u002fFo46FZq3ENHU0v\\u002fJdycKS5qH36hSKjVKpaZ\\u002fQFpd1w1OAwDA1s0zt252JKHR0sZ\\u002f+d1\\u002f9HoMANDaPvDWhwciw\\u002f1SEgaX0yEIgiB4zszsTB4PTp0Gh9wJhUTCXwi6dDdXgWmxm+t61yBO5NimqVQSlUXxsNSinQRILIqHXDeYr3GF+MwI6wn35mprbdwrC631kwEEKtwBAEk5Uda3rqq+3DxjeQYcvcrmymibnXPscvyvSx8+tXMUrlJodCqLw2Cy6Qw2ncVmMNkMFofBYNOZg0\\u002fpTA7jl\\u002ff267S2O5Aio2HYlRgyMRgS6DoMO9nQhAHApFJTAgOMo5khQYYHJxqaDIcAlHA3NpDhy+X4uNHu8kq0maoBmXD5mGHRwBhy3WbmBKEzEUIcOlos2fjA4kvHKyoLCTV4AQB0Nve9\\u002fcAP376xZ9GGvLyFKYnZURSqtU8tpVx95Vzt+UOlp\\u002f8pIp5qBwBQqORnPruN6+3i3aHH+HN0HDh2pvrtDw+oCK++mpZru8zIrLMF9YZsu9GZ\\u002fDqUcEcQBDELJdwnIDaNpsa1doFy4k4kUalUWpPzepCHB\\u002f4p4p4c2zQ1lBXTIq\\u002fRYVoKycznhkavFmsGIjmJ8IDzJGREnP6nCI7itNR0wSEc61cpfkFevDBfODpMUk4UHDJVVWQ14W5rx9SELKctyD28M\\u002f+D\\u002f+yw0nGeODKFzAv1CYkKCIkOCAjx9gnw9A309PbjcjxZbC6Tw2VZv4oz+PX9gzrgLheukxyd6fgyc8SdGfdNPVLXAABIDwqk4jZy92Ozw7y82kWi4\\u002fUNAABPBiPCx9s4CgBIDwrcVVYBADhW31jXz4\\u002f39zMO6THsj9LyV4+cGHr15EYj3BhdR7gWfkJCZyKEOHS0WEGmkJ\\u002f66Jb7l76tkNpRNNPXKdj58eGdHx+m0amRicEhUf5+Qd4cLpPOpGEYplZpJQIZv1vUWt\\u002fd3tDr2Hf+zhfWZc6Mh6POhuYt1lXVdr35\\u002fn4NgaZDBl6erKT4wXvw9uILZFBEIDR\\u002fkx5BEAQxkzhDxrsADkeIS7L3SKW4QWcq7ISzh+Fe3lAEcTd6DHNsb9v5vLXfNr5xvv\\u002fQnIBV8BgAp\\u002fr+0WKaeQFr4AHnic+0Uf3dUtuN6TFLi2dtNXAnVOgRGR\\u002fE5jLlEos3say0cddqde31PXDUVEKG7Vb1RBz\\u002f69IHT+0YSfNTnwDPzFnxaVNjY9PCopNDGCy0eGXiYBC+cF2wbkpSbhQcdVfObcc0HpEAMOybery+EQCQEzbUT8YgKySoXSQ61dgMAEgNMtkxFQBwTWrSB2fPCxVKhUaz9oftKxLjY\\u002fx8KWRyh0h8pqm5XSTm0On3z5z2wZnBjVWH65fJhQqFRKWWqFQSlUqiUis0g3flD9XWtwiEHgw6l8HwoNO5DEawJxd\\u002fP2Dkbx9NxA82Im2XJyp0JkKIQ0eLTUERfi9\\u002fe9dLt3zpwAoAjVpbX9ZWX9YGD4zM5keWrbtzPhx1ATRvse6djw8Nz7anJoYkJwT5+nAYdJpUppJIlQKhrK6pt71DMDUn2uEV8H6+HCjC8+dCEQRBEMTAOZ\\u002fyiFuJ9vap4w+1Pizp7lLrdHQXdFe\\u002f0A7P29J4PCiCuBuZSu3Y5UwCN3N92F1\\u002fd3zToWhM85rqSw+kkmhqvbJP1VUqulAhurg0aFMgM7xP1YmZrvzkMZyzC2h8egSJRLJyMaZWajpb+kOjh1ooGEmE8p62ATiKk5Jnu58MAIBEJiVmRRafqYEHrqova7OU9G+v77GeeaEzaJGJTliS2VjR8eFTO618o6wIiQqYe0323DU50clwqg6ZMIhXiiXlRl2zdS4cRdxYelDQpbYOwyq3nFD48yQrJHhfVY1hFOonAwDwZDA+v\\u002faae3f9I1Iq1Trdnspq\\u002fGgQ1+OjtasSA\\u002fw\\u002fOnvBUpuUe\\u002f7ac6XT\\u002fC4XXxcUQpGdWzZOCTM5O4zw7aOJeJZEq7b2sT8KHMjNOQU6EyHEoaOFoMxZCS9+e+erW78ivo2E69z46PKbn1wJR10DzVusuHylpbGlDx+JCPN97elrYiLNXBABQz\\u002f3EZwX5kyL\\u002f\\u002frnM8auMjQaZfHcZNOXIAiCDPnyx9NypRoAsGntlJAgb3h4oiN6wYCMI5lBQYcb6o1PlVptQXv7nMhI3EucQK3T7auB0465IRN\\u002fsjveOdZPBgDwUe3TvaoOPaYvEpwuEpyGhwE43P3b4e7f4CgA72b+AYccwvJghMYEtDf0wgM4LTVdZhPudVda4ZApghXuAIDk3GgrCXeFTNVS1x1lLm\\u002feZLXFPAAgJjWUyBpn6xRS1f\\u002fd9Y0DGZbMmfEb7l00ZUEyydGaF2S8IH7hKpdaXMyBuKe0oKHb3tnDzshZV9u4A3MJdwBAXljogTtu+fFy8amGphahSKvXezOZUT7eC+JitmRnGLZpSfD3q+6ztp3GZEBnEP0lkkkUcGh0qRQOnvRHAp2JEOLQ0WKXKfOTX\\u002fr2rnce+HEMP1voDNrD72xadN1UeMBl0LzFipPna\\u002fFP6XTquy9tsJLVYjJpTMLfz+EM2fxvfz3X1S0KD\\u002fW577Z5EQR6ciIIMjl1dgu37yowPF40J8nKR9NEhRLuE9D0MLjzxheFl5yecN9ZXtZt2qzGh8XKDYYv7xGdHsMwjEp4gzVXczjh3q6wsenoKIjPiLCecG+u7pxproV6bam1hDudSYtJJVopabONe01Rs9mEe0u1tRbzAICETCf0k9n+wYHu1qEFLkQERfjd++qGaUvS4AFkgvL0gZcDW6KU2dErFnEH16QkXZOSBEevygoJrn\\u002f6MThqiufBeWre7KfmzYYHrtp3+81w6KpdN2+GQ\\u002fYY4dtHE5fwL5FUNMbNbVUKu\\u002fOYI4fORAhx6GixV97ClE8O\\u002feet+76vtVVN4grhcYFPfXRLfAZ8selSaN5iRWWNSUHPnGlxrk5pzZ2RMHdGAhxFEAQZprCkBQ5NMijhPlI6vf5Afd3plubiri6+Qi5Rq7l0uj+bnREYNDsiclV8As0FvVysywoODvX07BCLjZELba2HG+qXxsbhXjUibWLRh\\u002fkXoOCKuHjK2DVUdUN\\u002fXar47lRh24BQq9PHBfrdv3j6sgxrs5Mvjg3e\\u002fXOpHpGDPf1fTv0ODo26hMyIE7vhxgJ4LbXm2xFYr3BPzIqkUon+ntpOuJe0LNs8A45a\\u002ftqMRp5w72js3fPNKThq1awVmY9v28LmMuEBZOIKCPGBQxYI+iVwCEEQALz9iW4RLxWOWRWqgUI+2ukndCZCiENHi2OCIvze+\\u002fuxn\\u002f+3\\u002f+9vTjqwOMAxVBpl44NLNj+8jEojOmd2FjRvsaKpxWTNWbaTtoNCEAQZuUslzXBokkEJ9xE51tj4ysnjHZKh1DYAQKhUCpXK+oGBv6oq3zpz+rk5c9cmjWprMxIAm9LSt50\\u002fhw8+cejgHxs3Jfmb6bZhL4lKdefffwsU8DXkrdnZUGQy23O58sU\\u002fDxuf1vfwH9\\u002f+7\\u002fcc1tRYiyUhHx+2uBOdO+BQx35LnHhbk0hLu5LWlcL7DeClTCHUwN2A680OjeF1NFostK8pNn8jt6XGZoX7SJeh\\u002fPbJEett4iErtsx86O1Nk2otNgLsuXDt7xTCIQRBAPAmvEecXKrUqLXEe747nYjv4F12h6EzEUIcOlocRqVRtj675prb5\\u002f728eED28+7tKs7jU5dumn6xgeX8EKJzh+cC81bLJEr1FqdHh8JCyb6vUIQBHEpPYYVWW0zMBmgemTHfXwx\\u002f669f0PZdkifXPbYoQOvnz4JD7jYrZlZngwGPiJTqzf+\\u002ftuh+jp80AFlPT1rd\\u002fxaNwCv\\u002fVweFx\\u002fv6wcFJ7NfzhXDIQA+O5oPhxB7xKaFka0252lv7B2+6ZaIL+3rFEBBvJQ8og3cDawXuTfXdA3vmatWabrb4N8aPBaHERY7oj2HJUL5qT1FcNSyGcszRvOqVa83uR5AxlBAiDccssD6Lw6CTFrefkQT7hiGjeHvkUKmGn4+cil0JkKIQ0fLyPkFem16aGlyrh2FI3YJjQ645alV3194+cG3No5Vth2geYtlsmEtdNhsOhRBEAQZEzV13ZLJt68GZMwqbsa73yvK378wWI8cyvVcHh+fFsDjcTgsGk2h0fTIZOW9vYfq6wzp+O+Ki4I9uHfk5Jr8Ea7EZTCemjXnxeNH8UGpWn3fvr2LY2IfmDotM2ho5zSCWkWin0qKf7pSoh02AWVSqc\\u002fNnQcFJ7mWfiEcAqCq02JZNEIEk00Pjw1sqbVYKq5Wavo6BDzT3Xus97gkkUj2Xqgk50Yd+\\u002fMiHL1Kr9M3VHRAVfPtDb2YHr4TgBeXHk4ij+ga8tiui8SXFQeE+Dzx\\u002fpZRu2rVanQ6Lfy5gYwV6BfEiu5Wvk6rH\\u002flevggywfgFecEhy3rbBSFRTlhf6IDRL29HZyKEOHS0jNzhnflfvbrbuRuoUqjkxKzInLlJUxamJGaNdPGlU6B5iyVqjRaKoP6uCIK4iUuTvoE7QAl3xwgUijfOnAIAUEikZ+fMuzUrmzJs\\u002frcuKfnZOXN\\u002fLCl+68wpHYa9n3\\u002f+mqSkADbRLV9G7saMjMMNdWda4KP8aGPD0caGeD+\\u002f+VHR08PCorx9wr0sXjfKNZqqvr7Czo4LbW1nW1v0w2qHDZ6fOz\\u002fM0xOOTm5sOk2mgsvKmDTHN4V3E43SyhZ5rVInM38oALAyeAsccqr4zHArCXcAQHtDLzQvr7O6lCksjsf1ZsNRq5JzbCTo68vahifc8U+HG3kD94tHK+CQZXc8v5bDZcFRl1E5ulUv4goxKUS3CNaote2NPZEJZjYBRpDJjM1l+gRwBX2EmgX3WF3e5FI97QNwyMXQmQghDh0tI6FWaj565jcr9R8GHC5r9uqsvg5Bf7dQIpApZCq1SqvX6SlUMp1BozGobA+mL8\\u002fTL8jbP9g7MiEoJjU0MjGYznCvCxY0b0EQBBl3Cid9A3eAEu6O2VleJlGpAADPzJ57e3YOPHwVhUS6PTuHRAL\\u002fd+qkXKP5rbz8wanT4Be5DAmAj1euvnbnr40CM2vr6vj8Oj7\\u002f68uFAIDhdwv+qKzYV1szoFDINbYLTzakpGzJyICjk15KWOCpqkYomBo6op4hY0uH6X5qfrdKbGPxr8sT7hkRR\\u002f+wdnXR1tCTMy8JH7HVwN2+fjIAgKikYCabrpRbvBirL4P\\u002fRkvN5Y1GmHBXKdTlBQ1w1ILQGN7ca7LhqCsJiaWlkNHB8WQFR\\u002fp3me6yZUljRQe6cEWQ4UJjeAQT7s22NvBwna7mPjjkSuhMhBCHjpaREPGlL9z0+fDZJmT2qqz7X7\\u002fOJ2DcF0WheYtZarW2b9SXMY0JvR67WNx8\\u002fmJ9bUNPZ49IJlcBDLBY9MAAbniob1pSSE5GREykgyvJquu7L1xqKK\\u002fubOsQCMVytUpLZ1B9vTnhIT7pyaFzpsdHR\\u002frD73Fj+GVAarX2bEH9xeLm+qbe7l6RXK6mUilcLtPHi52cEJyZEjZrWiyLOW56EGl1+nMF9ecvNdTU9\\u002fT0iRRKDZ1O9fPxiI3yn5IVtWhOEtdjaDNtGpWiBLYTWXZx0XHY3Ma\\u002fUt5W19Tb0NTHF8gkUqVCqWYxaWwW3cODGRXuFxfNy0wNy0gJg985AqNwbPT0iSuqO+Ho5IMS7o443twIAPBlsW7Lsj35uyUz+\\u002fNLF\\u002fvl8pPNTaOZcAcAeDIYv2y4\\u002fua\\u002f\\u002fmwYsFbipBtWt86Xy6GIJYtjYt9evBSOIgA8tnz2pYY2uXrog57DoD+2Yg7uJeNMAf+IIdsezo7zZwQVC84GMIIDmRESrbBNXo9h+pXBN8VzXX7rxWZium1YarvOaksZu3ZMNSBTyAmZkaUXLO6IMPwSyGaFe7ytf5d1daVtGjW8qtSSFVtmjtqibIPeDjO3\\u002fZAxFJ8RTvDCtbqoecH6KXAUQSa9sNhAgunCpsoOODRaOppGNeGOzkQIcehocZigT\\u002fzspk+tL\\u002fek0an3v37d8htnwgPj1mSetwwIZNX13f0D0n6+tI8v6edL+wekfXyp2FwroTse\\u002fREOmbpmeeaT99u+eFertYuvex+OmvPtB7fGx4yopKzwSsvjL\\u002f5ufLrzq7tCgryNT0+eq\\u002fnyx9Md3UJjxEAjUYglirrG3uNnqgEA8TG8bz+4FXqNdacv1H6\\u002f43zDsDvTSqWms1vY2S0sKGr6ZvvZtKTQu26ek50eDr3MDdFoFDKZBADQ6fS79hX99Hs+dJBodXqlStPXL6lt6NlzoMSTy9ywOvem66bRaBT8yyB7D5X+99NDxqdkEumPb+8JILx7PF5zG\\u002f+WB77DR156cvXiucn4iFkHjpV\\u002f\\u002ffOZ\\u002fgGTO0xKpaajS9DRJTh9oe7Tb09ctyb31k0zmEwaAIDDZtjbPXyUj8PmNv7+o2Vn8us7usycbmRytUyu7uNLm1r6T5ytAQCEBnlfuzpnw+ocw4\\u002fYpjP5dc+\\u002f+bfx6Z6fH\\u002fDxYgNnHxtGWp2+tZ1f39RX39Rr+L9QBGcUH3xmBxQxa\\u002f+Ohz04JrtRjmso4e6IFqEQAJAXEkqkSxqFRMoLDTtQV9siEsJjrhfk4fHb9Zse2v\\u002fvhTZrOUfHbExNe2PRYiLfhEkoPsjv36du+y2\\u002ftKlPAACIDvC5YUZmANeOnkLvbl4Ry3P+PrT1Pfyndx6AowSUCM8BAFYG3zSftxYAUCrMT+RmXxO6FQDQr+r6tvGNcnHBrIAV0LucLiY1lEIlW2msCaW2BX1ifo8IH4E4UOEOAEjKibKScG+t69aotTT60AdseyN8GwDPw4sdPLLqieG3GayYviQNDrnY6Lc1QKyLSw8\\u002fvbcYjppTdLoaDiEIAkB4XCAcsqCpaswKfFpGt7genYkQ4tDR4hi1UvPizV9Yz7azucxXvr87fXocPDCeTeZ5y8nzNR98eQyOTlw9fRJDolOn07\\u002f3+ZF9h0vhV5gTHOgNhywTiRVvfrD\\u002fQiG8GN2s8uqOR57fec3yzEfuWkQw+ThWOGwGAEAsUTz\\u002f5t9XKtrh4WHEEuX3O84VXG58\\u002fbl1\\u002fr4e8PBVS+Ylf\\u002fb9CdnVtd16DPv3aNltNzhyP+\\u002fQCZNOYlwP5tzp8fjIcCq19vVt\\u002f546XwsPmFKptdt3FZy9WP\\u002fW8+vDQny8PJndvdYyADa57jgsKm395Y\\u002f8wist8IBVHd3Cj785fvR01evPrHXgbkdNXff0KTFOPzYMHnp2R2VNl0argwcQlHB3jFCpBAD4sIh2EvRmMAEAIqV9N9mcxZfF+unaDZ9fuvjpxQKVlmgtiXUcOv3FefM3po729Hd84Xl6PLTUkVORQWZEcJivxfb6DmPQHPyt71V1UEm0OQGrDE9pZJparzI89mcEbwi\\u002f56uG18707VvAWz\\u002f0HhegM2iRCcGNlgsGofoX6zumevpywmIdKcpIyomCQzg6rb6pqhNfjN\\u002feaK3C3WbZvk3EL1x9eZ6hI6tDcYD1RQbI6MuanQCHLGhv6O3tEPBCfeABBJnciH9uiwWy1truiAS7N6sfOetnQKdDZyKEOHS0OOaDJ3c0lFvLlVBplFd\\u002fuCdtWiw8MM6hecvk0dMnBgDo9diLb+85W1APD1swI49oBVVHl+CJl\\u002f\\u002fsHFaqbN0\\u002fB6+0tPHffXnDCPtsuBSHRVco1Y+\\u002f9EdtA9EPWABAZW3XEy\\u002f\\u002f8dV7NzNwtWJ4TCZt6fzU3fuH7nj9e6Tslk0zyHYuPMIwcPRUFT6yZF4K3cJfaqDR6v7z6q7iMqIfyC1t\\u002fIee3fHJ25u9PdnwmJ1cdxzWNfbYm203qqrtevLVPz9750bDzRXiquu7M9PCnH5sGDS18lG23RJr3zjEEi8mky+Xi1SD2UabhColAIDLsO+3wokoJNKDU6ddk5i47cL5f2tqhveQIY5MIq1LSn5i5qxgrt031hC7eLOJ3tGxi4ejswSlTu5F86WQBj80GGSWQiczjsZ5pHtQvUoE51ydcAcAxGdGWEm493UKtFodlTpYgNBQZu2yJDk3Gg4Rk5wbBYdM1Ze1GdMxA71ihdTax0V8xkhXKfIJzxonQ9IHsSkuPdyX5znQK4YHzDn7b8m1dy+AowgyuSVkRVBpFK2G0AXGlfN1o\\u002f\\u002fZ29s+IBrd9r7oTIQQh44WB1w4VHbi70I4auqul9ZPvGw7QPOWUUehkFcsShOJFSKxQiRRiMQKqUw5ghSCHXr7JQCAD78+BmU5fbw5IYFeXp4sCoUsEiua2\\u002fjGnhgkEpiRazvRCQDgC2SPvvC7IZeKFxnul5ka5u\\u002frwWbRxRJFZ7eo8EoL1BPjSkX7a\\u002f\\u002fb99YL1+KDboXBoL3\\u002fxVF8RpVKIWelhUdH+vt6c8hkkkAkr6ztqqju1OlMFos3tfR\\u002f+u2Jx+9bgg\\u002firV2RiU+49\\u002fSJLxU3T8ux7zq6pLwN+s6vWpKOfzrch18dG55tp1EpU3Oio8L9vL3YUpmyny+9VNJsOGwAAHyB7JnXd3t7jjSR4rrjcM2yzB92XpDJTZIDbBY9LpoXE+nv7cX24DCUKi1\\u002fQFpW1VHfBBftNbX0f\\u002fb9yaceWAbFraus6XTRsYFYhxLujojw9OLL5Zc7O\\u002fQYZvPOnh7DCjs6AABRXt7w2OiK8PL+YPnKx2fM+qOifE91VbsYPtNYF8DhrE1K2pKeGentDY8hzkalkB3OjFvHZTp444dGosu0Q8cMm+rJV3fjxoEvndethM+IrhCfEX5oxwU4ehWmx3raBkKjB3csabK6WUcqgbvQZnn7c4Mi\\u002fLpb+fDAVfhbAh1Wy9sBAAlZkXDITgqZtYQ+XlC48\\u002fsUWScekFm5QYKMCRKJNHVR6kHLv0d4x\\u002f68iC5cEQRCZ9Di0sOri5rhAXOKz9asuW20N3G5ct5i3zMXQWcihDh0tNhLq9V9+couOGoqPiN8za2j\\u002fVEzOibzvGXRnOT05DA4CgAAoLdf8uzrf+Ejrzy1JjzUFx+BEExEUijkZx9ZgY\\u002fo9ZhIoqip7\\u002fnPq3\\u002fi407X2yc+cbZm97+D6V0Om3H9NbkL5yRFDfso6OwWFlxuOnq6SqvT+\\u002foQatz69ocHoJzv9Nzou2+ZGxcNL6PRY9jhExVf\\u002fHh6QDBUYXbuYsOfe4uuW5ODe6EbaWzpa2wZbElPp1NvvHbqpnV5HDacUujoFn741bF80446+w6X3rxxRoCf+eYhMZEBaUmh5dVDH4x7D5Xam3A\\u002ffNKkn0xCbKD17v8V1Z17D12BgmuWZdxzyzxP7tD+qAbFZa3vf3G0uY0PAGhp4ztYQI7juuOQzaKvW5G1fVcBACA81GfejMRZ02JTEkLMphWr67q3fX6kut4k63LgaPnN108P4tnRCyH\\u002fcpPxsXOPDQDAx2\\u002feoDVN0wMAhCL5Ey\\u002f\\u002fgY88+8iK4b9ow7FZ8Fc1rqGEuyPmREYWd3f1ymQ7y8tuTM+Ah01tL73SJ5cBABZEO5jac64IL68nZs56YuashoGBC21t5b09Nfz+DrF4QKHQm9625tDpIR7ceD+\\u002ftMDAGWHhGUFB5j4EEJdwUXk7AIBJo1LIZJ0e\\u002fky0yZ8R1KFoEmr43jQ\\u002fAEAgI\\u002fSK8LxYI\\u002fCkDa7ZlGiFZBLZ5D2uYXMhf1dL\\u002f1DC3WrzXIcr3AEAyTnRVhLu+L+309a2dSOvcCd+4crCbeA+OvIPl+mHnYPdH4VK9GBWKQZ7Go4v05akEbxwbazsqCttG\\u002flRiiATTGpeDMGE++WTVQqZijW6e0AVnjRZuD0K0JkIIQ4dLfY6f6C0p20AjpracM8iErH99MajSTtv8fJkeVnIkrNZNCgSHuprPYnpMDKZ5OPFDgm0I8fnmJKKthPnagyPZ+bFPv\\u002fYSq6FD4GQIO\\u002f1q7LXr8pWKjXwmDl\\u002fHygpKBpKOwIAbt00444ts\\u002fERIzKJtHxhWlZa+KMv\\u002fI7vP\\u002fP5DydnT4u1K9c5+jy5zPf\\u002fb5OlIyE0yPvtF6999d29xu8zAECr0+85WHKnhe8GAGDtikx8wv38xXqBUObjbTu\\u002fbKBWa0+eM+nDbr28XY9h2744Ai2qePiuhdetyTUJXZWdHvHtB7f+57Vdlx3t1gJx3XEIALjumtyePvGaZRnZ6TbSGknxQZ+8vfnxl\\u002f4orRxata\\u002fV6Y+frbnx2qm4FxLlimMj2txedH1X1xwYhQZ7W\\u002fp7JzCiCQUEb2NqOp1CAQC8cvL4b+VllhZXYQBsL73y2umTAAAOnX5DmrXPlNEX6+t7U2bm20uW7r7hxot331vz0CNF995\\u002f4a67z91xV+E995U\\u002f8FDZ\\u002fQ8euuXWT1atvndKXibKto8uH475D3Sn4DpUOx\\u002fNSQYAlAnzDU\\u002fjuBkYwHa0fijU8HWY7mz\\u002ffoG6z48ebPIe14hODqVa3bKmq3mwjbtaqekctvW8EZVKSciycZKzIslqV5mW6qH9rDqvfj1m+QRwA0IGb1o4DCO8yNP6t84VTv1TBIfGAwbhXxOpaHAh4fiSOz\\u002fZ05foLPn3T47AIcQ1Zvz8ZdTn\\u002f5NpiE7ZkTGUOz8ZDlmgVmryj5TBUVfSanVFp6rhqIuhMxFCHDpa7HVop41cM4VKnro4FY5OIGjeMkm0tg9IpEoAwPKFaW++sN5SlhOPyYRvPAwnk6u++OEUPrJ8YaqlbLtREM\\u002frpSdX41saaDS63\\u002ffY6Ow0tsgk0jsvbbCe2SSTSE89uAz63hYWN+OfQhbMSvTkDt340er0+4+V48ZtOHepAd9EhUGnLpmXghuHXbzcVGe6RnzJ\\u002fBRL2XYDGo3y5vPrAgM84QGHuOg4NPDz4bz05Gqb2XYDOp364hOrqBSTzG1JeRv+KUEuOjYQK1DC3REhXO79edMAAFq9\\u002ftljR5b9\\u002fMP7F84faqgv7u6q6u8r7u46VF\\u002f3v\\u002fPnFv\\u002f0\\u002fYsnjhlKiZ+eNcefPdKtG1yKQiZ7M5mBHI9gLteXxWLTiH5eIK7gugp3AICHQ11lsn3mAAA6FYN1AVneszyoXg3Sijcr732udPM\\u002fHd8DAKb6LcK\\u002fxUWoNEp0cggcxTHum9pc04XpLV7RxaaH0RmOH+fW902VSRS97YOFSNYr3OMzCJ1rrSOeHZaJRzU73FbXU3xm6A75OEJjEF0BJjXt8Dhe0OjUZTfMgKMWnDtwpblm6B4S4iKNwoEuKVwPgritjBnxHl5Ep3ZHfiuAQ6508WjF6H80oTMRQhw6Wuyi1erKCxrgqKmwmMBRXkYzytC8ZVKJiQx48v4lNpv3Erf\\u002faLkctybVg8N45O7FuHGLUhKCF8xOxEf2HSkzpGLd0zXLM1MTrV0pG3hwGMsXmtyiq2vs1VjemYZOp65YZPL6fYfLCN85BYdPmPSTmTczwcPq59Xhk5X4p1QK+e6bbffLYjHpt22eCUdHwOnHoWMCAzznTI\\u002fHR+ptdaw1y0XHBmIFSrg76KFp069LGTwK6wcGPr6Yf9++fzb8tmPV9p83\\u002fLbjvn\\u002f3fnapoEkgMLzg\\u002frxpN2VkDr0ZQWzx4bgw4e5YG\\u002fdwdtyD8W9uinjQ8JROZtwS9aQH1QsAgAEMAJDtM2ea72gk3AEACZmRcAjHmHBvstqCM2XKiLo8xaSG0q3exza2j++82k3PLJsdcohgDFtSaolEOKopmD+\\u002fOEa8is2tEM+j8btFcGicWHnTLBKxGSSGYV+86NqWnQgA4Ey7c5bBIqODQiVPI1xPWnymptnqniLOdfi3weVoowmdiRDi0NFil6bKTrWtZgVcH6LzlvELzVsmjwdun0+nE619sQnDAH7PTwDA2hVZwxtYW7JiURr+qVKpKSxx0wkbiQS2XDcNjlqQa3pBrdHqunqsXdRcsywL\\u002f7SjS1BSTmj\\u002fNrFEUYDrIQ4AWLUkA\\u002f8UolRqoK1K589OJFi6vmxBqhObgDv3OBwJ6IclurpTK3EuPTYQS1DC3UEkAN5dsuy1BYs8GdZylzwO59OVq5+cOQseQBCrXJpwd3g71gi2yZ3VKE7Ss8mf3hHz\\u002fM1RTzyV9OHmiIfJpFFa9mu9LaOxtXozrq\\u002fLcCl50XDIHlQqJS7d2pdhbONuvAFgVrwzEu5evha3MYEIh\\u002fVTc53Gio4jf4xqUacT+QZ6Eryoayhv147Pe\\u002f5BEX7Ee2JcOV83Jim8SeVsWzMcQtzbrBV2lFP88dkxOOQabfU9F4+ZlJKNDnQmQohDR4tdBnptZzpG8xs1VtC8ZZKIDPfLy46CoyNQ29DT3jlYCmmwcHYS\\u002fql1mWnhZNPdEa5UDDXUdivpyWEEE9MAgJjIwT3PjKxX7oeH+kBdUPYeLsU\\u002fteTY6Wr8ppqhQd5ZadYuoitqOpUqk1uMs6bG4Z9aQaWQp2RFwVGHOP04HIlI081aNRod9C2yyaXHBmIJSriPyE0ZmefvuPv\\u002fFixaFhsXwuUyKFQAAItKDff0WpOQ+N6y5We23rkiPgF+G4LY4tKEu2MV7mbRyIxEbla61\\u002fQAhu3VSU5kPUnd1zE4o2qqcmGFOwAg2Xob95ouAICIL1VIhzrWDeeUCndemC8csqC+rE2nHZrxuI5ep\\u002f\\u002f0+d+ttPRxc1QqxdufUDpArdLUlznSR88d3PT4Cjhk2Rcv72qr64GjiJPINZoLneP1QJq08han+hC+ejmxu7CulFAt2Ajt+PDQmHz2ojMRQhw6Wuwil9jOdHS19I\\u002ffJXfEoXnLZDA9d6TXaBD8bp8AAC9PVlw0Dx+xjkGnhoeafGSVVblpwh0qTLZueF9ym0nVdSuy8E9Pna8VE\\u002fh0gvrDrFySbr2oqb4J7peSl2XHvyvbakkccU4\\u002fDkeC6wEncLR2nhldfWwgZrnF+ohxjU2jbcnI3HK1Y4wOwyjWPz8QxIKiNx4yPqaSXVgqzmXBn9fjTmRCMJ1Js7S6ViZRyCQKDpdlbOoyXFCEny+PaJbEEutt3FvruoGt8vaAEB9vfy4ctV9QhMlNbysUMlXtlZbk3BFV9xPxy7YDlYUmiwfHndAYnqCPUL1Y4ckq6weD20rMjpy9KuvsvyXwgDkKqerV27\\u002f6YN8TxPvtINaV9fWcbW+u6O+r6u9tEgn0V\\u002fsYpH7zoekLAQDgvuypT0+fC0ev6pSKf6sqO93W3CoWiVRKbwYzystnYWTMDSkZvkyTO7h6DEv\\u002f9iOZRpPJC9qz4Sb80OXuzg27fzU8\\u002fm7ltQsjTa40btn35+m2ZiaVWnHnI9BUR6XT7qqpPNpcX8Xv4yvkDAo1xIM7KyxiU3JGoq8\\u002f\\u002fpWQjy\\u002fnv3fxLADg0q33BbA5AIBqfv+vlVcudLR2ySQYhgWwOUl+AfPCozcmp7vh\\u002fIpKpazYMvPXDw7CA+ZgGPb5i7ve2\\u002f0oybRQzrkqC5tO7rkMR0cFOhMhxKGjxS4MAh0SdFr9L9sOPPLuDfDAxILmLZNButXNuhxQYXpJGBPpb++Ewteb09I2uIQaANDTJ8YNupHYKLgw2QrmsA2rNFoba3bnzIj38eYIhDLDU41Gd\\u002fhkhfW9TDu6BBU1Q99\\u002fMpm0fKFJi57hoO1SfX04+P1abQoL8YFDDnH6cTgSVMpIs0OuPjYQs1CFu5O54dUgMl4wqFTjfxRXXo073FLGOj2m02LmM+BOR6GSY1JC4ShOX4eA3yMSDwzOBoZzypWb9T+kvaEXw7CuVmsJd6eUtwMA4qyuy4OMQquBcweu7Pz4MBwdb2JTw+CQBf\\u002f+dFaj1sLRceK2Z9ZQqEQnAx1Nfc9t\\u002fnSUO+pOYD+UFb2Tf2ZffXWDcMCYbbcXBsCHhRfm\\u002f\\u002frth4UXinu6+Aq5Vq\\u002fvV8gLuzveLTgzb\\u002fs3v1WV4V9PJpFS\\u002fHkAgNoBPvSXXu4eKgEr7oFvWFbx+wAAKX48aKpzvqN14Y7vnjt1+HhLY5dUotbpJGpVzUD\\u002fd6VFK37\\u002f8f\\u002fOn9AR+Kf1yKQAgA8Kz6\\u002f848efyovrBHypWi3TaJpFwoONdZ8WFbjt\\u002fGrlTbOI\\u002fwZVXW7a8dEhOOo8apXmgyd\\u002fHasSXXQmQohDR4tdPH04cMicg7+e377twFh9AowaNG+Z8EKCvOHQyNQ0dOOfhgR6458SAe3wKZWqHJ62uVQk4cVDjqFSyKsWp+Mjew+V4p8OB5W3T8uJDvCzsYa4zbT\\u002fj70JdHtfb4nTj8Ox5epjAzELvnGBIMiEF8vzy4gIgqMjdqh754nev9\\u002fN\\u002fAMecI2EzIjqomY4elVP+0C\\u002f1XW1qXlOWCPmF+gVEOLTZzonMFIrNb3tgu6WoWqI4az3xiEuLI7H4bJkxLZP2fvDmevuW8Sxp1LALkWnq9++\\u002f4cJcL1nfasAPGG\\u002f5NifF5ffOBMeGA9CowPW3j7vr69OwAMW1JW2PX39R6\\u002f8cA8v1DnTWcfIJcoTuwsDI\\u002fymEG7n6obWxSen+g8ualZqte8WnDE8fmb6XPqwSpYMnvnP7adOHPyzutzwmMfmTAkO9WIwBxSKS90dAwq5RK16+uShHrn04dwZxrekBwRe6upQaDUtYmG019DP8XL3UJK9pMdkDwy+Qt4nl4FhX8aRpvr7D+\\u002fV6HUAAA6NPjU4LMjDQ6HRlPR2NYuEegz79srlXpns4yWr8e8arlcu++DS+Q8KzwMA2DRavI8fi0rrkUnbJCKtXg\\u002fV2rsVvyCvuatzTvxdCA9YsP39g0k5UTlz7egeSxCGYe89tr2tfsz6J6AzEUIcOlrsEpUUDIcs+GXbgcKTVTc8vHTqwlSXLqYZQ2jeMuENb2cxQkKRyUfNv0fL\\u002fj1qUotgLz2GSaUqT66Tv86R83D2t264a5Zn\\u002fLqrwHi\\u002foam1v6K6MzXJYjH44RMmCXfr26UaSGUm3UsCCO\\u002f5YeDt6ZyThdOPQ0s0Wl1LG7++qa+lnS8UykUShViilCvUKrVGpdKq1Fq1WqdSj7SucRSODWQ4lHCfmAo7h4rU0niBTCr6QSNDNk3P2DTd9qnOXkrdqBaPxGdYS1X3dQiUcjUcxUmeYq04nbiknChLCXcAQFtDTzdu+eFwzqpwJ5FIadNjC44MJt2sk4kVu78+aVcTTOJO\\u002fF34\\u002fuO\\u002fjtNNRCGZsxLgkGXfvflPztwk4k1p3cqtT68uPFnVWmtS\\u002fmNFU1Xng8veefLDm6cuSoXHXAzDsMpLTUf\\u002fKDi557JSrn7wrY3wK8aVOeFRc8KjDI\\u002fFKpUx4X5zWjaHRjO+zIqfy0sM2XYqmfzanEU3JGeQr1aCa\\u002fX6H8qK3rpwSodh2y6eS\\u002fHjLY6KNQylBwwmzav5faYJ9w4AwLLo+ENNdSW93RgAxmxNJb\\u002fP8CA9IPBqDLRLRI8d32\\u002fItm9JzXx2+jwP+tAKqt21lc+cPKzSaffWV88IDb8xJdM4NNwf1eUHG2uDOB7Pz5y\\u002fIiaBSh6sXpSq1Uea6+N9iHafGBM3P7XyzL5iLbHFtnqd\\u002fv\\u002fu\\u002fOaNXx9IcdJpyADDsK9e2X36nyJ4YBShMxFCHDpa7OLhxY5MCG6pNbkPakl1UfMrt33ly\\u002fNMnxGXMiUmJMqfF+rr7c+lMagMJo1MIVob7s7QvGViYxPooUQchgGZTAVHR0yl0gD3S7izWYRmjyMRxPOamhOVf7nJGNl7uNRSwr2iurOjW2h86uPFnjl1cC5qhcz0Kp7JtO8fxWTY93pLnHscDieTq8\\u002fk1548V3uppFnj+nPQKBwbyHAoDztSOr3+QH3d6Zbm4q4uvkIuUau5dLo\\u002fm50RGDQ7InJVfAJtWJHaKNj4+2\\u002fGxzuv3zg1lGhjhJHQY1hZT8+51pYuqYQvV2j1eg6dHu7pmRQQMDM8wpvpdickxLmU+tFNuGdaqz7u7RBY2TmKzWVGW5gW2CspJ+rMvmI4elVbXXdPq7WEO\\u002fEaaptmLs8geOEKAPjto8NpU2OzZtuRULZJq9H99N9\\u002f\\u002f\\u002fjsKDwwbvkHe8ekhDZWDt2\\u002ftEIilL9+93fv\\u002ff0ojT7+Tqx0Bu2pD29+bM02ghlDAIBEKH\\u002f51i\\u002fnr8u94\\u002fm1\\u002fsHe8LCzYRhWe6X1zL7i0\\u002f8UW7nFNdnINJr\\u002fXhzM0b8+d8kNySaLfKlk8p2ZU8gk0mvnTgAAXj57bH5EtCGRbUya1\\u002fD7V8QMfg60iIX9CjkAYHNKxqGmOola1SDgx13NdFf2D\\u002fbTxFe4v1twRqpWAwBWxSa+MXeJMW6wPiFFoFQY\\u002fvYPCy9cn5RGs7w9yYHG2iCOxz\\u002fX3cxjm3RO8KDT1yek4CNuKDjSf8VNM\\u002ff+MPizsEkpV79w02dPf3zrtCU22pgSpFZqtj2x\\u002fdSescy2G6AzEUIcOlrsMvea7J\\u002f\\u002fRyjhbjDQKz61p2gkHwtkCpnOoNLoVBaH4e3P9Qnw9A\\u002f2Do8PDI8LjEsL9\\u002fQl1OXGRdC8ZWIjObWJnEzupu1fXIFOG43LkLUrsvAJ9+Nnqh+6cyGHbSY9feiESUOw5YvSqATu+cmhhLudCXQKhUylkLU6+\\u002fYUHc65xyGeRqP769\\u002fin\\u002f\\u002fIFxNb5uUUo3NsIBD0TR+RY42Nr5w83iEx2TRDqFQKlcr6gYG\\u002fqirfOnP6uTlz1yaN5cKxVqHI1Ql3HYbtLCv9uCC\\u002fVyaDxwAAAFBIpEUxsffm5WUFEV0RiYw7o1zhHhEXxGTTLZWx15e1WZndJmVHOWuZbXJuFBzCKStoaDfd9QUvJCrAiZs4zViW8cmzvxPsJK7V6l6\\u002f+9t3\\u002f3zYeit84upKWz94cgfB3PQ4MmtFJvF\\u002fVF1p61MbPnzx6zv9grzgMbcXlx5+y39WfffmP\\u002fCAVSf\\u002fvnzhUNmyG6avvWNeiD1b8RA00Csuu1B\\u002f6XhF4ckqEV8KD096u2srxCoVACDOx2+Tabbd6Nb0nG9LL3dIxB0S8cHG2tVxSQCAGG9fDo0m02gMbdkNCrs6AAABbM7ssEg6haLW6Yp7uowJ96r+XgAAm0aL9R5cxtEnl+1vqDU8\\u002fs\\u002f0OYYHkM0pGf+7eFau0fTIpGfbWxZEWOsM89KshVC2fRzZ\\u002fMjy47sKCfbHAINb+X298YHFNz62nG7nlSSkvKDh\\u002fSd+7Wwe+lGOIXQmQohDR4tdVt08+8\\u002fPjylcUKhriV6nV8rVSrlaIpT3dphMqkkkUkR8UMbMuJnLMzNmxI1J1TyatyAEqYh9yCDEzZgSG+DP7euXGJ4qVZqjpyrXrsgyeREAWp3++NkafATq\\u002f06QA4lvKo0y8oS7i\\u002fAFsqdf21XbYK0BII1K4XKZHhwGi0ljMmgsJk2l1hWXtcKvQ9weSrg77uOL+e9fOA9HTfXJZY8dOlDW2\\u002fPC3Pnw2GhpEQnhkFPx5fKtf+8u77X2kaHDsMMN9Uca6jenZ7w4bz4Dtbhxe181vAaHbOlQNMIhVyKRSbFpYRUXzf+lxWdMzu4QZ\\u002fWTAQDEpYdTaRRLK5EvHCyFQzjWi\\u002fTtxfVmz1+Xe+T3AnjAAplY8dg12+58Yd2a28xnyghqre3e\\u002fsHBM3uLsYlYPLJs84xfPzyo0xKdsdUUtzy4\\u002fN37Xtswe1XWmFx\\u002fjsT19y9uq+8hfggZqBTqf74\\u002fvfeHM5mz4mcuz5yxLH0khWMysaKpqrOpqrOmuLniUmO31QUiyJm2FsODlTEJlq5FKCTSipiEb64UAgBOtjUbEu5kEinVP\\u002fBiV3vNwFCW1tDAPdWfRyWTk\\u002f0CrvR2F\\u002fd0Xp80WIJtSM2n+vOMLWvOd7Rq9XoAQJKff6SntyEIYVFpKX68wu4OAMDFznYrCXcvBnN5TDwcHT98Arj3vHrttse3wwOWYRj22ydHTv1TtPWZNbNWZhHfA9Co6nLTzo8Oj8J+ksShMxFCHDpa7OLl57H5kWX25pddBMOwltqultquvT+c8fbnLr5+6ro75o9+tQGatyBE0Gnw6ro50+NzR9zVc9QafLshMpm0eknG9zvOGSP7jpQOT7jnFzbiK7jTk0MjiPXeZLHoStVQy3IH2pfr3DXbLhDK7n3yl54+k4JdAICPF3v6lJjs9PCocP\\u002fwUB8O22STXgBARU3nfU\\u002fZMclE3ATKezro94pyY7Y9lOu5PD4+LYDH43BYNJpCo+mRycp7ew\\u002fV1xmK378rLgr24N6Rk2vyR4yWVlcm3Ptkshv+\\u002fL1JYLGUGA8D4Ney0qr+vh\\u002fXb8C3eUXcUL10RDvJjI74jAhLCXfrnNg5l0anxqaF1RQPpr3skpAZCYdGZv1dC47+cZH4BaRaqfnshT9O7y1af9eC6UvS7EoQS0Xyi8cqDu3IL71QB48Nw2TTH373hvcf\\u002f5VgIZv78AvymrE04+z+EnjAMmG\\u002f5K37fwiK8Ft35\\u002fwpC1JCo+2on8L0mJAv4XeL+ruEfZ3Cvk5BT\\u002ftATyv\\u002f0fdujEocjeVBD797Q1+noOTsYNkycRiGlZytLTlb+9kLfwSG+8ZnRMSmhQWG+\\u002fJCfH0DPZlsOp1Jo9GoWo1Oo9ZqNTqlXCUWyMQCmXhA1tch6Gkf6Gnjdzb3W1mYggxX1jd4qzs1gGc6YiLtagOZYtyeqGkBgRe72lvFIrlGw6bRAABFPR3GF6cHBF3p7S6+um+qWqdrEA4Y4sY\\u002f4Urv4Giir7WDnMcZLFrvkMJXF3jpAYHGVP44tWTjtHP7rxQcJdoiw6C7lf\\u002fW\\u002fT\\u002f4B3sv2zwjb2FKQkaE9QVYapWm7kpb0Znq0\\u002f8UtTdYXEFlQKVRbv3P6m\\u002ff2AMPuBI6EyHEoaPFLtfdu6jkXG3RqWp4YEwJ+yV\\u002ffn7s769PLr5+6q1Pr\\u002fb258KvcCU0b0Fs4rAZZDJJj9sVOSrc79pVObiXIHZbsyzjp98vGPPaNfU9dY298TEm09HDJyvxT1ctIVrezmHTBUKZ8alCaV\\u002fCXaPRjUJLdMf899PDULbd14dz981zli1IpdhzRkPGC5Rwd4RAoXjjzCkAAIVEenbOvFuzsinDrhLXJSU\\u002fO2fujyXFb505pcOw9\\u002fPPX5OUFDAWa6VbhCadrHtk0sre3gGFUqHR+LJZ\\u002fmx2nK+fL4uFfw1xzx49QjDbblTc1XXXP3\\u002f\\u002fcu11lKtboiHuyZcemOszF45adllwakBt4+LfuRzbcZREJiXnOC3hDgBIzol2NOHuyNdvRXRyyMJrpxzbdQkesKq8oKG8oMEvyGv60vSEzIjE7MjQGB6VCheDKKSqvk5BS21XXWlb1eWmqstNBOu+SWTSM5\\u002feNm1J2sHtF4hc5bqbm55ccf5Qqd7OQonuVv4XL+0CYJdPADcpJ8o30IvrxfbwYtMYVL1er1Fp1UqNQqaSSZRSkVwilIsHpMJ+iWhAZvYv0uCqPFyKSqW8+PWd\\u002f7n+o4bydniMsJ62gZ62gbP\\u002flsADiLPxFYONvHhsD9MRE8Y+LcbXg6tt3PUYVifoz+QFS9Xq2gE+ACDVnwcAyOQF\\u002fVIBagf6Den4OgHfUMyOb+DeKx\\u002f80\\u002fbUVe2pqzLGLREqlXAIx+F5iFt55L833L\\u002fkHeHVRdbE9XcJt287sH3bAQ8vdmRiUHhckE8Al8Vh0Jk0tVKjUmqkQnlvx0BP20BrbTfxnsV3vbT+mq1z\\u002f\\u002f7mJL\\u002fHZCroUuhMhBCHjha7kMikF7++86VbvijLr4fHxppWqzu448KZfSVbn12z6pbZ8LDLoHkLYhOZTPLz4fThOvwIxaPaB3VC8vf1mJkXeyZ\\u002f6BNy76Erj9+3xPhUJledv9RgfMpm0RfOTjI+tc7DtL57QDCUfCdCKrM24RxDVyrazxaYfHoHB3p99OYNgQGe+KB5RG9MI+4FJdwdsbO8TKJSAQCemT339myLd0cpJNLt2TkkEvi\\u002fUyflGs1v5eUPTp0Gv8j1WoRCAIBYpdpRVvpbeXmzEM6Pk0mkzKCg5XHxN2Vmsexp9rKvtuZ4kyP1xQXt7R\\u002fkX3hi5ix4AHEnIayoJUEb4ahlncrmUU64x2c4krCOTgphecCrtEYiKScKfAsHbTK0xIGjI3b782vPHypVSO1u8cnvFv3709l\\u002frz5lsOgeniw2l6nT6lVKtUKmkkscnLs88Mb1ho0Bs2YnuNWFK0GRCcHLbph+YLuNBmKWCPokFw6Ng\\u002fUiRmwu850\\u002fHnrltq\\u002fKC4ZmyYgb0mGYSjdYesm0eu5mU2mGB1LN0KYXxtR5Nb8\\u002fkxdc1NNp2FIsixcMAMjkBQEAdBhW2tc9PSTc0MAdAJBxtVgeACBR2\\u002fc5o8Ospbqs7Kc6jvgEeL783V1PX\\u002f+x2tGbZFKRvOJio2OLtyBz12Rfs3UuACAuPXw0E+4AnYkQe6CjxS5MNv2NX+\\u002f\\u002f+rXdxHdpHk0yieKT534vPlPz2LYbOdxRuo2K5i2ITZFhfviEe0v7AG4QcdDa5Zn4hPvR01UP3bmQdrWBz8nztWrcCqGFc5KYzMHpqE0hwd7V9d3Gp212riPp7XfT7RMOn4R7AD736EpC2XYAJFIHz2jI2EIlxo443twIAPBlsW7LyobHhrklM9ufzQYAnGxugsdGhUil\\u002fLOyYtGP379z9szwbDsAQI9hxV1db505veiH7\\u002ffW2LFK8fviIjgEAAmAVB5vTWLSprT0+dHRYZ7mP0G+ulxob2k8Mso4VPvWhDLJTtv\\u002fk6DQmAA21+72eU5s4G5gfd9US8LjAlkcZ+b9DXx5ng+\\u002ffQMctZ9Koeb3iNrqezqb+\\u002fjdIoevWm97Zs2qmwcLnbJmJ5gOjht3PL+WR6zn4MTA4bLe2H7\\u002ftMWDzbsR90QhkVhXM+lKrbWmB3LtYPLXgzbUzC3ay4dDowMA6gV8AMCV3m4AQLAHN9iDCwCI8\\u002fEzdH4zxKsG+gAAHBo9+uqOqQCXx88ICLolLdvmf0uj443vncCScqIe37YFjo662LSwR\\u002f97o\\u002fGx6aDLoTMRQhw6WuxFo1Pvf\\u002f36t397cPR\\u002ftQk6d+DKf677yIG1Pg5D8xbEuqR4k8aM1XXdGsJrxRBL8rKjQ4K8jU+lMhU+\\u002f37EtJ\\u002fM6qUZ+KfWQa1p+vkSuWKoZMSmptZ+OOQeisva8E9jIgMyU4l+jAtEaFnGuGStJAqxxFAznhcSSqQpCoVEygsNO1BX6+rNS634z+FDcMicbqnkkQP7a\\u002fj8JwnUntfy+4u7Bvu3GuUEB\\u002f936fJoHx9jBAMgv6317bNnynoGW80aaHS6jwvyty1fgQ8i7iOCHR\\u002fCjIKjVrEoHDjkYiQSKS4t3N7io9S8GDg0MrwwX1+e50CvtfbEwzm9n4zR\\u002fHW55Rcb\\u002fv3pLDww6rY+u2bjA0NLCxMyI1keDAeq2MYcx5P19Me3\\u002fOf6jwiuRp8A6Ezai9\\u002fc+fVru\\u002fd8dwoeQ9yGP5vdJhYBAHpk1mp5jKP+uL52ZBIpNYB3sbO9XjAAAKjs7wUA5AaGGEczeUHn2lvLersBAIZuM2kBgfj2eX6swZusmYFBr81ZhBuZ7OatzRH0ib985S94YLREJQa\\u002fueMB41quMcnKoTMRQhw6Wuyi1egun6q6dLxSJh7ajdDdNFZ0PLXhw217Hud6j1I5Dpq3IFZMzYn65c9841O1WltU2jrNqS1GJyESCaxZlvHlj6eNkUMnKhbOSQIACETyElxyOTrCPyXB5J6HdfHRJgl3DAOFJS1zZxCt26hrNMk7uY\\u002f+AZPpelrS4KybiIrqTjjk5ob13J6cbOeLkeEMfUh9CPcb9WYwAQAiq91L3cdnFwteP3USjg5zrrUVigRwON+vuxafbQcAkACYER7xx8Yb1iQm4eMAgL21NX0y+xpyIaPmwfg3Z\\u002fgvg6NWhbFjkz0tdlhykfjMcDhkS3Ku82dXSTlRcMgWx\\u002frhEHT\\u002f\\u002f103a0UmHB1FZAr5wbc24q9aAQAUKjljOtGpkrtJyYt54oObSJNp6kChku99bcNL39zp4TVKV8uIvQztXwAAZX3dpiMmyvsHLzwMjWKMDG3c64V8cDXhnhMUahw1\\u002fOGGfVkbBAPAtJ8MACDr6p9meC+Ct+7O+Y\\u002f+d7P17U9dJCyW9+bOBz19hm6uxI1Fwh2gMxFiD3S0EFFT0vL+k7\\u002fekPHcK7d99e9PZ7tb+fAr3El7Q+\\u002frd31LfM+JkUPzFsSSjJQwP9xpEQCwa5+ZlfqIvVYtTqfh9s8oLGmRylQAgDP5dYZGhYMvI7xdqkFqUoixNY0Bvh28TVCfdDeh0eqUpru\\u002fenkSTSfq9VhB0dh0y3AYgw7XdqtU1tbjTlQo4e4ILyYTACBSEa13EKqUAAAuw\\u002fntI1zku+Kio402PtSginUAwKbUNEv\\u002fRjqF8t+ly1ICAvBBnV6\\u002ft7YGH0HGtRyfuVujn4WjLpZgZ9ral+cZFOEHR0fMgYR7YlYkHHIeMoX8zGe3jdW1K4fLevm7u4wrsvEyZ7nRhau9Fqyb8vA7m8iTbAf5GcszPj38dOYsd1xWP8HQcIeWDNds3YoFkYPrdfY31lraS0mHYQcbB5cBzQ4z+dhJDwgCAHRKxCKVslUsBADkBg3V2mQHhgAAWsTCfoW8SyoGpjumAgBmh0cZdowv6elqEQvxQwgAYNnmGc99vpXJHmrjMwqy5yRu2\\u002fO4T4BJRzhemO+oFZnioTMRQhw6Wqy7eKzi0TXvPbr6vcM782US9y1sh5ReqNv+3gE46mJo3oIMRyaToJYmBZcbC0ua8RHEAd5ebHzhuUarMyS78SlvGpWybEGq8SkRHDZjxpRYfOTo6SqoPNyS0or2rtHdt4YgGpUC5aCJ98k5fLKip8++xfRjzsODQTatVOsdxT5j7mNyJQ6cJcLTCwBwubMDf+POEj2GFXZ0AACivLzhMTf2zJHDIpW1kvwaPtwba0a4tVpjOoXyzhK4YvpIgzvefkTGkXg7G7OkTHFyPxkDe6vmKVRydIodi8gcQKVRnv\\u002fqdqhWaxTEpYd\\u002fdODJqYvMz6uyZifCoXFl+Y0zX\\u002f3xHgd2DhjXeKE+b\\u002f\\u002f24HNfbHWrRvYkMilvYUraNJPp+LjGotLYNJrhcTW\\u002fz3TQvDVxiTw2BwDQJBRsryiBhwEAAPxYVtQhEQMAAtic1XEmv4CGinUdhh1vacQAYFKpqf5Da3izAwfL54+3NBjmOoYEvVEQx2NNXBIAQIdhz506rNVPloZLxM1elfXxgf\\u002fEpAytG3CpDfcsfP2X+8zm1mMINwl1LnQmQohDR4tZTVWdT2348OVbv6wpboHHxoM\\u002fPj\\u002faWNEBR10MzVuQ4TauneKB2z0Lw8Dr7+8fd0lMN3TN8iz804tFTWq1Ft9PZta0OOKl3EZL56fgn6rV2m9+sd12DMPA5z+4b1MpP18P\\u002fNPqOmvrU416+yVf4Pr2jBdkEsnfz+Tfe2lS3uJCCXdHzImMBAD0ymQ7y8vgsWG2l17pk8sAAAuiXZLpI45OoaxNSn5nydJ\\u002fbtxy6vY78u+65\\u002fAtt32\\u002f7toHpk4LHba16YBC8Vt5ORTEM\\u002fTVwUsKMGm2NVwqjzcz3CQ9WtTVpbC61RuCWBcc6W82uWBJSp59mXGC4jPCKVQ7Pk6jkkLojMHMmuuQSKStz6557ad7\\u002fYK84DEXoNGpNz62\\u002fP29j4dEmaxlwYtKCvYyPfWOO1PmJ3966Gn33EjNpeaszv765PO3PLXK03e0d2uA+AR4brh34XdnX3rtp3sj7ekI6f5yr3Z0eSf\\u002f9IDC9uZINDLlldmDzdNfOXv8p\\u002fJifB2ADsO+Ky164\\u002fxJw9PnZ86nkU3W50Z7+xr2TT3cVA8AyAgIouJ2pvFjscM9vQAAR5sbAACeDEbksLqBp6fP9WWxAQDn2ls3\\u002fr2zsBvOqii0mrPtLa+dO\\u002f7UiYPQ0CQRFsv7YN8TG+5ZSMWtuXa6kKiA17ffd+eL6ywtwYlLH5uEO0BnIsQe6GjBw\\u002fTYzo8OP7Ti3fICG8uO3ZlOq\\u002f\\u002f2zT1wdFSgeQuCx\\u002fVg3nvbPHxkQCC758lf7GqNXdfY+9HXxy8UNsIDk1h2engE7s5WUVlraVWHUjXUO8XefjIGs6bGRoabrErff7Rs97\\u002fF+MhwH351rKLGjh\\u002foKMtIMZmMVdR02sy59\\u002fSJ\\u002f\\u002fPqrgGBDB4YD6B\\u002f7+nztY0thCqKJhK4sQ5CxMbU9C8KL6l1uldOHqeQSBvT0s026cQA+LX0ymunTwIAOHT6DWmOfNY4y+KY2DcXL\\u002fFnm6QmeRxOnK\\u002fvvKioR6fP+LGk+O2zZ\\u002fAVar9cKbkzJxdaCWIkNu2oQ6dQfJi2qz43p2ecb2s1PtXodOU9PXmho1T8hUxIcenhxWeI9iZyUYU7g0WPTg6tN9153AqXNnCH5C1M+fL4c9vfP\\u002fjvT2fVuNmPE5FIpFkrMrc+t8bKJasBiUTKnJVw+p\\u002fx3TYxKMLvrZ0PHtt1afu2A10t8FqfCYzOpG1+ZNmGexYe\\u002ffPi39+cbKuHG4u5FMeTNWNZ+oJ1U7JmJ1jKKo53W9NzzrQ1AwAq+nvn7\\u002fh2WnBYANtDpdOKVMpumfSWtKyNSfBEYmVswgM50z4tKtDq9S+dOfbx5fy84FBvBlOoUl7sbO+\\u002fmrW\\u002fPSN3XXyy6VsBCQDDvqnHWhqAaT8ZgyxecJtYdLK1CQCQ5m+yY6pBsAf3i2XX3LF\\u002ft0StKurpvG73Dl8mK8rLh02jKbSaXrmsQyI23ANYeLX7zSREo1PvfHHdiptmfffGnvMHS+HhkWGw6BvvX3z9A4tpw3pl4o3Jvql46EyEEIeOFgCAQqp6877vC09UwgOWMdn0pJyopJyoiPigwHA\\u002fnwAuh8ui0akUmh1nTJ1Gr9fr1SqtSqFWyFTiAamIL+3tEPS0DzRXdzZWdsglcMWVTUWnqmuKWxKzI+EB10PzFmfBMKBQqmVydUe3EBrq6hF6e7LYbAabRbeQNnAX1yzLvFjUfPpCrTEyIJDd\\u002f\\u002fT2OdPjr12Vk5YUQh92JsUw0NMnrm3ovlzaermkpbVjAACQFG+y4A9Zuzzr42+OGx4PCGSnzw99h3n+3LzsKONT4igU8mP3LH70hd\\u002fwwfe\\u002fPNrQ0nfPLXO5HnDeqbmN\\u002f+m3J9y80fnsabEHj5uUtL70zp53X74uyvTWgoFGqzt8ovLzH06K7f\\u002fUdRMzp8YePV1lfKrV6f\\u002fz6q5Xn74mNRG+4pjA4M8UhIgQLvf+vGkf5J\\u002fX6vXPHjvybfHlFXEJKTwej8NhUqlKrbZXKi3r7T1QX9skEBje8vSsOVCy23WGL+vekJLy36XLoSAehUy+PSc3mMt94N99xmC7WFzS3ZUTbP73Qa426TnlS2wL2Znh4SQAhgrwAKjs60UJd4PX\\u002fx48URncNjc3zNf5BT5CufJMzdCpiMOgL0wZ32sbEzIjCCbc6QxarMuq\\u002fJJzo4gn3BPs7IQzQhxP1t0vr99wz8K\\u002fvjp+9I+LYufdJPcJ4M5bm7v8xhnEC3ayZ4\\u002fBhasrLNqQN39t7sk9l\\u002ff\\u002fcq7ykqtKXThcVkJWhJfpCsSxRWfSVt40a8WWmWX59Wf2lZw\\u002fcGWg11ULcklkUnRSSO785KmLUpNzo+1aRzIeLYyMeWTKjA8LLwAAxCrVkWaTqkap6WnX6Klpc4I43LfzT8s06j65bH\\u002fD0HUOAIBNoz2eN+vOzCn4oFFGQNDFzna1TgdMd0w1yA4M2VtfbRiFGrgbTQ0O27NhyxPHDxT3dAEABpSKAaWZ\\u002fsJBHDc6hsdEaHTAi9\\u002fcWVfauu\\u002fHs6f+KVIRbtxpSUCIz5rb5qzYMpPI9oBxqdaa\\u002fo0OdCZCiJvkR4tMrHhm0ycEZ5UkEilvYcqyzTOmLEh2wupJBgAAWCoIx\\u002fRYfXnbhUNlR34v6O8SwsOW\\u002ffvz2TFJuBugeYtdOroE324\\u002fJ5Wr5HK1VKaSyVUyuUouV1tqpfvCW4MrGEgkwGbR2WwGh0XncBgcNoPDoi+ZnzJ7WpzpO8bSi0+seuIleWlluzGCYeD0hbrTF+poNEpUuJ+XJ9uDw1CpNAqlRixRdnQJVGq0HN+G5QtTv\\u002frptPEbte\\u002fwUG3BysXplso3bcrJiFi2IPXQiQp88J+DVw4cLZ+aEx0V7uftxVYo1QKhvKSirQlXApWbGcn1YJ48Ryg\\u002fMJpmT4+PjvTHf6ndveI7Hv1xwazEKVlRIUFedDpVKlP18yWllR0XLjXwcee+G6+dWlHTeaVi6NB1fwtmJX798xl8S\\u002f3efsl9T21PSwrNTA3z9mJTKCSVSiuVqYRi+YBA1tsv0WPYjx9vxf0Z4x4Js\\u002fDRiViHAfD0kUN\\u002fVpr8\\u002fltyf960J2fOgqMuc6GtdcuuP41PQz09j95yG4NK6ObKk4cO\\u002flU1VEzx3Jx5d+bm4saHxHywDf80wsvr5NY78BFLVm3\\u002fuapvaC3JTZmZry0YXBE\\u002fyaU+\\u002fT7+6c\\u002f3bcyJgtMfI9c+IFr2znfGpxQy+eJrDzBphA4PZALQqLX5h8vyD5dfPlUl4hPafAZiuJZIyYvJW5iSOy95PF5LOF1nc9\\u002fZf0uKTtdUFTaNsBzP04cTFsuLTAxOyo5KzImMiA8iOTpPHR0YhlUVNl0+VV1d1FxT3DLyzdz8Ar2ik0Pi0sNT8mJS8qI5XEJ3cyeSwu6O7RVXLnd39MhkegzjMhiBbE6yX8AtadlZV\\u002fuqD9evkO+oLD3e0tAiEorVKi8GM8rLe35EzA3J6QFsS8kTsKeu6pGj\\u002fxoeF912v6E\\u002fjFFxT9f6v7YbHn+6dM2qWGu9jM+2txxsrLvU1d4jk0rUKgaV6s9ix3j75gWHLYqMTfLzh98AAADg48v57108CwDYkJj63sIV8PAEJZMoTu0pKjxReeV8nb3lorxQnykLUqYuTp0yP2X8fvyiMxFC3GQ7WjRq7X+u+6i6qBkeMCdjRvy9r22ITjZfHeU6Wo1u99cnfvrvv1qNDh4zh81l7ih5wwn3A5xhIs1bOroEm+\\u002f5Bh\\u002f59oNb42NsdHm1rrSy\\u002fcFndsBRR23dPHPrZhtpkMIrLY+\\u002f+Ds+sn\\u002fHw\\u002fh+686lVGleeXfv+UuOd2p64fFVUIfxsXImv+75N\\u002f\\u002fGR07sfoJCeDmFTqdfsP49fOSN59bNmR6PjxD05gcHoNptAACJBH77+u4gnuMVhCq19tEXfrOr7U9EqO8nb28+errqo6+HahmffWTFikVpuFeZMTrHYWVt18PP7VTbeRdn9dKMpx5Y9uuugi9\\u002fOm0MWv\\u002fy3OTYKCxpfvKVP\\u002fV6ojnnAD+PXd\\u002ffB0fHM5RlcxAJgHeXLMsIDPrf+bNQcxU8Hofz8rwFK+JHtdvvsSaTpTS3Z+cQzLYDAO7KnYJPuBd1dQJgPuEOYVCI\\u002fhVxvn74hHu72FUlBohZQV5cMolkLFLQ6fWVHT2uyOwj7olGp85ZnT1ndTaGYa113Q3l7Y0VHV0t\\u002ffxu0UCvWCFTqZUajVpLo1PpTBqDRWMw6VxvNi\\u002fMlxfmExjmFxoTkJQdNdl2DbUpJCpg4wNLNj6wRK3SNFd3NVd1Ntd09XUIBnpFA71iuUSpVmnUKi0AgEan0hlUOoNGZ9I8fTm+PE9fnpcvz9MvyCs0hhcWy\\u002fP258J\\u002funsjkUgpeTEpeTEAAMNB1VzV1d3G72nj97QPDHSLFHKVSqFRKdQqpQbDMBqNSqVT6Awax5Pl6cPx9OF4B3B5oT6B4X5B4X4RCUF27cowIU0JCp0yrNjcJn8W+6Hc6Q\\u002flTocHrFobn7x2WKsZo+zA4Ob7noSjFswOi5wdZncBowNf8wTA4bJW3jRr5U2zdFp9TUlLU2VHe0Nve0NPT8eAQqpSyFQquZpEJrE8GGwPJtuDGRjhFxkfFJEQFJsaFpFgfqnB+ILORAhxk+1o+eS534lk20lk0tZn1lx336IxuStPpVGuv39xdEroq7d9pdXazrnLJcqy\\u002fPrceRZPN6MJzVsmOSaD9tYL1\\u002f6+p\\u002fDbX88qlXZXyRgK+eHopLd2RebwhHtuRuRIsu0AAAaduu216195dy\\u002fBvvlJ8UFvPr\\u002fe24udGOemk6WUhOBXnlrz2nv7CB57JBK4eeOMO7fMBgDkZUfhE+7jwpSsqKceWPbeZ4e1OrgJxyRBNEmKmHVTRua1ySm7qyrPtraU9fbw5QqVTsuiUv3ZnKygoPnR0avjE2kUF26TZVZJl8k9wIX2bNaa6O8f5OHRLR2sH2kWCk2GrSA82YvwMvnY7b36dyGjg0oh+3mw+yRDC5RquvpRwp0IqUZ1qrPpcm9HnbCvXSYeUMoVOo1Wr6eTKWwqzY\\u002fJCWJ7RHv6JvnwsgNCEr0DHF5Ah7ejruTH6sutEqE\\u002fi7MkPP6JrLlsqnPqg0gkUmRCcGRC8MJr8+AxxFF0Bi0hM2IUWgY9ff7Ab\\u002fVX8JFNcZnvzBzjAmHjQQUPIAhiDoVKTpkSnTIlGh6YNNCZyNX44g\\u002f6Re\\u002fCUQAiAvey6IRKatzHZDhaCo6UH96ZD0eHIZFJT31484L1U+CB0TVlfvKWJ1b8+M4+eMCciouNbpJwx0PzlsmJRAKb1k1ZNDdp5+5L\\u002fx4pk8ktVk\\u002fihQb7LJqTuHJxekiQNzw26aUmhsTH8Ooae\\u002fHBVUsz8E8dw2LS335xw97DV77dfk4gtNhejMWkb742b8uGaTQaBQAQH8Mjk0nEC6tH0+xpcd+8f8t7nx0pLmuFx0zFx\\u002fAevGNhdvpgV8D4mEAfL7ZANLg\\u002f03ixakl6TKT\\u002fh18dq6ztgscmAdRSxsl0GEZxRpZtJOZ\\u002f\\u002f22raLBTkieDUXLfA6bjNty55+\\u002fjTYO3EIO53HN33GU6PghqKRPv53fo5lvxEUt+ryh\\u002f5shh41Mrf8VkMzotZQAA13+0vbJj6HS4eUbmC+sW4sYRWLNE8NGVc\\u002ftaqgy9jIkIZnPPbbh\\u002fhDn3P+pLnzq\\u002fHx+5NiZt2+zV+AgyOblnwh1BEARxKxMp4T7haTW6O+b8X2\\u002f7ADwwzC1Prdr8yDI4OhY0au3WGa\\u002fycS16LcmZm\\u002fTGr\\u002ffDUQQZaxqN7mJxc0l5a21DT1ePSChSqNVaGo3CYtE9OIzQIO\\u002fwUJ\\u002fYaF5uRmQQzxN+M4Kz50DJe58fMT715DJ3\\u002f3C\\u002fIf3tFBqN7kx+3YXCxtqGnp4+sVKpodOpfr4esVEBU3OiFs1J4rAt9ldxT7UNPacv1JZVdbR3CSUSpVqjZdBp3l6s0CDv5ITgmXmxqUmj3S7MpSpru85frK+o6eroEkqkSoVSzWLSuR5MrgeD58+NjebFR\\u002fMSYgODA0e0KsLdoAp3JxvzbDsAgK8YakXnQ2wvUzwf1tCiS5HSvtaiRHDpJouwJJYb8iAu4udhsvKxlS\\u002fEP0Ugv9aWvHLpCPFUu0GyL2+E2XYAwPfVl6HI300VL09d7EUfNyujEQRBEAQZKz7cu7is1Tr9gE4\\u002foNPzewWv6jG0tNRNHdpxgUi2PT4j\\u002fIaHlsLRMUKjUxdcO+XPz4\\u002fBA8N0Ng81FEUQ90GjUWZNjZ01NRYeQOwE9QxZMi\\u002fFidl2AACNRlk4J2nhnCR4YNxKiA1MiA2EoxNXSkJwyuRbTkS0az4yjmhwmUGW\\u002fd0n6Lhu7Eqtffs5EAF9SfbmMZGR88TdUwEA9IktLs5C9jRVPpd\\u002f0IGjdEGoE+ZtLWIBFNFjWKtECAURBEEQBHF\\u002fDZ1TatpC9NjozbvIJA6dFsdiTPVgLffibCGT7S7EQUbNPz8Qas57x\\u002fNrSeSRlnQ4Uc5cQvmvvk6BTjtJe\\u002fgiyISnx7Bd+4rwkVVL0vFPEWRyQgn3CciTMbSaRqSyu0QdX3JuqQG9cctNB0A7uGr1aO412jxZJuut+nH93BG8RvHAU+f\\u002fhaPEzA+1Y+8Eu2j0dmf\\u002fEQRBEAQZW2ptg1Znss0SghjVXmltre2Go8NEJYVkzkqAo2MqPmOwv7B1Oq1eLrX7shRBkHHhwqWG9s6hQrGkuKC4aB5uHEEmKdRSxhpjK3NnsWv\\u002fUod5MZn98sG9FPhyuVqno1vIm5vVKZEYH3vhcvd4I6l8h9psUMjors9oo5ve85CrCe2RPQl9WnbebG17Hi9sYVhckk+AL4PNotLkWrVIpRSplR0ycTm\\u002fu3ygm0amhHt4w2+zX5SnT8VADz5CJZFjvfzwEQRBEARB3J9MSah+GZmcCo6WwyFz5l2TA4fGmocXm8VhKGS2e4Qq5Squt0lbSwRBJobf95j0QV23Mgv\\u002fFLHX0eK6p77ZBwDIjQ\\u002f75tHr4WGHdLTw\\u002f\\u002f29oPxyc1f7gFyqojNpXC9WeFRAcmb4Tfcvgl+NOAlKuFtz5z9\\u002fw6GRaXzkcTjkAn4sdgMY7ACo1ulKe7qnhBDde1Oj01X0DW2nSaNQdHr98Jw4Pik\\u002fiHDJu0Jjkt7l0OxueoOMEMV0IapaayanjAyoFHsaK6GgF5352bx1s4KjoDjEWes2bk+e8sQ5kxL7GxOyUAN3BEEQBBl35MpTcAhBrirPb4BD5mTPSYRDboDNZRJJuGvUjhdsIQjitkor24vLWo1Pvb3YS+al4MaRsXd8X8n7L+\\u002fGfwgrZCqFTNXbKVTIVSjh7joo4T4BpQcGXuxoNz79s6KCeML9YH29Cle93iEWf3rp4sPTpuNeAgAApT3wmkeJWg1FLBGZ7pLKMd1DFRkFEqXJj4BOtWMBxORxqadNi8F587dmLLeZbQcAUIfdo3LMhth0tV73Y\\u002fXlFokwiM3dkpC1NTkPfhGCIAiCIO5Nj8nlqgtwFEGuaigfunazhEwhx6QSvaYbTRRilxIMFrroQ5CJRqvTf\\u002fiVybbJ163Jde52qcgIdbUNbHvpL61GBwBgsuiZU2P8ArgajU4kkDXVdue55X3cCQMl3K1J9g+AQwCQSIBOoXozmXKNBp\\u002fX9mIwvVlMFpWm0+uFSmW\\u002fXGas+V4WG5cdHBLn62t8sUtlB5ts\\u002fru7umprdk6ivz8+aJZKq\\u002f30YgEU\\u002fLggP9LLa21SMj74Z0UF\\u002fikAQKxSYgAQ2cGnWWCyDySXbr5rDeI6bXwR\\u002fimXiX4EZhT2wlc+EVzvFZGENoZyos3xWZvjs+AogiAIgiCjSK2pE8l\\u002fVyjz1domPSYBgEQme9IoEUx6Cpsxm8NaRCZxoLco1aVy5WmlplylqVRrGgEYvItf1x5v+kIAAPD1fCDA63k4epVG1yGS7pArT6l1LXq9iEL2plGjOMzF3h5bKGSXX19gmEok+0OqPKxSV+r0\\u002fSQSk0YJYTNne3FuZNDMz4vkyjNtfZsAACH+X3NZqzCgFct2SeR\\u002fqzS1On0\\u002ficSiUcLZzNk+Hltp1Aj4zZOPiC+VSRRwdBi\\u002fQC8a3R0v3uUSQs3ZWWx0xYEgE82HXx2raxxqkODrw9l4TS5u3GlWvPBNt0BybtuDbAZqkGCff\\u002f+4aMi2h0T4vffT3T5+HvhRbAS7MyI2ueM52338u+VmOHRVi1B48+4\\u002fAQBJ\\u002fgF35ebOiYjyZ5v0pJOp1Rfa274rLspvb2sSCl+ctyCEy8W\\u002fwHWmhIRSSCTd1d8cjU539z97ft6wIcLL2+R1prR6\\u002fTNHj9Ty+6G4Tq9\\u002f4tDB4q6ue6bkBXO5Gp3u44L8\\u002fPY26GVyjaZFKIjy9oHiw9WY\\u002fhWR3l74p4irKTXaK61d+IgnG7UoMaNVKoQiMwIjidxSQhAEQRBkAtH1Cl8XSL42ZswHo7p+na5fqS4SSn8hkzgxIQVQ7lso\\u002fU4k+x0fcQjGF7\\u002fPF3+EYUNrSbW6Pq2uT6G6NCD5lOf9shdnM+71TiZXnu0WPK7RDlUhYJhapRerNNUCyXc+3Dt53i8CYLGYUa2p09J7OvpvV6qLjUEM06j0FSpNhVD6Q5DvNk\\u002f2etw7JiN+t0kpjCU+AaN0LWkXrUZHcDdUFgcl3BFk4hgQyD78+tiJszX44D23zGUynZ8Qb+4RdAuG9TRGiKkoajE8uPbmWVC2HQBAgrZYRJzKmQl3tV7Zr+oQafqlWqFar9Lq1RiusXeW9zxv+gTZqlit0929b0+7WLwsLv6TFauGtzgHAHDo9MUxsYtjYl84fvTXstJ79u75a9Nmmj2blzqMx+Esjo09VF9vjLSJRau3\\u002f\\u002fLI9Bmb0tI9zLVwudTR8c7ZM0VdnfAAAAAAPYb9dKXkpyslXgymWqdVWNgx9Xxbm82Eu0anO9s61OELAJDgZ7v0HnGiPwrKFKa7pEb4eeOfIgYiNXzxEO1l4\\u002fBGEARBEGSC6RO9I5B8aXhMo0YwaWlksg8AGo22Tamp0OvFAAAmI2d4pTmXvZ5BSzU81mPKftFbhscB3i+QAJyPYNKzoIhB98DjItlvhsdUSiCLkUcmeen0AwrVRZ2er9eLuwee0Oq6\\u002fTwfM32fc0gVhzr5d2OYBgBAJnmwGNOolCAMUyjVRWptMwB6geQrra4nxO9z+J1XKdVXpIrDSnUJACQmPZ1OSyABmlrboFBdAgDDMFX3wKN0agyTngm\\u002fczIhmLB2z5YsLTVdeh3cg3E4X54nyXQTKQRBxgWlUvPGB\\u002fuDeF6eXCaTQdPrMaFIXtvYU1zaqjX93Z81NXbFojR8xFnyqwdTxogD+L1iw4PoxCDTEcTlRppw12O6RmlptaSwSVrWp2rHZ9gh4eyECZNw\\u002f628rI7PZ1Gpby5aYjbbjvfcnHl7a2sq+np\\u002fryjfkjFKs8lbs7LxCXcAgFStfuP0qffOn8sIDEoJCPBmsVhUqkSl6pJKLnV0toqE+Bcb3JCW\\u002fk9NtRy3x6lIZW06+Gvplc3pGdZnUvvramWm3d6J9LpBnKWio+eDg2ehYEY4+uQ1Q6yGd3\\u002fi0lBhDoIgCIJMInpMKpB8AwAAgBLi9ymXfY3puE6hLpHI93CY80zjAADAYc4zxvV6sTHh7u1x6\\u002fD+M2YJpT8Ysu0kEo3n\\u002fbq3xxYABq87MKAVSr7tFb4OgK5f9F8GLc2DtcTkzSOm0bZ1DTxsyLZ7e9wS4PU8mTxUYS2W7+oeeBLDVBL5HiFjpreH+WXBUsUhAACdFh\\u002fi95nx9gMAQKkuae+\\u002fRafrxzANX7wt1P\\u002fHofdMPmqVSSmMJe658L\\u002fqchMcMic8LhAOIQgyHmh1+lPna+HoMBGhvs8+sgKOOkl+tUnJJmIXpXIw\\u002f8Z0y7u2E5vjCXelTn6Bv69w4LBYw4fHJrp9dTUAgLzQMB+m7V4cbBotLyT0eFPjP7XVo5Zwnx4Wvjgm9mgjvN+9Uqu92NGObz1viS+L9dzcednBwU8fOQyPmYrw8moViQAAlX193xcX3Z6dA7\\u002fiKpla\\u002ff4Fkw2jKCTS1NAwfARxEYVa80dB2YeHzik18AKFnGh33H9pzGn1OihCIdm4u4YgCIIgyESi1tRjmBIAwKSnDcu2AwAoLHoui+6SfrV6TNYnetvwONDnLS\\u002fOjfhREqD6cO8BgNwrfBkA0Ct8nsNaQBrBld1w\\u002faK39HoJAIDLXhPoM\\u002fiVGHmyN+h0A4a\\u002fnS\\u002fe5sW5gUSCy\\u002fYNKGSv8IDfqRSTfCuTnsXzfrWL\\u002fwAAQKY8hWEaS2+fDOjEWhITbJU+ys7uvwKHzAlDCXcEmbjCQ33+9+p1nlwWPOAMCrWmsBZuaIwQpFSo9Tp3vFk7STgyLcMAdpF\\u002f4FjPDoVOCo+N2IGu787378VHyCTyk0nfcKlu1MyhYWAAABBMuCc7j8MBV981at5ZsnTFLz\\u002f1ymTwADHvLl3mQadfn5rWJBB8UXgJHr5qVkTE3bl5t+7eZXj65ulTZBLp1qzs4XXuMrX6wf37oFL6qWFhUO\\u002f7CWzXpfKWfiEctey3\\u002fNKTVYRqRqzQ6\\u002fUSpbqVLyxr64Y6yRhE+HnnRqGEO4IgCIIgCMyYBdbqujBMRSKN3lo3sexPQ78aOi3eUpd2H+7tAunXGm27Rtsule83d0vAQVpdr0S+z\\u002fA4wOtZ08FBXh439Yve0WNyra5HrjrNYS6CXwEAAMCHezeUbTfgslZ1kx7FMA2GqdXaBkv7r04GBJubd7X0YxjmVv122xt6Sy\\u002fUwVFz4tPD4RCCIOMfhUJeOj\\u002fl4bsWcdjOrJ6uau3Jr26tae+rae9r7RXor67vmfX4J6YvBACArUvzHl47G44C8PXBgs\\u002f2ngcAHH3rbj9PDgCgrrN\\u002f19nSS7XtPQIJhmF+npyEUP+ZKVHrZqSRcT2vCqpb7\\u002f14FwAgKtBn90u3GeOQ9a\\u002f90NwjAAB88dCGaUkWNwBXqrUHCqsLqlur2nqEUqVUqWLSqP5enCieb0ZM8Jy06ITQAPg9tohkyrs\\u002f\\u002fLO2ow8AkBTO++KhDV6cwVLg5rqev346x+8V9\\u002fWI+D1iGa5l2QPXw9+9sCj\\u002fb\\u002fZabEmnVmmP7i3OP1HVVNstHJDR6dSAIK+s6bHLN0yJsnAPtTi\\u002f4dm7vgMAvLDtxtlLUnU6\\u002fbG9xacOlLU09AoHpEwmLTDUJ3NqzDU3zggKtZ3gVSk1J\\u002f69UlzQUF\\u002fZIRLI5VIVg0n1DeCGRQUkZ4ZPnZsYkxgMv+cqB754V7A74S7VCn9r\\u002fV+zrAIecJJsn4VQwl2P6YsFJ+YGXIsPji2pWg0AkJu2RrHC0ERFQvj1TuHDYn2x5po79vwtUCjgMVtemDd\\u002fYXSM4fF\\u002fZs\\u002fxY7PfPXdWo4MLfqeGhn28crUHne7NZAqVSgCAHsNeO3niz4ryjWnpeSGhgR4eNDK5RyY709L8fXFRu3iwe5TR6oREKDKB7S+pya+3YzHUvuJqOOQCW2ZludO83S2odboehUSlg5cCTGZ8pfxwW21BT1uNoK9HLpFq1RgGODRaANMjytMn2YeXxwvL5YWxqYRKtKxQ6bRnupovdLdUDvS2SAQilVKp09ApVE86I4zjlezLm8oLXxgW6zHh2vtUC\\u002fqOtNVd6m1rFA8IlAqlTsOi0gNYnHgv\\u002f+lBEcsi4kM5XvjXU5zXCFWkVhb1dVQO9FYM9DRLBCKVUqRWyrVqGpnColJ9GewQjmesl1+mf\\u002fCsoKhANrzTzkjUi\\u002fhfVRSc7WruVUg5VHqCt\\u002f+yiITNCVkcqpkpe3Ff5w\\u002fVhRd72vqVci86M9TDc2FY3PWx6SEcT\\u002filhI3hv92gVSIs6Gkt7O2oEvQIrv7tvgy2P4sT7+W3NCJhfmiM2e+Gs0za3zgEIYhOS6SQ\\u002fXR6vlbX295\\u002fc6DP23Tq4AzZ1WTK04YHXNZqACx95lM8WKsMLeZlyhNOTLjLVecwoAUAMGjJNGoUPAwAAIBMYjHoqQrVJQCAXJVvKeHOZa2GQwAAAEgkOpUSaNiOVa8XwsOTiacvoRZDCpmqta47MsFifmH0bX\\u002f\\u002fAKYnVDuZM2\\u002fy3lBBkHGNTqOsW5HV0j7Q1SOUylQKpYZGo3h6MCPC\\u002fLLSwpfOTw7imVykOMWOUyV78yvhqKN6RTI\\u002fT86X+\\u002fO\\u002f2p9vzN0DAOR9wrY+YVVb77Wz0nEvd6bd58s\\u002f2nNWKDXJyMlVmtZeYWuv8HR5468nig+9cReVYsdKepFMee\\u002fHuwzZ9tTIwM8evNaTPdR4o62p7\\u002fDfl4de7aiSgsZtL+3q7RQaIxq1VlavbK7v2fPrhfU3zbzzieVky+212xp7+b3hrz26vaZsqLuGVKOTVnc1VHft+63gsdeuXbDSWv+Pg7sKv\\u002fvwkFggxwcVcnVHC7+jhV9wqvrvX87\\u002fcuxpKtXMNpkj\\u002fOKdyL6Ee7ey+ZfmN0SafnjAeYKYUcHM6C6lSWFvkeCoWyXc\\u002fdnsTonkYkeHDsMotrKVOgy72NEBAPBjjXYpd1ZQ8F+bNm\\u002f9e3ezUACPWcCm0f5v4aL1ySn44B05uYtjYr8rLjrT0tItlXgxmDG+Ptcmp65NSqKSyQCALRmZn14sML6+sq\\u002fvlRPHh95vQZin54aUoWaOyOhLDuFtnJYBRycHgUrRIRN1y6W9cmm3XNItl\\u002fQopD1ySY9cKlCZv0f1zIUDz1w4AEeHWRIe\\u002f\\u002fWCDXDUstuP\\u002f3G8He7+ZMnckOifFm+CoyOQsP2\\u002faty9tDPX3hvu4T00DECbVPjBlXN7Giu0GLwjllClE6qUdaL+I211AAAmhTovNObdmSu96LZ7bQ3XLZd8UZ7\\u002fV2OFeNh2tQqtRqHV9Mill\\u002fs6fqkpZlCoq6OSH0ifEePpC71yPLrU2\\u002f5u0clLvXCnL6lGJdWomsQDh9tqXy88tigs7umc+XFefobRkSdhu+WSg621h1trL\\u002fa0Df\\u002fhAgBUOq1KpxWqlI3igbNdzQAAEgB5geF3JOcti0iAX23ZvN1ftEiEhse3JuW+OnWwy\\u002fCvtSUvXzyiudq7SaRWXuptv9Tb\\u002fkP15S\\u002fmr0\\u002fzHdpbQo9hrxce\\u002f65qaK1Vv1LWr5Rd6e\\u002f6sjz\\u002f5amLN8VZm64NN2r\\u002fdivOdDV\\u002fW3nxVEfj8ERFr0Laq5BWDvTsaSJ6pfFE1tyHMmbCUasm7W8cgtiFBKgB3i92DzwKAJArzzZ1zeUwF3h73MhhLnZ1CxSVutTwgEm3tgcdkz6YJlConXCBbaRUFxseWC88N5aua7UdpiODSCQGnRYLR68igcGbeYZO8ZOWf7A3g0VXKWzXZuUfLnefhHvB0fKTxNI6EQlBASG2ixmRMaSTfqKVbIOjAND8d5Fp2XAUmUzodOrj9zl5jxCbVkxJSgwbrPtWqbUf\\u002f3PO8PiRdXNoVDhPmho5dNVgVr9I9kXphS\\u002f35wMAWHRaTLAvi07rFUk7+WKtTj8nLRp+g5O8\\u002ffvx304NNd3iMOmRPB8Ok94nkvUIJIb2A2umpdiVbZcoVPd9vKu6rRcAkBEd\\u002fNmD13KYJleFsUnB9\\u002fxnFT7y48dHlAo1AOC62+b48UwKlbhe5rsAXThR9cYTO7QaHQCAxWGk5UT5B3qqFOrqsvbOVj6mx\\u002f766Ry\\u002fT\\u002fLsuxbzErUVHRdOVteWt5NIpLjkkIhYHpVGaW\\u002fuqyxuxTBMrdK+9\\u002fyu0Ej\\u002fhFTzvRY+fXPv3h35xqdsD0ZYpD+Lwxjok\\u002fR1iwz\\u002fnMVrc8xm20f+xTuRHQn3XmXr940vyXUSeMDZsnzmd3WZJNz5qq5WeXUE29qEbzTlBod0Smp6ZNJtF849NdPM6hW8j\\u002fIv9MikAIDs4DGYHkV6ex+46eafrpR8UXjJeqk7CYBVCYlPzpod4WXmFmWkt\\u002ferCxbC0au2Zuf8cuWK9S1Vh3tmzlw6xcxvCDI6uEzGe1tW0c19SE0Gz+UfPNBSA0cnvS6ZBJ9w\\u002f6O+9JWLR2Va21eAAAClTlvY2+7A1rI6DPuqouCDK2cJripQ6bS7Gsr2NFXclTL1iay5htt+45FGr3u98PiP1bYvVvUYdqSt7kR7wxPZc+9Nm04CwJfp+B3ciz1t31cXHm6t1dm5\\u002fRoGwMWetos9bdMCIz6YvTrY\\u002ftLyioEew4O\\u002fGyueyz9oOjioXSq66cjOf1beFsH1NkSevXDwt\\u002fqh2SqeXKt5+vwBBpm6LobQ7dsx\\u002fLcbSTWqZy4c3NdcBQ+Mlkn7G4cgjvHibCSTWL3CF7S6PgD0MuUxmfIYheztydngzbmFTouH3+AkWv1ghROFwjMdMUElD47qdM6siNLqeg0PxPLdYvlu00EzdBZK1ClkP+NGr4glJBIpLJbXUA7feh9u\\u002f89nr7t3EWVYvmn0dTT1vf\\u002fEr3DUgtkrs+AQgiCIZTOSI2ckRxoeSxQqY8J949xMNrFNL\\u002fD2XKg4fqWO5+3x+LVzF2XFGxPcMqX6ZGlDTPBgPZNz\\u002fX76ijHbnhYV9NA1s6bEhxsb1+gx7Epj54krDXYV10sUqns\\u002f2lXV1gsAyIkL\\u002ffj+9cO\\u002fGyERfutvNqnC2fHVCUOGesGqzNgk2znJnk7Bf5\\u002f7w5CwXrVx6h2PLWd7DF3gH99X8sEru9Uq7akDpZl50Suvnzr0TpwLJ6oAABExAU+\\u002fswn\\u002fl9aWt7\\u002f0wE\\u002fCAZlWq\\u002fv1i+OvfHzz0Huu2rezwJhtT0wP2\\u002frI0oy8aGNBOqbHKktazx+vXLEhb+g9Vznli3ciogl3mVb0Y\\u002fNro5BtBwAkcHMPdH0PBeskxe6TcL8+NW1vbQ0A4PNLF1uEwgenTkvyN9N3qX5g4OOL+XtrBhuDbEy1Vp\\u002fiOgwq9a7cKVsyMk+3NJ9taSnoaO+WSg1dbgAAbBotOSBgTmTU2sSkSG9vk3cS5stivbxgweMHbRf\\u002fGq2MT1gZ75w6wfHi5tnZfh7s\\u002fPpWvtRkXcyYCPfz\\u002fvTWayL9veEBZHLrlImNj7eVnPmodHByQ9D80FiyrUU\\u002fEJFaed\\u002fJ3ee7W+ABW7R6\\u002fefl+QU9bd8s2DCS7PNYkWs1dx7\\u002f065\\u002fuBbTv1N0slbY979Zq32Z5usRbHLgxzpcQU\\u002fr+gM\\u002f\\u002f73yliA2Fx6zqmqgFwOgWya2lG03EKqU\\u002f7mwf+fSGwEAv9eXWsq2Gz1z4UBeYBjUdWe4sf23G3TJxDcc\\u002ftVY8j\\u002f6Ju1vHIKMBJe9hsNaKJT+LJR+r9G2AQB0eqFA8q1A8h2XtTLA+yUaNRx+z0jpMExleEQmWfvAJ5MHfx\\u002f1emfurWXYLpU4Q\\u002f+Z4ax\\u002f8YhRal4MkYR7b4fg729ObrjXYhXU6Ohu5T93wyciPqFDjkIlr7xpFhx1K3qxqifL+Izmt51MnzE0epVesU8jfNj4lB54mUQ2U7aPaUp1in8wTQmmbcEwCQAYIHFIZB6JGkWmZZEZM0k0d1xeTOHcTmauwPQCoB\\u002fA9ANa8RsAk8EvQpDx6VhJHc\\u002fbY\\u002ft\\u002fbvT3MunfxWHSV01NxkecRaZUG28SzEmLfu+uNTTTMkcyiZQdG5oda76+2wjfRFSqUN338a7K1h4AQF5C+If3rWXR4Wy7U3z\\u002f4WG5VAUAmLM07aEX10KjC1dniQTyL9\\u002f9FwCw\\u002fYsTS9flUmnmKzg9PFlvf3OHb4DJFVNCWtg9T6965+nfAQCXz9drNTro7XKp6vsPDxseT52b+NIHW6AXkMik1JzI1JzB+zEQZ33xzkIo4Y4B7Pe2bWINHx4wRSMzvGkBTAqbQeHUSwbXITrAnxHKpflKNAP4YKO0dFHgZnxkDM2OiFyflLK7uhIAsL+udn9dbTCXm+jn789mM6hUlVY7oFDU8fltYpHxLaviE+ZFRhmfjj42jbY8Ln553GAZjkanE6lUTCrVgz7SvgQG65KSGwcGPsE1lrFidkTk\\u002f5Yth6MT3fzkmPnJMQCA2q7+83UtF+pbCxvblRrz1yeuw6LTNk7LuGfhVC9cqy8EMeiSD15gf1h6zoHU5KKwWDhk1YBKseng9jqR40V5RX0dmw79unPZjX7jKgOow7D7TjmS9AQA7G6sYFCoS8MdvGG5Jjr549Jz9lV3m9Mtl2w99seuFTfb1bhfplU3iwWflJ2Ta210D8jvbi3oaU3y4b11+QQ8NoxSp\\u002f264uIrV5vVWDK2\\u002f3YAgEyr3nr8T7PZ9mhPn5yAUH8mh0GhClWKRvHA5b4Oha3vkgGVRPagETqVT9rfOAQZOTKJ48u915d7j0x5WiTbKVUcxDAVAJhE8a9MdTrU\\u002f3s2w6SgbMQoZBJLjykAAIb\\u002fW6LXD5ZxkMnO3GeCTBr8HWfSM5n0HNNBM+i0ODiE2GPKgpR\\u002fvj8NR8356b\\u002f\\u002fps+IS8i0uEGfq105X\\u002ffmPd+JBUSzsTOWZfgF2bgj7lYw9RVgNuGusXH7H9P3aoXP6VXDGqtiQkwvxLS1euVhIAEkWjLdf5\\u002fljRnGCIlNosYavyadZBuGEu7IBPLUdfOhbLtL\\u002fZNfIVWoAAAcJv3\\u002fblkOZduJYzMGZ\\u002fgypfr+T\\u002f6qaOkBAExPivzg3msYNEK5XHsJ+iVnDpUbHt\\u002f+6DLTwUErr88ztKnh94qL8+vz5iTCrwAAAHDtLbOgbLvB7MWp71EpWq1Oo9a2t\\u002fRDW5ge2VNk2OiVxWE8+cZ1diXEnfjFOwuhH1Kx4HijdLCN4HAR7KRsnwVh7AQeI4JMGqzzf7Fsvemr7BPLySgRnsRH2hV1Kr2CQXaXKol3lizVYfp\\u002frlavd0kkXRKLlSAr4hO2LVsBR8cUjULxZzv5avnxmbOCuNw3Tp1UaC0mkSlk8h3ZOU\\u002fOmj2Z16QnBPsnBPvfNjdXo9OVtHSdr2u5UNda2dGjI7bpkAOoFHIAl5MdFZIXE7Y0PcEbpdoB8GGwrBeo9itkUHNnLzqTRSC\\u002f5suw7zfrpbzFW5PzBEr5gEox+H+VfECpGFApOmQiiXqwwG10dMnEAIB\\u002fW6o\\u002fKDkDDcV6+WX5B\\u002fsy2L5Mtg7Ti1TKBjG\\u002fcqC3+2qOnkomzw2JNn2TNUqd9rajv5nN\\u002fWX6B88Jjs70D\\u002fZhsDxoDLFa2aeUFfa2n2hvaJYIoBfXifrvPrFr57IbaWQ7zsdj6\\u002f2SM6c6GuEoADQyZXF43OKwuGCOpy+DJVQp+5Wywt72w211+MUHO+uutJpL2hIR7+W\\u002fMCzuWHv98Hi6X1Cqb2Ckp48XncmlMZQ6DV8pL+N3n+houNLfBb0eAFAl6P2x+vJ9adPhAav2NlfuaawEAIRyvO5MycvwDwYAHGqt\\u002fbbyItTm5ff6sjCOp2FPBSqZvCEmfUVkIo\\u002fFaRQLPik7Vy3ow794T1PlS3mLrS+wGPN\\u002f+0sFh6sFvVBwdnDU0znz0\\u002f2CoLhCq9leW7yt5Ax0c4JJoc4NiYnkekd5+kRwfSI9vEM9vGxuJwMm928cgjgPicOcx2HO0+kFQulPA5LP9XqxXi\\u002fp7L8rJvgCmex4s6nhKJQAvbYVAKDV9cBjOFr94CiFEmA6MiIUir\\u002fhAZOeHejzhukg4nyZM+M9vNhSke1VsGqV5qVbvnhj+\\u002f2xaWHwmIsppKof3tm798czBDdKBQCQKeQtj7vXVbBNek2p2dMbprGYDwEAYHq+pn8jpmu9GiCRKMGA7AUwDdDzMf3QyZRMy3C7bDuCTGiebObCrFG9K1xQPfhRsHxKohfH8dyLoT+7Idte1twNAJiVGrXtrjV012TbAQAlFxt1Oj0AICo+MDjcFx4GAADAYNJik4IrilsAAGWFzZZy1rOXmO\\u002fwQaNTfXlcw46mEhFcUlCcP3iltmBFhqe3fXkVJ37xzmL756TFNEe6f4GjAAAAwtmJ14TeG8SMggdGLMojFUq46zFdh7wuxsNdVmBRyeQPlq9clZD42aWCK93d8PBVaTzevVOmTp7eKTemZyyMjvmppHh\\u002fXV2rSIgfCuR4LImLvS0rJ8bHzOK7yYlGoeTFhOXFhD2ybFZVZ+91H27Hj75\\u002f0+rUMJPbfQ6gkEgeTIaH6U4aCADgzek21lgs\\u002feebWqFJZurZ3AU3xGfiI04RxfWJ4pr\\u002fpfhf8elPys7DUVfqkks6ZeKnzx8wXkh50Zl3p07bEJtm6f5Enaj\\u002fWHv9Xw3l\\u002fkyOhz0N3F8uOFLKhz88p\\u002fLCn81dkB0QAsUBAKsik16csmh3Y8Wbl4\\u002fzlSZXpJf7OraVnHk6Zz4+6LbKB7o\\u002fL78AR8H\\u002fs3fX8W2c5wPAn7sTs5mZYjtOHGbmJim3KTNt7bqu27qtKwza7rd1hZXbdWVumiZtmDlxyHEcxxAzk5ilg98fcuTTK1mWjHJy388+nfS8J8tSfNLdc8\\u002f7vDAvPu3VOWuixGjxxZrU3L9MX\\u002fZDTemLp\\u002fZqLq3oO7DqeJeHx89wJZ0xgNlxqcuTspYlZcX30ZR8SWLmExPnHm9v\\u002fN3RLc2m3mlbLv+9cOKecVMCuRDl9urZQwCQHx7z5bJbVcKeA9ApUQkZyvA\\u002fHN3G3nJ7YyWB4QAgJHj\\u002fXXSD+3JOXnjMwoS0FT991GLu\\u002fX20dmupun1iZJw74tMovvZqvfrH2jIkeFt24QszVvi8TiDm8R\\u002fImz4nLvXWnV\\u002fpWEuk2CnyvtypM2ODrm28Yvc4Dmc4EHhYhOLXcsk1jR1XUbSOorVG63al9GZ0u0EQCSY5yUYAsDvOycVXocOX2BylrhsiQaHHwOCIBIUAnwKA3dlTLHbZ29LyQpO55M7096U832fpw0og4i9fN2PDB\\u002f1P6gIAvdr0u+tf\\u002f8Xfb1y+LrjrvgNmszh+\\u002fuTgD+\\u002fvDbCNjNuau+am5vTz1Rw6MH4u4yzvI7FO0c4yAMB4OQxZiQ4CUIaXerLtmIQnf4oQXwN4b10\\u002fQ6sZxwnavp+ybscl69xxDoczAnKTo30ebA+f8w0918KnZg2q45xUJCAp+nf\\u002f3Xyurg0AFkzIePn+1QOulw9EZWlPc7O0LLQYiM1dut7ZrvMYuEQg5CWl9ly59yYQ9CSiXc3W2SrPt7huTJiW7jnSv6H65YdQ\\u002fwn3Yu1eE6lDowALom9cHH2ru6R9aEULffxddtlbQifh7rIsPWNZekajXnemra1KrdbbbBbSKebxlEJRZkTEpNi4KzC5HCuTPTV33lNz56ktlnaTyeiwCwlenFweKxvKia6Xn+zYKFc\\u002fInckUi5JCPOdBuJwhkm1Xv3E4Z9Nzp6y+mvS8l6YsUIu8JdGz1JGZikjH8mfqXf05gT7dbC1zrsx9x05k\\u002f4yfRmv768VHMNuyBg\\u002fNTrxzt3fICXeH5QVXZuen6Mayvq+YfLSqX3eK3bell344syVfo4Eb8gomBOXesO2L9hZ5oGZHp20NDEzUxl5W3ahe2FS\\u002f2bGJm9efe+1Wz9F6p01NsuB1tqVycGVBhAY9sqcNe5su8u6zIlfV5Wc7W51R8xOh+vGrybMRiZPyPjCXxbM\\u002fPPxHezg2e7WfhPuo\\u002fja3z1\\u002fjPb8dx8fHvu36cv9nwDkhkW\\u002fOHPlowc2uiMMwLNFO3dd80DvRgG4kvc4Dmf4CHipcsl1OtPHAOBKjvcJ6704R9MmnECvrXqTiZYYLZsAwGjZHKn8Qx8lsZTJutV1Syqa7zk0KFLRAgACgLLai51kPZ+Xim5x2WGA4eMizPf7PBLW3jP\\u002fp48OkiSaffDJZnG89tuvdn9\\u002f4t4\\u002frc2d4vEVOYQYhqksbtj57fEDP52xGIM4zHOJiFHe9fvVaDSEYbwsxlnOUK0MrcbwCPYQQ1YDYwEAnD+e8k64M2bKtsV1kx\\u002f2Fi5c6DEKgOERmGgVLlrFU\\u002fwdMK4QisMZUWGyke6ToTP11EglRAwqnyMVCf7+1e7jFQ2uu\\u002fcvnzas2XYA0Hb3TF7ft7Vk31b03MGbd4m6izJMirEa0AfO3a8sNjHoVOpQ\\u002ffJDqP+E+ynNLjQEMD1i1dKY29Ho0IkU+lg9oNvec60j1CQrVclKFRq94kVIJBFD3bXmMkbgWGZsRFmzv1nDHM5wqzNo6gwa1+1HC2b9ftICz3F\\u002flIJAp8tRDPP8CfSbZWVyzgszViBBn1LkqvcWXHf1lk\\u002fZPX8ohnml+NAHi65nbRiKTne1eBenT41O\\u002fNv05f0eksRK5J8vW3f1lk\\u002fdV0QG7MPFN6Kh\\u002fqiEorcWXLN28yfItYITHU2BJ51dFiZkjAvzkai9OXMCO+HuIuUJHsibjgQBYGli1p\\u002fBI+Hus1mKt1F57QzAvuYaJPirCbMD6a62OmXcf1SR7Ak3F\\u002fXdR9rq58Sl9m7k15W8x3E4w41mes7uCFzlMeAJx8Q4JqEZCwDYneU8Igbdwotcck2X\\u002fkWS6nCQtTrTZyrZ3egWAFrjR06yGQB4RLRcfDU6PAg8IlYhucZg2QBAtWufSoz8EmNdM7gsrUl4Fg2NrNjkiGvuX\\u002fDD+3vRgb6VHq9+8prXcgpTFt8wbc5VEyNiekuqB6Otobv8dF3JkYsn95Zpu3r+woPF4xF\\u002f\\u002fuA+qWKk81yDgfMyXN9zjPMcJlzEHqIdrtwNjvF8NKZgyDpgHACA4dHe2XYPXLadwxlxw52kRlgdToruOWaWDK7TwJYT5Z263nlFv\\u002ftws\\u002ffSr0PLbAruNJMie88O2ISigRwz2KwOV08YABBL\\u002fNX8+TRUv\\u002fwQ6ifhrnV0tFrRU8QoYcLquOBKq4IlJmQSnsJCGtjBbgd6Hs7hXE7GxUVxCXdOiFiRnB1Utj0oP9ddcKf1XcJFkpdn9zlf3lteeMzduVP+d+EkO7i7+WKLWZ8gHZqzzWHyReUZJIIBvDBjRSCJVwBIV4T\\u002fasLsQNYRHQ7jw2MXJKTv9+w+f6qzZ+5e4BYk+J4h6LNNytKkTBHh41glRiKLEEnYrU68u74MoUG+9guaDnc7IBcpT7AkycdJu09rU\\u002fNeOXuQHdnZdDHwhPuVvMdxOIOkNvzHSTbIJddIhDMxDDn3o\\u002fXm743mja47UlE\\u002fX5pi4TSz7QAAdOlfEgkKCM\\u002f6WW8Yxo9W\\u002fb1V\\u002fRAAdGqfBaBVsrsB3F8WlNb4cafub647UarnhjwhHqV62mw7QNFqi+1wU+f1UarnxMJp7A1oxmqznzLZdtG0MTb8NfYQZ2Bue2Ll\\u002fk2n1e3BfZ1Vnm2oPNvw7rPrkzJj8qelp+UlJGfFRieGRcapBEJ\\u002ffxU2i8OoM2s6DN3tuq5WbXN1Z1N1R31Fa+ALovrx8F+vH77S++GCR2B4GENraUcJ7plwd\\u002fWZwXhZ4GtaGONe2XhIF3IYANp+mLZuoJ1nGarTVZLvEz\\u002fiK1wwxP2IGKqRtu2iHUWMs5Kh1cDYAZdhRDzOL8BFV+MBLCvNkNWUdQPjOMGQdQxjAsAwTI4RSRg\\u002fFxfMwkWL4dJizj4N8uEcTrBsDt8rFwr5PAwD17zWvrYJUKfOhGPYzfMnnq5uvtjS3akz\\u002feaDn\\u002f73xE3D18NdJO751sjOT8iZ4KPvCCIpzUcR1YAJhXwMwxiGAQC7rWeuc+BG95f3qZ9\\u002fp1pzT1tAtkUxtwxTJxm2MH40knA3ONXsuxzOZSYnbth3eA4nEOFC8b+CScYF6+OKU0jk\\u002ftxp\\u002fhvXeLsjezKS\\u002fqMZ5sfasscK+j+gHy0OitrZeBEJLk3K8lnu3Zc7sie9ce6Iu93KCFuelI0kndsurZobuAleC4S6pCnCRQTPRnkcmE6P6fNoKVGmZCfcu6xDkCDwYzCv\\u002fYIGvZg6PiLWTy8XRKFXj\\u002fUzXUHM+bti9zgOZ\\u002fBoxqQ3f6M3f4NhQiE\\u002fh89LJXAlw9AU3WVzlLiXM1XJ7hTwszwfigqT3e9KuNsd52vb5kiEM3lEDM3YaFpHUu0q2b1K6S3IQ+SSNRHOx9WGNxggO7R\\u002fVhv+IxZOJ3AVReus9iKS6lk7Okz+oELiY7oJSXXRtJZijK6VXSnaQNM9aUGTZZuTrMcxOY7LCUyO43IeEY+k7HlEfELkh83dd9O0weo43dh5DYGHC3hpGC5hGCtJdTrJZgAaAKTipewHhojT6u8Pdv73F9nrS3XbSrVbTWSXnB8zIWz1lPAb2P15Kg37t7a85L77y+wNQsKjDWaAPwcAWiylRd1ft1vLScahEiTkq5ZPDr8e671G0j+JXPT0e\\u002ff94cY3Amwsg2iq7miq9vi6EYoFMoVYIOLz+ASGYxRJUyRFkbTNYjcbbfSlQsIhd\\u002fdTa9bcPQ+Nhj7GgvELGfs+7zbutLMEAHDBZGB8tNbB8GjXDYZqAMYE2Gh0UmUsTt1vaBs6oc0XrN8sUFAYqpnUP03bD6MDtJ6h9ZSznLJ8h4uW8FVvAtbXdFiKNPwfZf7Y9ZHixjBqhlaD8yxl+RowiSD6EIaHsTe4ZJAP53AGQm\\u002f28WkAADiGKSQi12i71pCf0v+ctr5EKKSvPLhmYnp8u8Z4x7++Uhst5+vb\\u002f\\u002fLlrpfuWYVuOkRU4T0fX9kFiY8+vdZzcNhhOCZTiFydXjrb9NlBrg0+ur+8T\\u002f181DZZqpCImJCNV85BgsNB5NXc0E4Ne4cdDmcU5cQHkXTjcIbP7TmTAu8PE6w6g6aku40dITDs9uxJ7Egg0hRh+eExZZ55zH3NNaGc\\u002fjva3mAm0UT5zZkTkIh\\u002fUr5gaWLmproL6MCImOyV+WWv5xmgNEU4GgIAAAwgViJHWqXnh\\u002fd5kBop8jhOMDiCm0UYrMG8drUdrTJLlAVRGB7ntWRxS8Dl\\u002fFfyHsfhDB5+KT3EMHab45zNgabhAIgw+X3RqufQsBepeGmE4km14VUAoGmDybqTPUrTvi\\u002fgRSr\\u002fyCNiu3Qv0YyJpDqNls3sURyTRCp\\u002fHyZ\\u002fmB10a+m+1+ZAp1W5aIzvIJHk6I1iIdq\\u002fSyyckRKzpV39hNVxGgAoWmN1eEyXceETvi+jhoKfmv+qd7ZlyGbzMH6N6djBjg8spHZe9IPuDVKkU29MedlK6o93f6G21\\u002fc+0lO\\u002fP6fCsHd7yz9VgoR81QoeLmw2nzvY8UGrpWxt4nN99N\\u002f3LW9q2iN\\u002fu+Gtp79DBwbEbnXYreiBx3B74Jlrb3hkMRodExgbLiik7fsYZ4ln3M44KwEA409iqHqPIQAAwHgpGBHLUO3AOEnjazzFKLQncuqe7Mm2Y1JCei\\u002fOzwNMyJC1lPkzhmoCAACcp\\u002fo3zs\\u002fHiGRA5+sMCoZHMM6yS3fEOD8XI1IAEzF0J20\\u002fDowZAGjbHtLwHE\\u002f5L\\u002fYD3UjjK5T5f67bGJGE8fMwPAwYJ0M1084LwBgBABdM6itdPsiHczgIdwsaR9\\u002fXPts1RqvDiUYvGZcUXVTRCABnqluWFPZzPd6P1JiwienxABAbLn\\u002f14asffP17B0ltO1mRERdx\\u002fwr0K3tI5BQkbv62CABqKzxOH0ZMZm588fEaADh\\u002fpn7usnx02K9R\\u002f+W99ZNwV9vRLi5p0vEjs5iMAEfTPY5LRRkczmUpJzYSDXE4Iw7HsDuyJ6PRobOzCS3xnhSVgKyfGaCCiFgk\\u002fXdO3e6kKT4+on36AlfU0YhE+DgxOy4FCfZrfnzaaCXco8Ro0ZaTpqykU8zzqIv0Q8Lj+7mcEyYSIwn3ZHmfZ0cSzye1kn0e9Q6Jwbx2nQ1NzSuCKTD33jjwNYqv5D2Owxm8CMVvxcKZZusem6PESTZStJZm7BhGELiSz8uQCGcqpDcIeBnow\\u002foQqfydVLRAZ\\u002frM6jhFUu0ANI4peESMUJAnFk5Bt75EJbtHJl6tN39ptu5xkHU0Y8RxpYCXKhUtVkpv4xE9pbXDRMDLSI752WI7ZLRutdqLSKqdoo04JiSIKAEvQyycLhMvE\\u002fJz0YeFDKOz886090WEHABmRN7+Vf1jp9XrJ4StUfLjXBuICFmSZCIAnNdt95Nw9\\u002f9zrJR+d9t\\u002fEiQTbkj+PxwjAACiYFvr\\u002f1Xo99YYj2XIg7swufquuTar48O\\u002fb0QHQp5ELvrNv2+bu7oQHRgjGIbEBVMBgKG1DNWMET3FlbSzDIAEAFwwibJUsx\\u002fiRkjvJw0vAgBl\\u002fpih1Tz5H7ERvBBFO4po204AAEwiiNqKEZdmBwoXEZLbHeobGecFABqoZkx8be\\u002fDhgomJqT30c4LhOQmXDDbo089Y3TqfkvbdgMAZdlAyJ708bYwZsr8CQAAEHzVa7h4jecwRTtKaNsWXNjHnIlBPpwT8vhE73GmxeaQ+O2UNSTkl7qHd+nNDpIS+GoBf7isDg2xzMhJdiXct5wof3TtnCH5nSekxT13+7JnPt0OAG\\u002f\\u002ffCQ9NmLRxEAPPwI3eVYmjuM0TVeca25r0sQl+S6TGj6FMzNcCfe9m8\\u002fe\\u002fatlYgnr86Q\\u002fo\\u002f7Le+sn4a51dCKRREk2EhkmQlyMRBx0oKeXHM5YJBcL48MUrVqPTkoczgjLVkXFSNDE4hA60laPRObEBp1xdhkXhmYZnDRVrVfnesVDxFnPQmMAmByVIOUFcRjhMjkqAQ2NFO\\u002fMLwA4aUoMgR5HRorR6WtsQs927TwcDxOiBwNuAtbxNwCQTJ9FKENiMK+dvdyoi5MO4rd1UOjGAl997X26kvc4DmdISIRzJMIhm90rFk5DOqEHgkdERSieiFA8gQ74lRLjUQ4\\u002fGBLRPIkoiHSVRDQvJwkt2\\u002fKWFncIDQ21XOUSV5YcAISEbIJq9cHO\\u002f1Ybj04Jv8Fzw374\\u002fzlVhgNO2jo5\\u002fLqebDsAAIxTLKrQ760znQg24Q4ANzy8WCQWvPfcDwPrLTMqMsYnPv3evfGpY3rCLo0LJgJgAAzjPOdOuPcUvONKjJeONC1xI6T30vYjtH0\\u002fANDWnxy27YToalyyDhdMDmqKw8DQth2uG4Tkjt5suwsm4smecGofAgDKtoOQPeYxOkQI2aM+UpIAgMn5qtcdnfMYWgtA045jhPg6ZBOGrHE16sH4eV7pcgAgcMFkXNBnMdAgH84JfSIBTyzgu8rJq1q7h3XJUJeU6DABj3CQlJOk9pVUr5iSg2xgd5Kf7j6NBNmunT3+\\u002fa3H7U5Sb7b9\\u002fatdL96zCseG4HNg9fTc2nbNRztOMAz8+ZNtn\\u002fxuXXbCEH\\u002fkRkQrFq4q2LulhKbp\\u002f\\u002fx14wvv3c3zdb1h+Ky8fuqX7+5z2J0GneU\\u002ff\\u002f3xD\\u002f+4GcMDfetG\\u002fZf31k9TORtlRiJS3gitBEJg6Mkk08fXG4dz2eDauHNGnXffjKF1To0mnTOUEUgkQD5TscO6cuYgXdR1I5Es1UBee7I8DElMjxhs0AeLCr6\\u002f2mqBZ610hEji5\\u002fl4rLwGAFCuxYmGzWBeu3dRf+DtaABA67ngKgBEiCRIpC9X8h7H4XA4YQKP5GOEMBUANHZ0wlm\\u002f\\u002fP+cDmsVAPzU\\u002fJfXype7\\u002f7ex6VkAsFAe07YCt\\u002fquuf\\u002f49rGwqJ4sfyiTKsQP\\u002f\\u002fX6\\u002f2z+3RjPtgMAA5gc42UAAM3qH0U7zwEAzi\\u002f0mzrH+eEfENJ7erZhHJR1vVN9k6NzIWV6g6HQ7+KhxZA9a8zgvqabYJeCDOmvJne4YBJcuKDntu\\u002f34dIxLdUOjN1jJCCDfDhnDCjM6Dk\\u002ffXPTYa0JPSoecgIeMSu3pzzl5e\\u002f3lzd5VCG3qg2Pvf1jc7eOHUSEycQPrOxp+bL9VOUv3txQWt\\u002fuuQmoDebtpyrf\\u002fOkIEvfvsbVzFk\\u002fMBACrw\\u002fnr9zZpjBZ0i0G794kVyjApAJwtqvn9PR+WFTcgG9htzuLjNe\\u002f9c8urz25AhgZPGSa99aGFrtv7t57700MfV5xr8tgCQNtt3L\\u002f13Mf\\u002f8WjN5zK6v7y3fs7YnV6rgkiIEUq4WykTEuEPaa8xDicEFabEnWvsORBhT57icEZMjmoYT5Y6LCbvPGNK3z1D\\u002fJPzfXwpDPfKmQNmdjq6bejvliYfyEw3AsPipfI6wwDP4UeXlB9ERT\\u002fSNGbsylCi\\u002f9Dey6j6gTRyAYB4SUAHY1fyHsfhcDgAQHiuBCvmKQHASQedofD\\u002fc2y0CQCmhN\\u002fgirOp+AOflDZ+RsZ7e5\\u002f+799+3P39CXQsNAiE\\u002fOW3zLzjyVXKiGGcHDnCcH4hRVaz27i7buOCQnekDzye4jlCfD1p+g9t2+eqhWeoJtL4OhjfJMTXEPJfo+XnQ4W51FXP56qkvcFRmjBB9GRLGQbN8AAAxs\\u002fB8HCG1jB0l1N7P0\\u002fxd4yXhm7Ut0E+nDMm3Lpw0rHyBgCoaOq85i8fT85KjFJIbU7SaLF16kw3Lyi8dlZwzb779eCqmUcu1JMUrTZa7vjnV4UZ8akx4SRFN3ZqS+vbKZrOS46JUkoPlPZc6\\u002fJ2\\u002f4oZ1a3qHacrAeBEZeNdLzeGyyWJkUqxkG+xO1vVBrXBDADZCVG\\u002fujqIWXQYBi\\u002fcs\\u002fLeV76tbO5q1xif\\u002fODnD359o8+mNwMWFat89vXbnn\\u002f0c7PJVl7S+Nu7PlCESRKSI0Vivt3m1HQZO9p0DM0AwPT5aO3\\u002fkLjlwQX11R0Htp0DgLNFNU\\u002fcXqMKl8YlRYjEfKvF0dGq03YbASA9J+7eXy9HHjvqvzyin4Q7DgSN1pUPbwWZm4UyIhHvru4czmXmgYXTHlgY9ERjDmcIqXwVsQ6VJpMODQFcs\\u002fVTNDQIw93Ie8A6rT7OMZLlKjQUmGjxECfc7RRZqeuu0aubTfoOq1FntxocdivptJKkjXLaKNJGkjaKtA367RUGcylxZJqDj8BrnxyVgHkeP9UaNM0mfYBLpx5sRUvS5sSnIhGfruQ9zluT6cD+tj+wI3dkHsE850mMpEbTvhL1hyZni5Qfkx92V4ZiNboFh8MZNKfnGmCuu3w80ElCbv5\\u002fjhCXAkCmYm68eIiTPoow6W9fu2P5upmf\\u002fmtz2Yk+MzsjTxEmXXP3vKvvnX85pdpdMEEhWNfTzlIAGgAH2sCQDQCA8QNqS4Lxx\\u002fPD\\u002fstQrbR1PWXZwFCuaRAUZd1A2bbyFH8mJLcjDxk8jJcIDgBWqTsbQ9a4brib5Iw4\\u002f\\u002fUTBKH4E6n7PQDQ9qOOrmW4cD4hWYcLF4Pnha4+DPLhnDFg3vi0h66a+cHW4wBgtNoPnOv5k3Yx24Z+ZkN+Sszf71r5\\u002fBc7HU6SZpgz1S1nqlvco+NTY199aO3Go2V+Eu4YBv+496qcxKgPtxdZ7E4A0Bgt3gXpA5hAKxbwX3\\u002fkmjv+9bXaYC6pbX3h691\\u002fu3MFutHgjJ+c+p+vf\\u002fHvP693VZcbtBaD1sfMsMjogAqAgoVh2B\\u002f\\u002feXN6Tuw3H+y3WhwAoNOYdRq0xKevt250f3lEPwl3AS4iKY9TKe88+DAxkTokouAPcBI0h8PhcALks1H1UOnwlXQeWo5gWmOPJLUNPcCCQbzbA34golLXta2hcn9L7XlNO0mPRN+2oHLoAryfo5TBGMnXHiuRT4pKONPVe6QOAB+UFf1tBlqX4a3RqNvZWIUEFydkIBGfruQ9LsTpHfUH2\\u002f7sapaodzQc7fi7UpAaKRriVB2Hw1E7PE6zu2y1ABAuTGYHA+H\\u002f58SIsi7odzWZS4Y84e5SMDPz3xueKDlSteGDfaf2l9PU8H5n+SEQ8qcvyV943ZTpS\\u002fL5gmH8jh5FOL8QAICxMGQNxsuinecAGAAsgAr3XhgRT8geJ2SP045jlPlT2rYLgAHGRuqfBYAhz7njopWU5XsAoMyfEpLbAfdIJFGmty9ttpQdH2oM7Sxh7EdoshLIFobRAW1iwA6MHRgHuq0nQnwDholJ\\u002fV8YuhuApu37aft+wFWE+FpCchvGy0Qf4GmQD+eMCb9YPWtWbsr6Q+dKals79WaGYWQiQZRSlpUQOSFtWBqirpyaU5AW++2BkhOVjS1qg83hDJNJMuIiVk7NWTMjl8DxtFh0AisCw+De5dOun1Ow+cSFExVNVa1depPNTpJiAT9KKcuIC5+albRsSjb6sADEhslfe\\u002fjqB17\\u002f3uEkfz5+ISMu4u6lU9GNBicxNfL1Lx8pPl5zZHfZ+dP16i6D2WgXCHlhEbLE1Mj8ySkzF+amZsWgDxsiGIatu3\\u002fBVTdO3\\u002f1T8dmimrqqdoPO4rCTQjE\\u002fMlqRnBE9YVra\\u002fBUF6MMuGd1fnq2fr0kpT4lk2M2kgX13mFhIg9qOrrQTJuBW5RoUBqCiq6uiu0trs+ltNoPdTg91x9u\\u002fLlqMhjgczpgiJoaxGMTgQLtbXDl8FgKLBvpuB9WYxaddTRffLj12thv9qh1uQS0ZFMy2QRiV137PuClIwv3zyjMLEtKXJPo7D7RR5OOHfkLWXJ0Vm1IQEcuO9OVK3uNCXJP5ILI0UZPpAJdw53CGXIV+z5TwG6S8cAAgafs57WYMsMzgVzH1\\u002f3PGKRcf6frkjGbDOOUiJT\\u002fO\\u002fSgLqRUQUh422K9sl4lzsifOydZ0GvasP3F4y9mLpU2uefEjICEtqnBuTuHc7CkLcsWyobnkH7Iwfg5gEmAstPMcwctinOcAAONlAjaQfvq4YBYumMU4zzt1TzJkNQCQhn8QojWABzTFLUC4cCEumEo7TjF0p6P7KkL2C4yXi2EihmqgzB\\u002fRjtMAgBExhPQh9JFDhLbtII3\\u002fZMh6dCBguOgqgXAhZfmKMn\\u002fGUM0AALSOMn9CmT\\u002fFRSt4iqf9l+cP8uGcMaEwPb4wPYjc+oMrZzy4cgYaDUZChPLJ6+ej0UuWTsoqfvs3aNSLUiq6fdHk2xcFNEXGJZCfXJAaW\\u002fT6r9CoL98d+jMaCtikmRmTZgZU5eMyaWbG9tIX0aiXD3\\u002fu59W5yJXi6+6cfd2dQX9luwT7yw+HfhLu4YLYLnszO9JsQcushkOtuZTx6l0TI0pBIpwAnWlr+\\u002fD0qePNTTrb8J57cwl3Dofjh40k0dAVw2chsIQ\\u002fwIQ7D8PRUMBazYanjm493FaPDlwBRvG1r0nNfa+siN26nQF4eP+GpyYtuGfcVIGvTjsV2q7fHdlyXuOxyBIG8Oepi9gRP67kPS7EWUk1GqE0SITD4QyehBf2Zd0vM+VzRIT8ovGwxtE0NeJmd06cZqg2a7mDtjhoi4nsBoBKwwEpL0xASMMECTJeZIA\\u002fR0QoVsb\\u002fYUvLC1\\u002fUPpKjWCTjR1pIncbR2Gw+d1\\u002fmpwr+UJbRhUcrbvrl0pt+uVSvNp0+UH6+qObiuab68laS9HGYMWARMcr0\\u002fISM\\u002fMT0\\u002fIRxk1Oj4sPQLS5nBM4voB1FjOMciG+gexq4T0K3CgbGH8+P+MrRtRRoAzAWyr6XEF+HbjQoGC\\u002fsfafmXsZ5jqFaXXX0nsNp\\u002fLD3MHxY\\u002fh0p07uk8WXXbQwPw4TzcX4eRiQApgRcgoGYsnzsKsDvByYhpA8Q0vtp+2HKup627QTGDsDQtu0O+2F++Ae4YCb6ELZBPpzD4XCGWj8J92hRcqXxFDtSby5jgMH8rdA9BCoMJ9EQQJJkJLraX2Zajcand+862FCPDnA4HM6IG\\u002fJZNWOIz1dODDRvPuAHlnS33bv3e42v\\u002fjZukSJpujI8QiQJF0rCRWIJTyDh8aV8gZjHZxjmsYOb0AeMEaP72nEMe3XOmmu2fmqnepPgJE2\\u002fdHrfB2UnFidmFEbGR4gkIoJncNrrDdqj7Q3H2xu8\\u002f2yembpkfHhA5e1wZe9xIU7CSuS5SHlDmZLjcDgusyLv6rbXnddtM5NqOT96fszDU8Kvd4\\u002faadN3DU+yNoc97f9x3ZgZeeesqDvdcf8\\u002fBwAy5LNuS3vrZPe3tabjVsogIuQqftyc6HskPBV7syGkjJAtvn7a4uunAYDTQTZd7Ght6Gqr725vVGu7jQaNyaAxm\\u002fRWh91JOinSSdIUQxA4wSd4fILHJ\\u002fgCnlQulodJ5CqJTClRRsii4sNiksJjEsNjEiMu+zJ2\\u002fzBBITiKaOc5AOipcOcPKuEOABgeSQgXU9aNAOAqdR9aGB4miPjK0X0NQ9YAJgSGBAzHMBXGz8VFKwnxtb7XUx00xllBGl9x3Sak9\\u002fDkT\\u002fl4IiyoXskYLpyHC+cxtJa2fEWaPgDGCIyJ1D4qiN4fwDyDQT6cw+Fwhkw\\u002fCfdUaf6hrg3siIUyXjSeyZZPYQeHlsbRVqo\\u002fjAT5uIBLuAfrQlfXfRs3dJrR5QU4HA5nVAgJH18629feF1Rfb\\u002f8iRFI0FBoEvl6jheynqWVfvCeBBaLOoL1z97c+24xEi2XLk7OWJ2UXRMSG9b1wrsbusWrcGBIKr31cWNTbC659ZP8GpGV8t838XfW576rPsYM+\\u002fWL8zPvzpqHRvl3Je1yIS5TOL+5+191VhsD4qfL+G\\u002foPwJnut0nGCgB5qltl\\u002fAR0mMO53NFAzoi8bUbkbegAAACICeVvcneiUV\\u002f8\\u002fxyXSGHaqoQ\\u002fotERwRfw0vMT0vO5fXxo4PxCCoAhyxmqiaE6AAAXBNEOok94eM8NZiinI\\u002fRgbE71nQxZg\\u002fHz+eH\\u002fw\\u002fARasZL2X4CoAEA44\\u002fnKZ4Fn3WZjB6NBADDwwjZo7hojUN9HdA6htZStp2E+AZ0uz4M8uEcDoczeD7OxNhSpXl8XOCkPTIC+zu\\u002fH9aE+56Or2mvL6Es2WQB7nWxlNO3Rr3+1vXfGe1Dv2Qzh8PhDIyY56ODSphQEiORodHLjojn4wvXOtCOH0Zn0J\\u002ftNMM8fmiTd8ZZKRD9pnDe7dmFQ5iEDTWh89qXJmZ+tfzWxw\\u002f+1G7xWCCnXwqB6KWZK9ekjkMH\\u002fLqS97gQpxSkzI97qUT9X5OzRSFInhz5mFIw9I0TTc6WMu3nrtupsmVcwp1zJRrI5WlfhurncMYCzNVAhnFQlm8BADA5xhuCRsDuwnaMCHSmWuAo80e0sxgA+KrXRyzbDgA9DdMBcMFU39l2ANrh0TIhKBgvhRCtpSyfAwBDNqHD\\u002fRnkwzkcDmcwfJz\\u002fswlwUa5i5jndQXawyVJ5SrNravgydnConNHuOac7hEYBJoYtQEMcv57auWPEsu3xcvns5OS5SUN\\u002frsjhcC4n0b7SfFq75UpI\\u002f4ULJWgIwBR83tzF4Aj6gT\\u002fXl5eqPbqBA0CcVPHF0nUZyggkfpkJqdc+PTppzzUPvnL20EflPrrneVMJRbdmFd6bOzVaHPRuciXvcaEvWbYwWbYQjQ6pVssJNMThcDic\\u002fmB4NEbEMVQbZfkKAHDBRAC\\u002frfwYC2A+DvPYaMdJ2n7EdRsX9rkM44DRdleHAB7GS\\u002fUcGV4Y9NREMjS6NokLZd0wmMVUAQAYk+v\\u002fsYGtNDvIh3M4HM5A9ZNwB4Bp4cuRhDsAbG79IEqYmCLNReKDVGU8s6nlXTQKECaIyVUMan3hK83+uroTLT1Xm\\u002fuCY5iEz8cwDAAGlpqXCgR\\u002fnDtvTnJyqmpYFmDhcDiXmUSpjx6ObRbjuLCRq8QZLdFiH503mkwDmWMLAN7F2v36srIYiWAAb867OqiMs8U5wB44oyvUXvvR9oaNdWXsSKo8TGO3up5CIRAqhaIYiXxyVMKUqIQ5cakiX51hAnEl73EcAGjjEu4cDoczIBi\\u002fkKHagNYBAMbvp58MafgH7TxHiFbhwtkYPw\\u002fJsTB0N21ZT5reAqAAABetwHjp7A2GRk\\u002fndJI0vERI78WIOICRmL2HCQrAuh4AaNsehryI8bJYgzRl+Y40\\u002fIUV8YEyvc1QjbhoDS6YDhiyeABNWTdQ1p9cd3DhPM9RgEE\\u002fnMPhcIZP\\u002f6dwqdL8NGlBnbmUHaQY8tP6v6yNf2RS2CJ2fMBohj7YtX5\\u002f53fezWQAYGH0TcO9TOtl5rNzZ9EQAACEiUTrxhfMTk7Oj45RCoU41vOuZr\\u002fxurul7LrxBS8sXuK67aBprdWqsVrLOjuONDbur68zOXozDmaHo06nu23CRHfEj1cqfooXh82KzEmVcuf5HM4VKlkeJubxraSTHTzb1booYQgm6oY4hUCkEop0do9EeZ1Bw74buEajDg35ZXTYT3ehV2GXJ2dPjU5Egv5pB93HfOSF2mv\\u002f74UTL53a625OEC6S\\u002fGPmyhXJ2exthsqVvMdxGKDbLQOfxc\\u002fhcDhXMlwwibZtu3S70GPMB4ZxlpLOUjACAIHxkjE8HDAxMHaGameoZndPIoyXzVO+6PHQIUJIbqbt+wGAMn9MmT\\u002f2GMNEGB6F8ScQkpt8FtczdDfQWoY2AmME2sgwRobpOeahrTsYsgHDZIDLe\\u002f5LxLOTSIToGsr4OkNrgbE4utfiolU4LwOAx1DNtP0gQzUDJuXJHyWNr7kfgmAYM2X5nrJ8D5gQ42VhvFQMUwDQDN3NOM4xdKdrM0JyG8bL9HwowKAfzuFwOMOn\\u002f4Q7AKyKu+e9mqeQVLiTdmxofqPSeHJR9LoY0cB7iTDA1JrO7e74qtlShY4BAECCOHNS2GI0yumbwW4\\u002f3NCARgFunzDx6fkLxL76CAt5PPJSJt1KOgm8Z9KcGMfFcnm8XD4+Onrd+AK93fbBqVMfF5+xXeo7\\u002fNGZ0xqL5ZWVq\\u002fxfD7GQ9g1Nx2mGfqNyS6w4bHZkzrWJ07Pk8eh2nBFR0dpV0dpz8AEA107NZw1yOMOIwLC8sOjTXS3s4JnuVvbdy1imMvJUp0fmt1rXzb4boA6LSR9khXuFroti0Aa0V6UE1xAcAILtPB4KQuq1b66vePHUXvfdCJHk2xW3ZwZTaB+UK3yPu8KpbRUOegj+aDmcMWpKxE1TIm5Co8Ebqp\\u002fDGVswfqH7Jt57uw8Ye8V1iiHrGKhjRVwwQnIDT\\u002f5nGJ7GJrhwIS5eS1t\\u002fRgcAgLExVBNDNdG2LYTkRp7y\\u002f5AOOaTmIdp5lh1xo8wfIBF+xHe4YGrvfVzBC3vPqX0IaD0wDtq6ib0uPEbE8lRv4fwc0vgf19qq3jB3WTpjZ5znGed5j2EAAIKQ3s1T\\u002fAkNA8CgH87hcDjDx0fu1VucOH1+1A37O79DBwDK9Mcu6I9nySflKmZky6co+IGeNNIM3WGrrzGfO6XZqba3ocOX8DD+tYmPcuXtQSlqbnaXq7s9PmPmE7NmI0E3EY9nvpRwtzg8SuHYlELR7+fMXZmZ9eBPGzvNZldwY0W5XCj86yJ\\u002fF0VK9Y000\\u002fMrtVu1G5qO5ymT+k24l+jq2XczZXFSHjJNjDMQPxeXf3LwtPsul3DnjKR58WlI+u9ER5PeYVMKLv9lsQsj45GE+6muFjtFCoNsGFKh7b1gFqAOX8nibFUkGupPUUcTGgp5ofPaLaTzuaId7Mhz05YOX7bd5Ure465wXD8ZDofDGTCcPx6AB0BivDTAVeiwJ57iz7hoJW0\\u002fzDjPMVQzQ3cCYwWGBEyM4SqMl4ULpuLitRiRhD5yiFCWL0jjq0DrAAiMl4LhMYBdWjWdcTKMkSHrgDEDAGVZj\\u002fHyCendrEcPFi6YJojaQZk\\u002foe37GbIRwIlhKoyXigsXE9I7AJMCAMbPZpwV6CMBAICQ\\u002fRoTTKdt+xhnKUM1MbQOGDtgBIYpMF46JphOiK\\u002f104RnkA\\u002fncDic4RPoSf6i6HWt1uoq4xl0AIABpsp4xjWk4EdECOIjhD7W3b5oLG6z1VlIo4Uyqu2trdYaB91\\u002fgd7q+AdiRalolOPX2Xb0AsbkuPjHZ85CgmxCgnDftjj7TLi7FMTE\\u002fLDu1qu\\u002f+kJr6\\u002fkX\\u002fLzkbGFs7HW5eZ4b9qo2or\\u002fSOEUCEvH2yIn32Hffm\\u002f7IRFUqO8IZGJNtIC37OZwhsSI5+\\u002fWSw+yInSLXV5fenzeNHbwsTY9J\\u002fPCCRwrMSjqPtzcuSAjuNGBPczUa6o+N6pmWxBYhkqCh\\u002fhxu867YCnWh89p3NlZpWH1peDi+OvhC+2BdyXvcFa7NUoSGOBwOh+MNVwjjatEgJhLG+Zh\\u002fz1M8zVM8jUYBcMEUXDAFjY4IyvQ+afwnAODChTzlCxjhs6aNIg0vu8rVKet3SMKdH7mBfXcAMDyaJ38K5E+hA5cIIreiIRZcMAsX+EtW+DfIh3M4HM4wCTThjmP4zcm\\u002f\\u002fbj2+Rarv\\u002fN8g1NtcKqRhu8uR7o3oaH+zIu6fmr4cjTK6U+tFm0K\\u002fOj06e527T6JWH1m+k24A0CCQvHqyqvu27jBPUv\\u002fuX1756WkRkp8pzBqTO3su0Kcn8Z1ch89JlvPbAYOZ+TlhkUXRMSWqj0+Ez6uOHVrdqGEd6kY5zI1Ny5NSPDsnvnf9TWlQSXcGYAdTT7OAP2T831MD3I3BwvQ4bb6Cm0XGg15ofPayzQd7LsKvpB3qYHb8LmS97j+sQ6NKMbRZDrYajmutV80k21O2oIDT0AoRERYpCg\\u002fWjwxSTqfh7ObBoQ0M9neZfOeVs\\u002fhcDicywtjJU1vAgDgSn7Ym65ycl8InvxxyvxfAIYhB1tAwOFwOJxABJpwBwAhLrk3\\u002fW9f1L9Yby5Dx4bB7Mi1y2PvRKOcADTq9ey7Ej5\\u002ffmoaO+It2IQ7ACxITV2RmbW9+qLrrtnheKvo+F\\u002f6aCzTZtWy76bJonFs2LMMnL4YuQp3zqi6L3fabw57tJhsNulfPLX3xZkr2MHLj4THX5yYsa2hkh3c0lDxa\\u002f3cwPuKbKg532ExodH+hIl8JAqbTLpkuQqN9oFimNfOHkKjY0HovHbkyrfGbt3RWDVMy6WyXbF7nH8ExscABwCGoSr035VqPrFTHodPNJAkabOQnRp7ZZV+g5BQjFPdnB92N+Gepx8AinF8Ve1jhTpvq5M\\u002fCxcO8I+BZkiDo0HjuKi1u\\u002f5XZaN0yDY7mh9GIj6ty9glwOVolMPhcDghiSEvAmMBAJyX23e2HQAAMCEADkABxvWU43A4nJEQRMIdAIS4+J60v2xt++iEumfN7uGAY\\u002fjK2HtnRa5BBziB0Vh6Z6wDwMTYOMJveTsACFkJd7Mz0PLnR6ZNcyfcAWBD+YU\\u002fzpvPzt27ddsN7Lsqgd+jAc4wM9sD\\u002fSfmcIbDNWl575cdR+qFv6wqHhcWdWfOZHbw8nNH9iQk4U4zzLNFOz9fto4XwGVIs9Px77MH0WgAMhQ+EvqHWuvnxKWi0T783+l9SCvwsSJ0Xrt3iv\\u002fRAxtvypywKiUnSxUZIZQIWO3dhtCVvMf5wcelAGCn9Afa\\u002fthhLUaHvdgpQ4n6wxbzsYVx\\u002fxTzgl4DYPjsbP5Fl+08zQRULcHhhKxS\\u002ffHP619Bo349kf1yvDgVjXJCzKGuLT+3foIEH8t6MVkywEuMHE89R48M0081Bm3dAkABAM7LQsc4IYDbUzicy4+P3Kh\\u002fBMZbG\\u002f9QpqxwS+t\\u002f9c5udHjQIoXxNyT+OpH7WBkEo8OjfjleLmPf9UnEmldutAda\\u002fjwhJjZFpWrQ6Vx3TQ7HoYaGZRkZHhsBAIDeaWHflfN81BtyRoyRaynDGVU4hv1j5qobt39OMe6uVAAAzxbtbDEZniycF1TakaTpPc3V62tKlyZlrsuciA6HmDlxqZOi4ou7WtnBY+0Nfz2x+28zlvu\\u002fNGqjyHv3ft9m9rh+GaAIkSRHFVWp80i5fnXx7EP508P762bOALx+9tB\\u002fPbvPjyGh89pXpYz728k97J5CJEN\\u002fffHs1xfP9m7kRcLjKwQilVCcFxY9MTJuaVJmglSJbuTXlbzH+cHHpSRt3d3yuMbucQ3Mv25b2e6WX1+V\\u002fBGB+WhVNCp0jqjFhIMAAQAASURBVFou287hcDhXJoyXAZgEGAvjPE9ZviAkd6BbAABjp6zrScNLrnu4ZJ3nMIfD4XCGRdAJd5dcxfQM2YQj3T8d6\\u002f7ZSvVzNTVAIkI6P+r6WZFrecHM1R1We+u8lk8ZnMVpQTTqHTA7RbHvKoX9zxqTCXrfc4Pd7qCoAE+\\u002fZyYmuRPuAHCqtcVnwt1OeZwKKvj95Dg4w4pbNJUz6iZFxT8+ca53m473yo5vqr\\u002fwq4LZV6WMU\\u002fX92cUA1Bk0x9objrY1HGmv19ltALAwmE7oo+jpKYtv3v6FR+IT4PPKMw1G7b\\u002fnrI4W+75Eery98S8ndw2mjfjy5Cwk6Wxw2B7Y98OHi28MF\\u002fZ5EfSivvuFU3sPtAzxt+EIC5HXHi4U\\u002f3Hywr+e3I0O+GUhnRbS2W4xVmg7N9Se\\u002f+vJ3YsSMp6YOLcgwscC9X25kve4vhC4qKjrZXa2Hcd4MeJJKkGGiBeOAW6jtN22si5bKcN4HFbpHDWnuv4zI7rPpeHYcCAyFKvtlN5G6eyU3k7pHLQJAPkA4HA4HA5nQDAxT\\u002fYIaXwVAEj9c5T5M1wwCyPiAOMDY2doNUPW0Y5TcKn+HRddRYiv9\\u002fgJHA6HwxkeA0y4A4AAFy2Kvnl25Nqz2v3F2r3+F1P1L0qYOC18xaSwxSIitPKwD\\u002fy0EQ0NTu2vn0RDwwCpkSRp2jPgg1zgUajVYTYlKQIqoBsfHfMtlLrvVnT7TgaRnierAnzgf3icweMWTQ0FJqf9gqbT4LQbHXaj678Ou8Fp673rtLdZjMijijqaFm\\u002f8QC4QKvhCmUAo5wtlfKHCdUMgkPOFcoEoViLLUoZQu4O+\\u002fHrCnDqDZmMtuihIm9nw9PHtzxTtGB8ekxsWEy4Sq4RimmFMTruFdHZaTDUGTb1BY\\u002fNcenQMmRadeE\\u002fu1I\\u002fLTyHxg611c354d0li5tKkzHipIlwoMTrtWrvlXHf7vpYa9nqbBIbdklX4ZVX\\u002fTTDY7sud9tGFU2bSY\\u002fc\\u002f09Wy4qcP7x03dVlyVqYy0vX1QTJ0q8lwvKNxd1P17uaLNKssemJkXKxEvqMx6FVbR1fovPZ7c6fyceIfZ\\u002fYF3r0NQTPMnubqAy21v5o45\\u002fEJc\\u002fzPimC7Yve4vujsNTp7jes2gQnyw+7MC7vN1WeGzehsOdn1Sov5KDtYbfipIPxeCS+KHfQJw4jZMc+yIwzQdkqvtlXsbf0NOz5gyxPfoT2PsgDARmn3tPyaHZkd82yYsP82At7vAIfD4XBCGSF7lGEclOk9AJIhqymyj7QMJufJHiZkj6BxDofD4QyPweY9hbh4RsSqGRGrtI6OatPZWtO5Vmut1tHB9Fe8I+Ep4kXpabLxOfJpMaJkdJgzCEqRSG3pbeGiD6BFTKTE41JHg04XYMI9XOxRENds8N3rgIcRDqb3XN1M2liDnBFFM4zFMcBED2cIne1uu2PXN2i0P3aKrDVo0KinGTHJ3664DY2GpFfmrCEw\\u002fIea3ot2bjTDnFO3n1O3owOXhaenLCpTd5zobELiTpra3li5vbGfBhd\\u002fmLxwbVpesAn3MKH4icK5L57ai8S7rOZ\\u002fFR\\u002f4V\\u002fEBHo4rBCKSpo0Om8+v8DRF2MeLbzra3jDIpPPIC53X3m4x2ihnglRRpRtUUz6SoV87e8jidPxpyiJ0rG9X7B7nn5BQLE14q68FS+X8hEXx\\u002fz7U9myDaY87SDNklX5DYURAy5AiMMBFRJiMH48ODJRK4GNmoYXsRCJyfmJfr5HDCQUxoqSF0deYSaOZNJgpo4U0mEmjlTL3e1LJ4VzxMJ78SUJyI23dSNuLGKqOoXXAOAATYZgUIxIxfiYumIuLFgHmexolh8PhcIbDYBPubmGCmGnhK6aFrwAAB23TONoNTo2J1NppG0k7aIbi4QI+JpDw5Ap+hIofpeD7WMQs1ORG+qhdwjAQEDyVSGRxOk+0NLvjSqFIJRaJeXyKpnU2W7fF7D48XJGROSkuPjM83L3xsFIIheyEu87msYaqT9Eyj2\\u002ffiq7uuckp7EhfVCKPhDv7edkkhNBB9ybcNY6haUPEGQCz3eHZxZfDGTUEhv17zuocVeS\\u002fzhwgmf7n4lw2+Djx4eIb7tnz\\u002fZngl+L8ZcGsh\\u002fJnAECESKK2+f7I7cuDedPLNZ0bas+jAwAAQNK0pu8fmKOK+mLZLeEiyey4VGwMdsQY9dfeaTX9u\\u002fjghtrzgUw7C9D7ZUV54THXpOWhA324Yvc4PzDAF8e\\u002f6j8TjQE+M+aPbZYTDrp31lGb5cTAEu4cDsenaGHCVXFo+2kGGAtpNJPG\\u002fV2bTmn2IaMcDscNI5IJ2eMEl1HncELe4ZaGOzZ\\u002fj0a9SHj8C\\u002fd7zFbkjDlDlnBnE+CiWFFqrCgVHRhrttx+Jxq6pEGnu\\u002fPH9QAwLjLqwSlT5iWnIkXiZofjWHPTR8Vnjjc31el0zy5YFC+XszcYPmEiUR3rbpVazbrnW4pSxb5b3N7GvusHsgKblfQ95TxcKNM5ze67FYbeCxWcEcb1k+GEFAzgofwZ8+LTnivaebJz4J8MEyPjxocH0dJ61CkEoi+X3fLciV3fV59Dx\\u002fogInh\\u002fnbHMvUxlfnjMwVb2h31A\\u002fjXnqkix9IOyInTAr7Wpuf83a5WULwCAcKE4NzzmAqvFzVgxiq99b3PNk0d+drU+dxPz+IWR8Xnh0SqBWMr3sXqNnaIcNKW1WdosxjJNR7NJj24B8M8z+1cl5wS47ApcwXtcX7KU10aKxqNRLwJcnqFYXa7rnZaksVdRjJMImWWHOJzLEgaYlKeQ8hRKfhg6xuFwOBwOhxPChiXhftlzUNRDmzc1GwwrMrPeWrWawHF0CwCpQLA0PWNpesYze3d\\u002fVXru4Z83bVh3Kz\\u002fgU+LByAyPONPWmzFvMxq1NluYZyk6Ij3M4yj2WFMjRdM+XxdCa\\u002fUon2e3u2WLE4fXmnozFN12Y5m+KV+ZxNrkskXRDMMwPKL\\u002fN3NkcAl3TgjKDYv+fuUdh9vqPy4\\u002fdaC1NvAS4BxV1IKE9BsyxueofExICnFiHv\\u002fl2Vddm5b3r+IDJd3+LnPyMHxVSs4fJi9MlCndwYKI2AEk3HkY\\u002fvSURfPi014\\u002fe+h0APX1BRGxTxbOW5Tg0bNiXlzqAJLOo260Xvvu5upH9m9g\\u002f1WLCN4ThXNvz5qELKDiX41e\\u002fa\\u002fiA0hPm1azYUdT1drUXHawX1fmHucLNj78LjTWh1jJVHbCnWacJmerUhDQdEAOh8PhcDicy0+b2XikuaG0u+N8V0e31WJ02A0OO4HhEj5fwudHiCTJClWKQpkTHjUtLiFOOkIVqBxOiOAS7gPx7fnSi2q1mMd7acmyfrPST89b8HNVZVlX53dl52+f0FOZOKzyoqPBc1G00o72+SmpHiFPKSqVTCAwXWrtrbPZ9tfXL0lP99zKh\\u002fIuj1VSFULfuYMMWeyRrnJ25L\\u002fVO1+dfB+OBb7e29iz4WTZRwdONWl0JEVnxkT8cunMFRP8TVp\\u002fb09wdZcD06Hn+vkEZOfVD6ChITU3LrX+rj+i0RFXdfvv0dDomRuXOjcuVe+wHWqtP9PVXKHtajbrNTarjXQCBjK+QMIThAslqYqwDEV4lipqZmxSpGjMr+83Jy51U1zqeU377qbq053NNQaN1m61U6SYx48Wy7JVkbNiU1YkZ8dJ0CPU309a8PtJC5BggObFpc6LSz2nbj\\u002fYWlvU0dRo1OrsNpPTzsMJKY8fI5GnKsImRcYvSEj3mVf905RFgbcOP3BdEMtzfbZ0HRrq2z9nr\\u002frn7FVotD8j+doBoN1ifPLwZnZGW8oXfLfi9vzwGNZWAclQRry38Pp793y3v6WWHd\\u002ffUhtswt3lytzj2KLFE6S8QOv0w4SZSMRB+163hsPhcDgcDmdkPLF3y8aLHpmWAat84AkhEWiGcE9DzWdlZw8113vXXJJA2ylSa7O2GA3nunrXB0qSKxckpV2TlTs1NuFyTgP1J04qvzYrV2uzaW1Wjc2qtVnMTie6EeeyEOjuxGHbfLESAKYlJPovG3eR8PnT4hP21tX+VFUxMgn3\\u002fKhoJLK\\u002frs5\\u002fwh3HsMLYuMONDe7I+6dOBpJw311bw76rFPp+QwrDUj\\u002fzLMQsUl98oez7J8ddLeP5fshYt+n0hWfX73Tfre5QP\\u002fnllo+l4ukZfdb1v7nzKBricK48SoFoTeq4Nanj0IHL2vjw2JFv0DEhInZCROxjBWj8SjBir\\u002f2Nc0cMDo9OMn+bvnwA2XYXDOCxgtlIwr2ku5V9N1hX5h7nEieZhob6JsDRi14OqrelO4fD4XA4HM6VoNVkePbwnj0NHomgQDQZ9V9cOPvFhbPT4xK\\u002fu\\u002foWdPiKkaEKf33xanbEQVEam\\u002fXr8pL\\u002fnD7GjnPGun6qszk+1Wg0ABAXcE\\u002f2aKkULj1qBORHR4t5HpdS9tR6nJz7NC\\u002fFY1r0qdaWj4vPsCPetldfrPZ8USmq3nYHbFPCM6Q8tPh9W+uZmw69\\u002fO\\u002fyTQc7y5otagtp9746OnZ9caQYDQG8s\\u002fs4GuJwgnHdW1\\u002fkPfNa3jOv7a\\u002fsf6fmcDijy0aRP9Z6zDhLkCqvTc9nR4JVGBXP85xaF+zauRw3lQAtWveDh6H1ATTDlSNxOBwOh8O5gpSru9b88PkAsu1scxK4jnweBAQRK5VFS7hVjy83XIX7QLhar1guNWDpl9nhAABjwNsPkojHm5eSurOm2h1pMugru7tzIiNZW6GWZWT849BBduSlgwdkAsFN+b4XEzvf2fnn3buQ4NT4BCTiIsB5K+Mm\\u002f9CEXq\\u002fTOc0\\u002fNB1jx3Gsz4tAvzz5ARoaHkeWvYSGgtfQrUNDAOWtnWiIw+FwOJeps12tVtIjJzsvPpUYXC81HoarBOJuW+865Dq7lQEY1A+9UikFqWiIw+FwOBwOh+NLtVZ968\\u002ff6uweczfdlEKRQiCU8PkWp9PosOvtNp\\u002fVlDiG3ZzjO8XE4VxmuIT7QERKJK1G44mWFoph+j1zphjmREsLAESIJejYsFmWkclOuAPAd2Xnn12wkB1BpKrCJsfFn2nrnZlOMcwfdu3cdvHibRMmzE1OEV2qmq9Sd\\u002f9YXv7p2WIbSbo3dpmV1Ge\\u002flDvTFm5tPW2l+rnqQDN9Lt3mZygESQR8sx19sSI+H4lwOBwO53J1Ud+NRDKVEUhkAGyUx5cvnyD6ORDh9EGAc5VEHA6Hw+FwLh9CgpeuCkOjgfG\\u002fwB4D8MeDO5FsO45hV6XnXJ05bnJMfKRnvstKOqu1mipt9\\u002fHWpiMtja2mnpVv5iemxskC7RXB4YxpXMJ9IKbExbcaKzvMplePHfn97LnosKc3jh\\u002frMJsAYFJcHDo2bBanp\\u002fNwnL1K24YLF34\\u002fZ647ae7Tg1Om\\u002fGIz2gp2f33d\\u002fvo6AAgTiUR8vs5qtXrl2V3GRUZNjotHo5fEiJSP56z554UN6MBlKi8x5kA52vQjPwFtr3\\u002fZW\\u002fzyh+1646nnHpMIuIsNHA7nyuJdAaQUoG1JgmV2OkxOOzsSIRy5y\\u002fmXGT5+Wa0By+FwOBwO5wqXrgrbduPdaHQo\\u002fFxdcaq9hR1JlCvfX35NfqTvFIeYxy+IiimIirkhOx8AqrTdGy+Wb6ouXzdu+NdQ4nBCQ5\\u002ftOzh+uLusvHvyxGNbN1d0d3mO96jWaH69feubJ3radt\\u002fcR2+W4RAmEl2Vnc2O6O22HdUX2RFvyzOzJsT0uWqf1mZrMxr7yrYDwINTpqAhT9cmTn8wYxkavUz9ZuVcJMUsFQp+s2oeO3LZq+vWtuu5NeU4HM4Vik+gR1kmJzrzKVj7WtCmmWmKcCTCCRCBCdAQh8PhcDgcDsfLjxcvsO9K+YJv167rK9vuLTss8qnp8w7f9tCKtCx0jMO5TPmrd3b5uO559+14cfqK2GG5XBY4BhgLabTTFj4uFBMyHjYKZbNzk1OuG5f3Y8UFANh6sWrrxao4uTwnIjJSIhHyeHaS1FitF9XqJoPe\\u002fZDVWdkLUlLdd0fAPYWTfqqocN3Ojoh8aOrU1dk5npugMIAXlyy94duvHRSFjvVnYWratbl5aNTLfRlLMuWxL5dv6rb3TCm6XGXFRmz5\\u002fT3fHj9X16UFgLSosFtmTYySB1FM969bV2VED0HzAUR1h\\u002foP32xDo8PjaHUDGuJwOJwrRrQY7VhSpUObzASFAfisAl3PfFp0IhLhcDgcDofD4XCGipV0Hm72OLV\\u002fuHBaglzBjgQCA8D8Nq7hcC4n\\u002fSfca03n3Ld1js5RSbhTDFVpPFllOF1tKjGSancvbwwwlSA6UZKVLZ+Sr5jFx4WejxtG\\u002f1y2nGLonyp7MtptRmObsc9K3lVZ2a+uWIVGh1lhbNzE2FgRj\\u002ffwlGkL09LQ4T7kR0f\\u002fffGSP+zaiQ74laRQvrJiZYAfnPOj82dG5mxpOb217fQFfRPN+FxL43IQrZD9avlsNBqwiclxieFKNDpoQn7\\u002fe\\u002f1QOVrdiIY4HA7nipEqR3to7misfGbaYilvgIXV75YeO9HZhARXJHtMaONwOJcBknFeNJZWGotbrfUaR4eVslAMKcRFCn5YlDAhTZqbp5gSIexzWurgaR1dF03n6s0VXfY2raPTRllJxsHD+CJCEi6IiRElpkvzsuWFUt4V0YeXAabJcrHGVNZoqe62txmdWgdtp4EW4EIpTxEhiE0Up6fLcjNkBQRGoA++vGgcnRcMp2pNF7rsLQan1k7bCIwnJiThgph4cWq2fGK2fCJSDzfk74mTdrRYa1ut9a3Wug57s4U0WimLlTIxADyMJyIkUp4yQhAdJUxIlmSly\\u002fLERBAFTwFqtzUe7tpy0VRqcGr5OD9cEJMlnzA7YmWYIArdFAAAmi01R9Xba00XDE6tABfK+WGp0pxC1ZwM2WBnwIfCu+FicGrrzRWNlqpmS62J1Fsok42yEBhPylPI+cpkSXauYkqaNHfI\\u002fx68hcIOGwp7ytBqMRqctEdR5kquUJ3D6U9wqTeds4tmKHxkPwsqjae2t33SbfdoF+XCAKN1dGgdHaW6w1uID2dFrJ4Xdf3IpN15OP76yqtWZ+e8c7KopL0dHb5kfHT0I1OnX5U1OifDX1x\\u002fo1QQ9Fn9TfnjSZp+ft9edgt4PybGxv736mvDxGJ0oG8CnHdd0ozrkmYYndYLhqY6U2enTa93Why0k7p0NcXgtJ7WeEycnxyeruRfQZ1qVZIg3tLAyURB\\u002f0kMjNXhPFmHJoY4g8QVBHA4Y0hhZHyUWNplNbsjGrv1ryd2\\u002f3P2VcHuyjTD\\u002fPvswXdLjyHxadGJeeExSJDD4YSsRkvVWxf\\u002fzI48mP5slnyC+66Tdhzo+ulo93YT2TtT1sVKma2UucPWfF5ftLn10wzZ+OWxN6dKxyGbDQbN0Of0R490b28wV6JjAA7G7qDtrrRakXo3gRHjFFMWRl2TIh2dM50RYKMsR7q3ndDs0Tp8dBC1URYbZVHb26uMZ\\u002fd2goSQTQlfOD9qrZJ\\u002fGXb6arJU72j\\u002f9qKxhAGPYimaoZyX\\u002fiqOdm+X8uSzIlYsjL5GgPesWSIihub0zUIayw2nzxtOVhlLnLTHWiZuDoZy\\u002fYm2WetdEQIjsuQTZ0esGKeY7LFpf75tevu0Zr\\u002f77p9y33En0\\u002fd1\\u002frij\\u002fRt3CSBFka3W+lZr\\u002fZGubavj75wT6VFmRzPUz62fHununV5MUk4LZeqwNRWpd+crp9+Y+LCUF3SZ8Ai\\u002fG\\u002f7VmMqOdG+9YDjlfk\\u002fcSMZpd1g1jo4Gc9Whrs3IqB+\\u002fzPz7AD7cQmGHHfU9ZZiwj2ZdUhVoWQmHw0EEl3CnGUrn7AoXDGM9BWJ72ydHujehUV9slHlf53dndQduTX4qTpyODg+PZekZy9IzGvW6M21tVWq13mazkE4xj6cUijIjIibFxqWHjebH0ACy7S63FkzIj45+bu\\u002fecx19XksAAJVI9OCUqfdNmiz0uxarH3K+eEZE9owIH4fpTZbumw\\u002f\\u002fmx15KHP5RFUqO3IZ4xH4MGXG5aJAr0jVdmk2Fl84Vd\\u002fc0K0z2R0YBjKhMDFMMS4uakZ60oKcdO91UMtaO45WN1a0dVW2d9V3a93TF6b+7S3PDQEAHpg\\u002f7cnlfa453KYz\\u002fnD6\\u002fJHqhiaN3mCzKcWilAjV\\u002fOy0m6YVhPm9FPHe\\u002fqI3dh8FgIN\\u002ffChSJgWAqo7u706cK6pr7tAbaYaJlEtzYiLnZqdeP3k8gfdkvb48fvbFzfuyYiI2\\u002fequTqPppc37j1Y3SIWCZflZTyyb43qlXx4\\u002f+9nR4k6DKSVCdfecyddNzmc97VA6Wt2w4UxZcWOr2mSRCQVpUeGrCnJunDJewCMERD\\u002fXO5s0+j3l1SfrWi52dKvNFgdJSYWCOKV8fELM6gk5MzOS0QcM6WsfwLMjjlY3bDpbfq6prctotjic6PAln9x\\u002f0\\u002fQ0H2007CS5qbh8X0VNZXu3xmwR8nixStnM9OQbpxZkxQx9gyYOxz8cw65Jy\\u002f\\u002fwwgl28LvqcxbS+ZfpSyNFAVWZUQyzpb78nfPHKrTo2SOOYX+euhgJcjicsaXVWu9OuDeYq75qfN1nqgjBAFNtKq2uLp0WvviahPsEQ1FvVGu6sKH5g05fRU4+UQxVpj9Rpj+Rr5x2XcKDCv5onvUMOQaYIvWubW1fWSk0zdQXC2U61LX5uHrn4ujrF0ZfG+L1qoEjGefPLZ8cV+9CEog+mUnj7o71JzR71iU95vrDlhBod7VgNVouHuneek53jGKC7npKMVSF4UyF4Uy6NO\\u002fm5F+GCwZ4ibrVWudKuO\\u002fuWL+z\\u002fVt0GAAASMa5qeUjiqHmR61xRRhgvmh47by+yHPDXmX6E1pH5y8zXwh8Fw6Fd8PNTBq\\u002fb3rnguEUOjDiQmGHHfU9ZVghPYdxDOP3d1o6dlEMc6i5\\u002fkRb85mO1laTQW+3m52OCLEkViKbm5hyQ3Z+umooL9KMmIta9Ym25jJ1Z7m6s8ti0dttFqdDzOfL+AKlUJQVFpEbET09LnFabAL6yAHR2W276qtLOtsrNF2tJqPJYbeQTh6OS\\u002fkCuUCQJFelq8JywiJnxicN\\u002fv0cyecKStB5Uo2jfcQS7j+1vHdSswON+qV1dPy39k+3pzydIZuIjg2bZKUqWalCo2PchJjYjbfedry5aUtV5cmW1jqd1klRACDh8yMl0slxcXNTUpZnZMoGmtPvV5Rw6LupjCHDVN4OACI+j8Bxyu\\u002f0BYpmXtlx6LOjZ5CGPxrSojFbzjW3f3eyVCLg7\\u002frd\\u002fUju+8tjZzcWe6ymMgAMA+\\u002fuP\\u002f7BgRMOsvd7XW2yqE2WMw2t\\u002fzt06qlV82+Y0v8EzE6DOVImfXvv8Xf3HWe\\u002fkEa1rlGtu9DWedNUdIX0+m6dxeF88JMNFzvUAGCyO744VtyqM7x1+9Xv7it6c89R12ZVHd1\\u002f3rATw7BrJ\\u002fW\\u002fbkFQSIp+5sedP50td0c0pFVjbjld3\\u002fJNUclbt1+tlPQUQXhr0Rqe37Tbu2++wWozWG2V7V0\\u002fnD6\\u002faFz6K+tWi3y1FRrkax\\u002fkswOA1eH8\\u002fffb9pajC0J6wzDgXbpSwlZU2\\u002fTnDTtbdb3rQzhIymizX+xQf3n87J2zJv1u5Xz3JRYOZ2Q8PmH2proypCxoc3353ubqq1LGLU3KzA2LSZIpcdbsFZphOizGeqO2Rq852t5wpK1e77CxHt3rycJ5hZHxaJTD4YwpLdY6140S3dFvGt+kGNJzvB8nNXubrbUPpj8j4w38yJlm6G3tXx7s\\u002fDmQPJG3Mv3JGlPZrcm\\u002fzh3SstlRZKXMXza8VmUsQQcC4KQdO9q\\u002fqTSevSPlycvgIoSFNH5Y92Kzpf\\u002fDMzaDU\\u002fth7QtrE+6ZG3nVAMq33ey09YOavzVZqtGB4NWaL7xe9dQjGX+NF6eiYwFosdblK6fXmM7vav8OHfO0te2LDFl+gjgNAHa1f+cn2+7Saq3\\u002fsfm\\u002f65IfQwe8hM674dJoufhZ\\u002fcsGpxYdGHGhsMOO7p4yApRCj5NQmmHUVkukOKSr8geAYphPzxd\\u002fVHq62YhOMuswmzrMppKu9reL+9mpCQyreei3aHT0XNSqv688v7P+Yr1eh44BmBwOk8PRbjZVaro311QCQIpCdff4SXePn0wMdHJ9tVb98snDexpqvHtmkDRtI0m11VKv1x1qrncFY6SyFalZN+TkT4wKOtU8ks81AL4TH35o7O3gee3NSdt1zi4rZbJTVj4ukPAUYfwYPj7YPGyp\\u002fnCw2XYXJ+34suEfD6S\\u002fGC\\u002fOQMc4QZqZmDQzMcl120qSvBG8kiki+DKe2ERa0YErQ5i0z7zq4MlFAp3Fd\\u002frG5Y3dRz45ctp1OzFMmRsXpZKInRTVojNUtHUZbXYAmJgU511pvnriuHFxPdMtbU7y9V1HXLd\\u002fu2Ien8B7twMAgPEJvj\\u002fjnvlx549nyly3o+TSScnxSrFIa7GeaWjRmK1Gm\\u002f3ZH3d1Gsy\\u002fWDTD83GoLqP57b3H3t57HADEAn5GVLhEwO8wmFp0BpKiF2SnoQ8AcFLU\\u002f23d36jWryzItjupfRU1ALC3vGbLucp39h2PUchmZ6ZcaO2obO8GgLf2HBvyhPvTG3ZsLqlw3Y6Wy6amJUgE\\u002fHa98VR9S3Wn+v5PfohTyj0f0StCJrnQ2uG6LeLzcuOik8KVIj6vy2g+UddstjsAYF9F7d9\\u002f3vvi9cs9HgkAg37tg3x2AHjqUrZdKhTcOWvSuLgoIY9X16356nhJs1YPADiG\\u002feOGFbnx0UnhSu9ZNXvLa37zzRbXdUGpUDAlJSFGIbM6neea2xvVOpphPj16pstk\\u002fvfNVyEP5HCGlUIg+uesqx7a9wPpOc\\u002faQjrX15SurykFAALDJHyBmOA7acpKOm1UQOm2+\\u002fOmPVYw8GVCOBxOiGi11gFAueH0141v0MGXrAJAm7X+vernH816cWDdmSmG\\u002fLz+lUHWqNooyyd1\\u002f3dNwn2zI1eiY2ON3qn5oOavXfZWdCAY9eaKd6uffSjj+b6aeo8JNsr6fs1f22xoOUUgGGB+avkYAPIV09CxgAlx8eCzCm42yvJR3UuPZb6kEkSiY\\u002f1ptdbTDL2x5X\\u002f9XpSiGWpH29f3pT\\u002fdbmvc2\\u002fkjOuzLGe3BhdHXxoh8zN1kC513AwDU9vaP6\\u002f5hJo3oAAAA8DC+jKcQEzIHY7eQxsCrzgcgFHbYUd9TRkCMBC3AP9HWfFW6j0YFY1eb2fjrPVtOtDWjA2PW0ZbGd4qLDrcE95fZYND97ei+TdUV7y2\\u002fOk7aZ\\u002fKhL6+dOvJ2cZF3+tuPDrPps7Liz8qK\\u002f7Nk9TWZuehw30byuQYGzVn0S+Noc93QOjpOanbWmErabfXI0SGO4bGi1Gz5lELVoghhHHsoQBbKuLn1v2g0YE7a\\u002fk3jy49mvSrEL7drbqNI7JXhGm7RIoXJdIUm3Ievwh0AZCKhn4S72e74\\u002fFgxABA49q+bVq0qyGGPUjRT2ty+rbRyTlYKO+4yJzNlTmZP3GizuxPut86Y6N1\\u002fxqevi0pc2XYegT+zZtGNUwvchZ8UTX9+rPiVHYcomnlzz9FxcVGLxvlrHvXjmbJdFy7GKGS\\u002fXzl\\u002feX4W71LG32R37CuvyYj23WDkh9PnP76vp13JqzsPf3jwJAD8cf22lIiwrx++RS4SOinq5ne\\u002frmzvatUZajrVff2cAdhbXuPOtj+0YPqvlsx2l2PrLLbnN+7adaG6Rdtbvo0Q8Xl3z55c3tZ1\\u002fZT8menJAl7vtTGjzf7H9TtcOfRNxRceXzo7RoEeM8HgXvsgn\\u002f1kXfOe8hoAEAv4Pz52R2JYT5negpy0W6ZPvO2DbyraumiGadEZ1hb6+F5s0Rr+uH67K9u+bvqE366YJxP2npD8fLb8uY277SS59Vzl9LTEm6f1zNzncEbG4sSM9xdd\\u002f8sDG+19ZNIphjE67EawowN9kPIEz0xbfGtWITrA4fQaYFESZ+R12VvbbY3e2XYexk+WZMn5KlfZo96p6ba3dth8JwI67S2f17\\u002fyYMazWJD\\u002f9DRD+8+2RwhiokQJcp5KREgspNFI6tptjT5rWhlgNrb8D8eImRHL0LGxw0wa\\u002flvzt76Sd3xckCzJUvDDpYScBtpCGrXOriZLjc8rJWpHx4e1f38s6x8DuxAy6hhgvmp83U8OMVaUHCmMVfDDGYY2kQads7vFWot08d7c+ukgX\\u002f7CqGtqTX1On1XxI2JESRKeXExICYywUGa9o7vRUu2gfZ\\u002fpGJza9c3vP5D+Z3SgPy3WuvP6IvcOmCzJihOnAMBF4zmNo9NjU4AKY7Ha0bGt7Sv3H4aYkGbKCpT8cL1TU244TTIeXRMZYE5q9qyJv5sd9ClE3g2Scf6v7iXvbDuB8aaEzZ8avihRksFeEVRtby83njnUtbmvflliQhopjIsQxEQIYyIEseHCmITAWgSHwg4bInvKcIuTyWOksg6zyR35rKz4ckq4a23W237+rk6PfrsRGBYmEkv4fIPdrrP73pVCVpm6M9hsu1tJZ9s9W39Yf82tckGgDa8A4KkDO76rKEWjgZHyBUtTMtFo30byuQYs6BSqxtFuJLVbWz8s0x\\u002fr6wIvzdCt1tpWa+2BzvXjlXNWxt2j4AeXkzql2WUh+8wrBULr6Njb8e2quHvRAc7YESVU1Jp6amavNGHSYUy4+2\\u002fjXtettTlJAMiNi0ay7QBA4Fhhclxh8kAupPXL4nC6c\\u002fTPrV1y49Tx7FECx++ZMwXHsP\\u002fbegAAXty8b352KoGjhfNuO8suxihk3\\u002f3itii5x+GLTCjwmbR1mZgU524OfvO0AlfSmaKZ++dNdb1vfIJYMT6rsr0LAGq6NEOYcH\\u002f\\u002fQE+j5xXjs59YNoc9pJKIXr1l9Y3vfOkqMO\\u002fLwwt9V\\u002f3LRcJ\\u002f37xqyb8\\u002f1FlsNMMU1TZd7esdGORrH8yz77pQ7bpx64yJ7my7i4jP+9WSWY9+8RMA7CqrfsTXs7y+67DJ7gCAFeOzn796CTK6tjBXa7G6\\u002fmze2Vt03eT8EZupw+G4LEnM3HjVXc8V7TzZ6TtZFiAejl+blv\\u002fbwnlx0pCed8wZdQSGftGTzBg7S7xyMMC8ffEZO91bYpIizVkSc0OGNN+7mrXD1nRMvfNY9w7vs7BqU+mhrs3zo9Yicf+2t3\\u002flM9vOx4Xzo1YXqubGiJKQIQaYJkv1Ge3B4+pd3nmrjS0fRgrjMmUeh3BjBc3QXza85rOLfaZs\\u002fMLoa9Nleew0ooudtpbpT+7uWN9t76lLc+uyt33d+MZ9aX9C4mPCse4dFYYzaBQAA2xW5IrZESujRQnIkIUyletP7er43p2Dphn628a3PLcKzjjF5FhRcrut0R0JF0TnKaaOU0xKFGdIeD6qL2mGrjSe3du5wefav1XGsw3mqmBX+tU71T80vw8AUp7irtTfpUl7DmVphtrU8tEx9U6PrQG2tn7hfvdmR668Ku5299qYGkfH+zV\\u002fRVLPZYZTgSTcQ+TdONK9zfuvPVwQfU\\u002faH2JFyUgcACKEsXOFV82MWPZD8wfspWhdbk56dGr4QiQYiBDZYUNkTxkBcxKSN1T1Xu853tr01YWS2\\u002fImsjYZwx7d\\u002fTOSbZ8el\\u002fhI4fSZ8UkSXs9fUYfZtKuh+p3iE60mNFepEAhTlKoUhSpFoUpThiGjo+W23AlvnD5mdHjU9Ej5gryIqJzwqAixWCEQWUlnh8V0qr2lXI1eD6vUdL90\\u002fMA\\u002f5vuenu7t+8rz3hnwCLFkelxiqkKlFIoEBM\\u002fosOts1mqdukLT3WXxmPtyTVaulI\\u002fusH0ZyecajKAT7g3m8jeqfmULbFoQA0yp\\u002fnCV8fR1iY\\u002flKwOd+MwAc1qzC40CAECBam6hamGCOFNMyBmgNY6OOtO5Is32Tta3jluResusiNUqQTQ6wBkjokQeebcryrAm3P0vx8q7lMJu15vsJOndu2P4\\u002fHT2gqtfTXpUeF9d2m+fOemzo8WtOkOrzrCz7KL3JQG2P61eiGTb+8VudJMYppQKBa52KNNYS3QmqHpSXd0mizs4SE0aXWlzu+u2z5wygeP3zp36x\\u002fXb0YHAiAX8edlpP58tB4B2PVqQ4jJ8r73fZ6\\u002fv1rhujIuN8hwBAMi5FKxXo0UHANBtMm8\\u002ff9F1+8nlHhcq3G6eNuE\\u002fu49aHc5Oo+lYTeN8Xw2FOJxhlRsW\\u002ff3KO7Y1VH5fc+5ga11Qkx9xDBsfHrMqZdx16fmxEh+n0xwOQkDIMMAZ6P0zs5BXaAXDmODOtuMYcUPiQ9PCF3uO94oRJV2bcH+BcuaXDa+ZSD0yurP920lh8+Q8FRLvS7Xp\\u002fP7OTWgUIFNWcFPSL\\u002fpqrYABlizJSpZkzYhY+l3jOy3WWvaoKwX2u5zXpb5yfyFuX+eP1abzSFCIi29JfixfOR2Juwlx8eSw+YWqOZtbPzvcvRUZrTCcOaXZNzV8ERIPcWbSuK3tKzQKIOMpb095IqOPCyoSQjYlfOEE1ezt7V8d6tqCDg\\u002fUguirv218S0xIp4QtmB6xxGdKlw3H8FzF5HGKSbvav9vdsR4dBjjY9fOd0t+i0f5YKTMG2P1pTydKMtxBHCOuTXygyVLd7LkjlOqPu25MD198bcL97KFwQcx1iQ9+VPsSO6i2txtJXSA776i\\u002fGw7atr9zIxIUEeIHM56L8LsKKw\\u002fjr0t61EqakIt8W9u+yFdOG0B9dyjssCG1pwy3O\\u002fMmsRPuAPDckT0Uw9yZX8gOjkX7GmuPtnhkFB8pnP6HGfORKWMxUtkdeYVrM8bdv\\u002f3HU+29V3ruHT\\u002f5+Tl9fnGPIilfcEfexHfPngCANGXYqvTsZSmZhTFxPqfClXS1P3to97munoyEy\\u002feV5x+dNCNR3n9ejmKYf504xI5ESaR\\u002fnbNkZVoWe6kqtjq99mBT\\u002fZ7GmiPNDRTD3J4b6MWbkXyuQQo6lWahfORK\\u002fLPT1m8aX74q\\u002foFZEavRMV+aLRc1Do9\\u002fZgDAALsh6dcTVQtYMTxKmBAlTJgWsfJQ14bd7V8itR4UQx3p\\u002fml1\\u002fAPs4NBqNxmPNTe1GY0mhxO8Kk28PTVnHhri9C1SeOVW8A1rwt1\\u002fhXtWTES4VKwxW7tN5l98vum5tYtTI0foIu3R6p7vuRXjs\\u002fr4qAQCx5bnZ7lazB++2OAn4a4Qi5bmBT1RCGmSrhSLzHYHgWPxlxLNACC91K7E7iTdwUE629hTZBGrlOfERnoO9nC36xkY90tzpdG9Detr9\\u002f\\u002fsTqonK+RzSVV3kKZ9fMwer2lyLQKcHROZFK5ChwGgp6181JmGVgA4Vd\\u002fCJdw5o2VVSs6qlByd3Xa4rb5c21Gp7Wow6kxOu4l0WJwOAODjhIQvUApEUWJpkkyZpgjPC4uZFBUfJhzGLwXO5QcDXMKLMrOS7G2WE5mKq1mbcEIOBtityY9PVPVfopQhy7837Y\\u002fv1TzvpD2+Uh20fU\\u002fHD0h2ry8UQ7oqdhHZ8on3pP3BuyzUW5wo5eGM5z+o+SuSajSThp9aP7o1+dfsYOhTOzr2dPyABEWE+BeZf48T9X8AhmPE1Qn38nC+9zWMbW1fTVTN5uP+Dr9Dzb7ODexZFy48jH9P2lPJkmwkjuDjgrXx9wBgh7o2o2MDUqiaiwM+XjnDe86HHxhgy2PXmUmDd\\u002fl5jek8A0yw\\u002fZcAwNUsBQligC2MvvaLhleROABIePLV8XehUYAceaGSH6F3qtnBNmu9XF7Ijvg06u9GpfGsdzOZpTE3+8+2u92Y9MhLF37BbqpjIvVF6t0Lo69hbdW\\u002fENlhQ2pPGW6TYuLmJaa6F58EAJKmnz28e0fdxWdmLxoX7vsEdkx4v+Qk++7M+CTvbLubUih6Z9nVi775n9nZ8xX8WVnx7XkTM8MiPDcMCfcWTGkxGW7NnTArvp+LcxOjYr+\\u002f5pY7Nn9\\u002fknUtgaTpzTWVjxT2eQXL7WhLA7uKXEjwvlm7LkMVztoElaYMS1OG3T1+UrfVcqi5Pj8yGt2iDyP5XIPkI7UxTLa2fqjiR+YqfFRuIpqtPbWKbAWquZ7Z9l4YYPOjbpAQik0t7yBDxdp9K+LuDuSQMVg2knxu354N5RdoxkcCqC9cwj0oUVdYwv3Mi79y3+bhBGtkiMnF\\u002fg4gCBz\\u002f3cr5T\\u002f+wAwCO1zSu+c+nc7NSb5w6fkFO2nA34ii7tOpmbpy\\u002fT8C8+J7RkqaeJLVP+fHRfV3h9EMp7pnv6eJqWaMQidg\\u002fCr\\u002fUWj2oGlX\\u002fqjt7jrmzYvr8qo6QSRRikcE6wLYA7nVr+\\u002frMGtbX7v\\u002fZE8J6dvbaLo3niCuodd1wb8bmnhmQ3ceFCpdIWU\\u002fhTJsOnQPI4Qyf8\\u002fryf1S8CgBxoth\\u002fT\\u002fy7K6gSitakjluTOs5jUw5nSEWJJ5qNvYmVRtN+nb1GJURTRZzQMStyRSDZdpckSebV8fd6Z8xPafatjL1VREiQuLeTmr1qO1rhFC1KuDv194GfOokIyQPpz7xa9Vukq3ux9vC8qLWJgXVhDhE7279BOmsDwM1JjwaSvHNbEXvrReO5FmsdO2gkdUWaPXMjr2IHQ5mDtp3Q7EGjAGvi7+o3h+i2Jv6uVmt9jVcB8gAQGDEpbIAn0Wvi7yrRH7N4JogtlKnd2hAnTmUHAzEl3Hc6Ik85lYfxvf9+poTN91m4jQGWJh13VneEHey2t2cHMC1k1N+Ni8ZzSISPC6aHL0GCfZHxlAWqmcVaj+rUE5o9wSbcQ2GHDbU9ZQS8vHDlyu8\\u002fQVqZH25pWPX9JyvTsx+cMHVyTDx7aEwwO53scnUA+GXhDP9JhGiJ9Ibs\\u002fM\\u002fKil13KYb56PyZl+Yt89wqJERLpG8sWYNG+yAkeP9Zsnr+1x+yz\\u002fSL2poDSbgjHWmWpWb4z4CzRYol12XlodG+jeRzDdLIJdwBYGPz24lZWXJ+P+9Fq6WnmS\\u002fbJNUiNORpaviyRkt5sXYfO2inLVXG03mKmezgkPjVts17aj2qOfzDMSw7wl8yiOMtSuQjuXYZG7HmLf5bygDAtZPyxHzeC5v3qU0WmmEOVtUdrKpTikVrC3NvmT4hPaqfXXjANJfWyPXfB8Y9qvbb1SR8QLME2Kt9ug33lQYA0F9Ko7vzwj6pJP0k3BkGSlvaj1U3VHWoW3UGvdVmsjnsJOkgKUcfCza6Df61D\\u002fjZl+VlbThdBgBfHj9764yJyCSM9\\u002fcXuW74XCbX3dxmc0mFe9VZP9xvNYfD4Yw4xklbnbTZ5GxGBkzOVhGh4uNSPi4ZkiVPk6Rz61kJd5oh97Y+OT\\u002fuxUiR7xnunNHFxwVLY25Eo35NC198oOsnpA2xg7af1R2eGbGcHfTGALPPqykEAKyNvyfAuk43CU++Kva2b5veRuK727+\\u002fJ+0PSDBkaRwdJbqjSDBPMXW8sv9aMTYCI1bE3vJR3T+Q+PHunYHk70JEie6ojUKLdsMEUTOCWQ4XA2xN\\u002fJ3\\u002fqRrlvwE+LpwevsS7BUqbrTHwFLMLD+OnSHLQKAAA8DB+vDi10YJWDeYqpiARt1hxMngm3JGC9+EwJO+G96qtSeJMERHEaVembDyScO+2txmcWgU\\u002f0EnVIbLDhuaeUq7uSn3\\u002f32i0P7FS2fE7HkGjXmKlso9WXX\\u002f31h+QtuAMwLbaqm21VfmR0beMm7A2c5xK6FHIFcrOdLSy88t8nJiVkMQa921eYqo74Q4AB5o8LtuMXfEyxYrUrC21le7IBXUna7xPSJP0BNkwpvJG8rkGaYQSfC4Wyrix5e07U59FBzx12puQCAZY8qWVSfxYEXv3ef1RJ+2x81cYTg55wv1oU6M72x4mEi1ITYuXK\\u002fg4\\u002fkbRMQbgpvzxCqFQb7Odam2t12kB4DezZt9XOFkq6CfLyUFECZVoiDMUMqIjJiTHolFPK8Znz8tO+\\u002fbEua+KzrZoDQCgt9q+OFb85fHiZXlZv18532et8WBQNGMne3KyPvuKuIkFPYVXrnUy+xJUptjNZ1G8r9gQszh6ajT8X3cR+31ndl2ofnXHoQa1Dh0IzCBf+2CefX522uSUhDMNLV1G87Vvff7Q\\u002fOnZsZFiPr9Ro\\u002fvs6BlXK5houez+edPQRwK4+v4HjvTVl4bD4XCGltHZfFb9gZM2OWmzgzY5KbOTNjtpM7uvOtuBtj9euonxcQkfl\\u002fJxqQCXuW6kKVYkSeezt+9XimxJMf89k7PVHTGTHduaHogSFUSLC0VEOI7hJGN3UEY7pbdSagvZwTDM2pQvWT+DM3LGK2fIeMEd9+IYvjD62vVN7yLxUn1Rvwn3amMpsmYjAGTJJ+QE0NHC2+TwBYe6t7Ra69nBcsNpvVOt5EewgyHrpGYvzaD75pKYG5BIIHIUk1T8CJ1n8rTT3tJua+y33XaIKNX3FDqwLYy+lsCCO7ROEKenSHN8rtU5krJkBd4pZu+mKP2KESX6eQeihPFIwt212gE7wqbgocllMzkSUzAH\\u002f27onehs1PiAk\\u002fUuPpP7Lda6wBPuIbLDXmZ7SoAmx8R\\u002fe\\u002fW6R3ZuajTo0TGAsu7OZw\\u002fv\\u002ftvRfctSM9aNK5iflBbwqeSo6bSY2HdTlSp+AM0GMsM8ahBbjAaTwyG7LDJ+cxKS2Ql3rQ29quQTksRoNPr48xgqI\\u002flcg+Qvd9MvFT9qnGJ6kiQ7XBAnIqR8XGCjLBZK32KpqTOXXjQWI03VAaDKeKbdVh8rSkXibFbK4y8eAFSCaPe63n5IecpJYYtPqLexg7WmUvbdIfFTZU8R5dL0jNdXXiW5tL7te6dO2iny3sJJ4yKjXJEN5Ree3rPrg9On5iQlT44bzfk1XWZzeXdXvU7XbjJpLBaTw+GgKJKmg2qJ069PrrseDQ3CldZSZsSsmzlh3cwJaNSLRMC\\u002fd+6Ue+ZMOVrT8OOZsj0XauwkyTCws+zi0eqGt+64ZjprLc3BI3BMxOfZnCQAuP7bF+ul3LTsUj\\u002fxy4D40seI+6qDT36SxR8cOPH6rp5KGZVENDcrNTcuOl4lV4hFEgFfxOd\\u002ffuyMq4p8OAzy2TEM3rx97cOf\\u002fni+paNNZ\\u002fzrT+j0zNTIsDdvW6uS+PgikFy6ADM+IWZCYj9XkgAgbdimaHCuEI8X\\u002f0Ht0Pxv6lsiIrg6UM4VxUqq2QXmwWBcqXl2SClICzbhjmHEzOg\\u002f7ml5Aknxd9lKu2y+j40lvJ7DV87IG9\\u002f3En9+jFdO39D8PpJ4qjWVOWmH\\u002f\\u002f7OJZ6ltS5TwhagocBggE0OW4Ak3BlgzmgPLYq+lh0MWWc8620BIEaUlCTJRIKBwADLUUwqUu9G4pWGs\\u002f3m70IByTirjehHBAZYQZC1wy4TVbNHPY2YIE5DQ74SDv0K99ujXCVAry2FCaL8ZDCkXhfYbF6twIfDIN8NiiG9W5ZLglwhWULI0BCAJZjrDaGww15+e0rg8iKit914z4vH9399ocTnqamTprbWVm2trUpVqu4dP2XduAKR35Ky0YUklBWCgA7vlUJ0M63denkk3JFm9A6KspJOMa\\u002fnjLsvqQoV++6ehpqLWnXW8PS1H8nnGqQB\\u002ft3LeKrV8Q\\u002fkKWbhWE9bXhclHwAgTVowN+pajaN9W9vHFYYT7A0A4Fj35usSH0OCbDbK4xwDAJT8SCTSl0mqRUjCXe\\u002fsMjjViiEtryjt6AAAqUDwyvKV7mw7AAh5hJ0irax82fW5eQ6KenrPrse2btl1590jX+R+UaP+7vz53bU1DTodOhbywgSy1ybf576bIfN3lMMZJhgGczJT5mSm6Cy2b0+UfHT4tNFmN9kdT3z9844n7\\u002fO\\u002f\\u002fmqwImXSZq0eADqN6IcAW6eh56AwQtZ\\u002fi9KxQnGpsb7WjB7FsuktvtuhVLZ3v7G7Z2blHbMmPbl8rvcsAYWoz4P+QRqSZw+TiD+5\\u002f6ab3\\u002f2qtksj5PFImsYxTCkWjYuLXJaftXZirvfPdAmX9vwZFCTGPrN2secghzPE2mztagda28XhhKY4yfSZMX8s6vwXzfi7lMsJBenSfDQUAAkhS5Zk15s92qlRDNVkqU6X+WtRWmksQSI8jJ+nmIYEAzdRNWtL62dIrVWF4cyYSLh32lu86\\u002f3HySchkcAlijOKAM3fefcbCU3Nlhrv1tgJkvRgZ2C4ZMpGv4eVmCfDAEP+OK1eCYd+eafU2YQ4elYSLohGImzel8RIGn3bh8Mg3w2fW3q\\u002fFv98rhLh8OxS4EeI7LCX354SFCmf\\u002f9K8ZXflFf775OHdDTXo8CX1et3zR\\u002fa8eebYrybPuj1vIg\\u002f3SB6GCKQC1RnYQmU2ryK5flPSY4XCqx2Qk6b7bRo1JzGFwDDq0pvpoKjbNn\\u002f3ysJV85NSPbYbCiP5XIPkO3\\u002fhX5wo7a6052Q8FTrgKVwQe3vKn3a1f3Gw6wd2\\u002fJzu4Or4B\\u002fxc77XTFiQiIQK9apogyZTxVCZSxw62WmuHNuHeajQCwMzERLnndS0hwQOwu1tDuNycP\\u002f6dk0XNBsMXpSUPTxn4gWywLE7nc3v3\\u002fFh+wedVxzEBx7CZkdlolDNKVBLRwwtnrCrIWffe13qrTWex7SmvuXaSv9O5YE1IinUl3MtaOpbl9VmkcKG1p49YQQDlzGNFRnTPZ5R79VRvZrtDbfZxmAsAW89VuI4V8uKj\\u002f3TVQp99YIavd\\u002fmQPLvNSd7\\u002f8Q+1XZrcuOj37rrWfx9\\u002ftoLEmG9OAABUtKEH3xzOkCvVX0BDnCtGnSlyY3Nhqkx9XWIxOhZibj344Tlt86V7S7PkthuS9rPGOaFFJYiUBlki6pYhy0MS7gDQbK31k3DXODq8u0WnSLOD6sKMUPIjYkRJ7bZGdrDBUuWk7cE2hR95NcbzaAggXTaQSyAuMSIfc0BbbfVoKCQ1W2vR0CCygTGiJD4uRDq+jjAMMCEhQnpte0\\u002fE75f\\u002fRKp30lnut0EKD0PzMDRDIZHhMMh3g4ehLxMAbBSavfHP5\\u002fZSXqBT20Nkhw3ZPUVI8NJV\\u002fv72fIoUB3rmxTYuIurDlddVqLu+uFCysfqCyeG73Wu31fL8kT1fl5e8vHBVQVTI1VAir73Ds8NMX9rMHpvhGBYmGvh3aEjhD+i6SJxUvio9e3NN70SNLov5rq3rZ8Un\\u002f3LS9LmJqb4yBAM0ks81SOgHPULjaEMiEkJ+e+qf+s22uy2Nvb3T3lhhOOmOkIyz2XIxXVbA2sqDd0MuP9l5BAZYijSvTN9Ta+nSaW8aB0OZ6TY5HQAQLUEnQ8mEgi6LWe+5ajOOYfNTUr8qPbe3rnbEEu4aq\\u002fWuDesvdHHpJ84QS45QrZ6Q81VRCQA0a\\u002fx1y2K3UDfbHe6+H37Mz07beq4SAHaWXfz10jk+87YUzey6UO26PTvD30S\\u002fsWViUpzrRrNWX9ul8bky7Ym6pr5aQDVre6ZhTk5J8Pm+AcCZhhY0NESG5Nk\\u002fO3qmpKkNAF6+eVXg2XYAmJ2ZQuAYRTPnmtuaNLqkcBW6BYczdLiE+9iVJFtwZ9ZxAKgzda\\u002fZ8xYALE4wJkhUyGZ9wTBCLHjcTh+tMcXfmfUuOuwlWjzR9XSj4uqkiTnKGK3dUmFobzZrI0R5q5LubDYf6rKVmZzNdspI0hYeLhEScgEul\\u002fCiw4RZ4cLscGEO+oM4IyJKOPC2k1HCBDQE0GX3952L9H5xiRH1v0acf7GiZCThTjNUm60hWRLq1TMtvhJnkcKeA7MBEPvqmKF3qBlgMOjjOClkdNp8\\u002fPFE+\\u002fozCwQGWIwwwWdqcmQNwdsuIvwdnXq37fbZOMUN6RMAAHTAWe9BG\\u002fi7ISLEAlyIVKMjDdD7pXX6yFHI+P6uZ7CFyA4bsntKuips2413o9HhNC4i6oV5S5+etWBLTeX3ledPtLmv93uo0HRft\\u002fHLF+Ytu2Vcn5nAUTE+ymMySofZ1GjQJyv6+YM86fkys8Mjib5Og68Yz85adLi5QeeZDj3W2nistTFdFX5H3sRrs\\u002fLCh+iyxEg+12D0k3D3boA+N+paJT8KCfqBAbY89u5Kwyn2hdNGS4WfhLs3wusKsB\\u002fx4gwk4a51dLDvDp6QICw07T2LxLUWc4sRbUCWqFAAQL1Wh8SHz9O7d3HZds4wcS9VqvTVUNtNxOeJBXxXv\\u002fXK9u5AUqirCrJf2XGoy2iu79Z+e7LklukT0S0Avjxe3KozAECkTLqyINRP4QKXFhmWFx\\u002ftKt5\\u002fd9\\u002fxl2++Ct0C4NMjZ9DQJe52Kxqzj5oRANhUfGFgy5kGYkie\\u002fWh1AwDwCDwlIriijBiFbFVBzuaSCopmnt+054O7ruMR6DkMhzMk7LS93FCFRjlXjNlR6evrTy+Jy0UHQs+taT0VHm9X7H+ncj8ARIryI0UDLwDkDKvBrCzqM82ktvs79+mw+ciGxA4+4S5OAh0abLM2hn7Cvc3zOgEA4Bjuvx+IfyIC7S4CACTjtFEWsd+kbShQO9rRUB9\\u002fZgFSCaKGJI046oQBlwC6BF4yOLbEi9OQWTX1pnL23X7Vm9HtMcBihD7KzH0KkR2W21MQEh7\\u002fppzxN+WMr9Vpvrhw9rvK894F7yRN\\u002f\\u002fHADgxgXSjl3LPDIpMVSvYasJ+VFT8za2HvFl5Imv6m4hw7siQ5nX03ZDlpqlqruaDurNZq1DaL1mbV2awmp8NGkjaKtJOknSK985wBipHKPl194\\u002f3bNnRb0ZxArU7zt6P7Xjp+YGlKxs3jChYkpQ3y+sRIPtdg9JPIRhLuBEZMj1jJjgQiSpiQKh1fZ+79Uc2W4M5Xca8rxn5EeX1Y63xdRB2MCLHE4tQ36HVIPEGhKG5v8850u9Y5RC6\\u002fDJ9jTU07a3pKgDmcYL2\\u002fv6hJq19VkDM1NQFZAJpmmE3FF7ac65m8MyczhT3qbXJy\\u002fJHqBgB4befhvPjocGk\\u002f1xj5BPHnNYue+HozALy4eT\\u002fNwC3TJ+CXPh8pmvmq6OzL2w+67j61aj67iP4y8OD8ab\\u002f5ZgsAbDlXmRyh+uWiWQTe89otDue\\u002fth08UefjDNklPyHmxzNlALCvora6U515qUENANAM88Pp8y9u3te79VAbkmcX8nkAQFL0v7YduGv25FiljAh4OtuTy+cerW7QmK3Haxrv+t93v1sxf3KKR7WgzUkWN7bur6g12R0vXr+cPcQZAAftONJdVGYorzM3GEmTlbIJcL6Kr4wTxWbJMyapCpIlfSZu1A7N\\u002fs7D5\\u002fRlnfYuM2mR8aQxouhJqoJF0fPlPB8VRgBwXl\\u002f+j4pXASBOFPvviX9Hhy\\u002f5XcmzbbZ2APjTuCfHK9GU6MaWLd83bwSAtyf\\u002fW8VXAkCTpWVP54ELhkqNQ8sAreQrkyWJE5XjF0TNYded1ZkbSvUXGixNjZamNmuHu3rg\\u002flOPubdxWxu\\u002f8pakG9AoAFyqZaMZ+nD38WPqE83WVoPTKMD5UcLIfOW45TGLo4SR6GM8jdZb59JibTvUfbTCcLHd1mGlbBgGYkIcLYxMliTlK8YVqib4X0LWSTsPdR87oy1ptDQbSAMf44cLwscrxy2KnpcoHmA52MibE51ZtPpPaJTDGTRZwL0UvPlMM5nI3sSBN52zGw0NLk\\u002fkEinw8RN8Pleo8e4HTTP0n87dggQHz0Hb\\u002fOTvQoTRqUND\\u002fa0X6p\\u002fCb2eVAWOA6ba3d9lb1PYOA6kxOfU22mKnrA7aQTIOJ+1wMg6SdjppB8k4qKHo1uKz87gfPDy47QdjJN+NbPlEJOGudnTUmcvTpL6PHxA0Q3kveZokyQq8pUyI7LBjZU8Zeemq8OdmL35y6twvLpz9oOSkxnNJUgB4\\u002fsieKTHxyMqco+vO\\u002fEkvHtvvvvtx6el5iSkLktJ6t\\u002fD0wrH99axkoJDg3Zbro1IwdJgcjh31F7fWVh1qrndQg\\u002foE8G9iVOz2m+55\\u002fvCeLbU9KSM2kqa3113cXncxTia\\u002fK7\\u002fw9rzCAJeo9Wkkn2vA+km4qz1bysSLM7zXAwlEmswj4W4gNazBIaYSoAX4Zl+fhoORExnZZNCXd3U6KErASvnlRERuhsqD9XU2kmQvxFyj1cBAeyENwOYqH39wABArk09LiM+JiIyWyiIkYhGPxycIwms6G+cKZ3Y4N5wu23C6TMjjZUZHJEeo5CIhwzDdJsv5lvauS8uZ3jxtgs+2J2x3zJrkSriXt3Wueu3jqamJUXKpnSQNVluHwXTbjMLrp6DVdsvzsx5aMP2DAycomn7h573v7SuanBKvkoh0Ftvphha1qecC5l2zJ6+ZOM7zoWPeivHZy\\u002fOrdpZdBIB39xX9cKpsalqCVCDoMppPN7QYbfasmIiUiLDdlzrqsK2dOO6tPUd1FpvV4bzxnS+X52elR4UTON6iNRyprm\\u002fRGqRCwcMLZry5x2P2z1AZkme\\u002fYcr4Q1X1APD5seLPjxWzh0R8XqRMOj4x5vrJ4+dm+bjME6uU\\u002f+fWtb\\u002f8YpPRZj\\u002fb2HbHf78Nk4hTIlViPt\\u002fmJLuM5ladwdVlfkFOn0dOnADt6zz0bdMGI2liB22UvZ3qbLd1FuvObW\\u002ff\\u002fdakl73nVjPA\\u002fNiy+afWrU66t25C7zTonYYqY\\u002fXPrdtvT7l5YdRc1iOGhc6hV\\u002fGVG1p+3tD8M3vunY3q7LB11psbFkXPY20OO9r3Hur296cbIDEh0jp0r118p8ZU5w6SFNlgaWqwNO3q2P9g2t1zImewHtFrdN86mqG\\u002fblq\\u002frW23R49XBpy00eA0Vpvq9nYeFBHC1wv\\u002fr6+8f5mh4oPaT7rtvVPOnUBarC3N1pYd7XtXxi69LflG78n1HM6VYzCVsD4f6z\\u002fh7jNPJMT7qYrol88W8HrnMJ70DQmaocwkOjt5mJBjYflin++GzxLgAIkH8Vhv3fa2MsPJKmNJk+Ui0oh8uAU15x4AcBj277VReTcmqebuav8Oafu+te3LRzL+6n3s5+1I9zbvjHmByvfxj7fQ2WF9\\u002fhqhs6eMOplA8Ejh9DvyCl87feTj0jPshUltJPmPooP\\u002fW3kda\\u002fNRdmde4RdlZxsMOtddimHu3\\u002f7jQxOn3VcwJVLs8e9Sru56xWud2AcnTk2QB3rFaIQ5KOqzsuK3i4u0Xlc+hkmkWPL2srX3tE9+5eTh461N6DAAALSZjP8sOvTWmaL7CiY\\u002fUjhdyhegWwRmJJ9rYPr52rBQRvbdOHEG+27gYkUeKRIr5XGiPrQUPDQJOORPNyMhcXdtjZUkjzY1LkztTd\\u002fMSU5+5dgRrc32ryOHnl2wyFWbelGt3naxCgCSlSr3lsPqVCvaUCxBofj74iULUtNGbSoFZ+xwV7XbSbKstaOsFZ2VTODY7TMn\\u002fX7lfCTubUFO2i8XzXxn33EAMNrs+yo8vplMdjv7rtsTy+bEKGSv7jxstju6TWZXAtpNLOD\\u002fasmse+ZMYQcvGy\\u002ffvApfj20vrQKATqPJ1dHeJTcu+q3br95WWukz4S4XCd+87epHv\\u002fzJYLU5SGpziUfhSYxC9uotq7NjIt\\u002feewxZh31IDMmzz89OWz0hxz1\\u002fgs3mJJu1+matfntp1XWT8\\u002f9+3TL3vAe3KakJ3zxy69M\\u002f7HA1gtdarNpGH0cVMQrfCUFOgD6p\\u002f2pXR+98BTEhjhPFiAiRzqlT27V22g4A8yNn+zzj+qD2k4NdPZlrFV+ZLc+U8iRGp6nKVG1wGi2U9b+1n2oduusS1ng+bohpnbrTzWc3tPwMAEJcmCCOExJCrUPbZVdTDFWomoBsPydyeqq0p2DfQTu+bfrRdfvW5Bu8VzxLl6YiETccw16tervWXI8BlipNThDH8zCizdZeZaxhgHHSzvdrP44Tx\\u002fj8CaP71n3fvHFr2y7X7ShhZKo0WcaTkjTV7ehuMDdZKCsAZMrS+8q2n9aefePi+66zVhEhGifPChOoHLSj2lTXYetkgNnWvkvn1D2W+RD6SA7niuG93GLg+LgAAwxJfpEMOpGfzUH7mHQ7+IS7z5\\u002fg87lCio22Br5i5GD1fRQUOpD23C6D+RMV+PrDCBYDTJn+xIGunxvMPg4URwbmdfA5Wkb33YgQxk5QzSrRedQiNJgrv2t6+6akX\\u002fifB1CqP7617QskqORHzI5YgQT7Ejo7bGjuKaFGJhA8O2vRzLjkX+zaRNK0O763oaZOr01ThkpRv4jHe2PJ6lt+\\u002fs5KOl0RkqbfKS56t7goIywiUaYQ8\\u002fkmh71aq2kzeyRIAWB5auZvps5BgiGi02K+b9uG891oPoeNjxMqkUghEEr4fDGPL+Hx7RR1rLUR3S5I02ITvlm77mxn2\\u002f9KT2+rrWL\\u002f67uZnY43zxz\\u002ftqL0xXnLlqVmosMBG8nnChZ6roiwkh5\\u002fTxJCzr4bOGQlCttQZ8DZvNczsdFoW59BWpaR+eKhAwDw\\u002fYUydsJ9YmxcqiqsXqf95GzxqdaWqfEJaotlT12ta9bGwtRU95bDqsvcU4PsIuTxvrzhpmSlkh3kcPry6OKZU1MTDlbVnW\\u002fpaNbqdRabgyQJHJeLhGmRYVNTE9cW5qZFBvrt+NiSWbMzU749ee5sY2unwUwzjFwkiJLLcmIjC5M9mn6w3Tpj4vL8rO9PlR6orGtU64x2u0IkSolQzctOvXHq+EgZuo9fNvgE8eq61ddPzl9\\u002f6nxJU5vGbJWLBGlR4asn5NwwZTyfINKj0QuKblNSE35+\\u002fK7PjxUfqqpr1OhJilaKRSmRqoU56bdMnyAVCgAgKyaisn1YJncP8tm\\u002fLip5Y\\u002fdRvdVG4HhyuDJaIeNf6sPupGiTzVGv1prtDgD48UxZXnz07TML2Q93SYsM+\\u002frhW47VNO4qqz7d0NxpMJvsdgGPFymTpEaGTUlJWDguPTumn64dHD92dex3Z9szZGnrkq7Llee4C5MZYKqMNae0xYuifVyN29Wx35UyJjDi7tRbF0fPd69DRTH0jvY9Xzetpxl6ffOmFEnS5LCJHg8eUge7jpzUFIcJVLcn3zw9fLL72oCVsp7WliSK0c+lAmV+gbJnLo6FsrgT7kujF\\u002flvooKoMtYAQII47tHMB1NYLXdqzfUvV75hcBophvqxZfNvsx\\u002frfQwAjPZbZ6Ns29t3AwCO4Y9mPDAzoqczuAvN0DXmuuPqk+63CNFlV79b85Er274kesGtyTeIWTWwh7uPf1j3mZN2HlOfzFXkLIle0PvIUPLEye92tV5w382UR29a\\u002fEvWeA8L6Zi25SXX6Mnu+o+qj5RqW4ykPUIonRGZ9nD2\\u002fFSZj9nTFENvbynb2nL+gq5V57AKcCJcKM1Rxi6Kzb4mqRDZeH975XcNp0u1LQanTckXTwpPuiN9xrTIVGSzwA3sd+aEFAwwPi5AUj8kazaMN59lmwLCR6V8UIS+KtydtL\\u002fUfygg6Z70CseF8vrzcF3UQYKB83kNPigt1roNzR80WXxUnIykwbwJQygU3o1VcbeVG84gl9OKtYfarA2LY64vUM7wng3Qbms80PnTae0BJA4Aq+Pv4OOBHlCFzg4bgntKyFqWmvGnGQv+fqy3ZIcBONBUFzoJdwCYGB338arrf7nrJ3YPHAagWquu1vbO0USsG1fwt7lLRrFFuB\\u002fdVsu1P37ZakKnYkSIJYuS02fFJ2WGRaQrw+RejVaKO9qu2\\u002fglEhyYwui4N5esUc+2fF95\\u002ftuK0jq9Ft0CoNNifnDHxl9NnvnbaXPRsWCM5HMFDv0oRDgZj6M3n5ULgUA+Q+3DOeOJwHgExmN\\u002fApJDfaiXrFTOTU4p6+zIi\\u002fRoX4MB\\u002fGHO3F9s+RkAznd2nu\\u002fsdA8phaJ7Cif3bjqcLE6P76FFqWlctp0TlBnpSTPS+2zBHKzJKfFIQ+1ARMgkjyyc8cjCQCcYug3sUbfPLPSZw93x5L1oCGBhTvqFF36DRofO3KzUuVmpaBQA+nvqKLn0yeVzn1ze5\\u002ffHj4\\u002fdiYaG7rUP7NkB4H+HTr2y4xAAzM9Oe\\u002f7qJXEqH1d2KZp+bdeRjw6dAoD1p877\\u002fIVdZmUkz8pIRqOcQbNS1u+aNrhuT1JNeCL7F0h9NwZYjjwzR+6jZMBG2b+7lKe+N\\u002fV2pGcLgeFXxS3DMezzhm8B4NOGryeqCoav3dkJzZkwgeqF8c+4Orm7iQnx3MiZ7MiQk\\u002fIkT+f+FnnedGnqnSnr3q7+EABK9WUkQ7Lf2FF\\u002f61pt7Q7aCQCpkmQk2w4AOIZnyTKyZH1Of\\u002fyuaYOVsgLAjPCp96XdgYzOjZxpIk2uX\\u002f7Hls0LouZ4TxoIBcvicsMFEq3DUmPsqjGiE+ERDWb1pqazfz6zCcMgRqSggemwGn5qKtnTVvH1\\u002fAcy5B7HjWq76fET357V9MyBFeI8K+VsNGsazRoBTrAT7hRDP1O86aemEgCQ8oSxYoXaZt7dVr67rfz+rDlP5i1zbzkAQf3OnOHA+K2j7BfN+CjmCtrgfgcAYHz9GoNJP40MGnz82lcy7\\u002fJhbHCtUQb58JOavT82f0gyAaVZxYRUREiEuFiACwW4SIALhYSIjwlPaPagm45NIfJuhAtirk988JvGN5F4u63xq4bXeRg\\u002fQZKu4keICAnJkBbS2Gqt66u71OLo6wtVfZ47eAudHTbU9pQQd\\u002ff4SW8XF2lsvYWwpzta7xk\\u002fQvmxAM2MT9p2492vnDryfUUp+q\\u002frpSAq5vHJs5el9nkMPOr+dHAnkm2Pkkifmj7vuqw83kg1u3aJEEseKZz+SOH0E23NX1w4u9VXEfqbZ45HSaR35U9C4sEayecKRD8nNjxM4GB6L13aaI\\u002fS6cAhGXbva55u3p9cA4Ak3GmgWIND46UlyyIkErHnkpIAsCIz6\\u002fez575y7Ai7c0KYSPTemmuipSNUlhsjlTWxFlmOk\\u002fvIXnE4HE4osDlJV9MhhVj0yrqrXLXw3ggc\\u002f+WimR8fPsUw0KD2cb2aM9wOdh11NQ8REaJHMu4NKjF6uPuYhbIAQII4bmG079Oq5TGLt7Xv7raru+3qk5rT3rndIXRXyi1I1ntkrIpd5vN5p4VNIbCPKYZy0mS7rYO9iOiov3XueiuNQ+uknfxgloDTOfVFmlOu2+uSrvcc7LE4ev53TRvttF3r0J3XlxeqCtAtQsDqxILViQUAsL7hzPNnf0KHPTlp6s9nNq1OLHhq\\u002fPIIoQwAijWNvz7xndpueqN873+mr3NvSTPMr4q+KdE2S3nCX+UuWhmfHyWSUwzdbNEe6qieFO5x2fudygM\\u002fNZXIeMIXJl2zOG4cgeEUQ+9oKXu+5Of\\u002fXTwSJ1bemjadvX1QAv+dOcNkMGsYMsB4Z9\\u002f8r9bos+mBnR5sOZTdV\\u002fcYn88VUvi+OmDIeMqp4YvQ6KD5WYAxdCDn0QDg9NU6IxgDP7svUu\\u002f+ofl9NHqJhJBlyManSLPjRCkRwlgFP8xnPxOaoQafYg4FIfVuTA6bbyaNP7d+gg4AkIyzwVzZgIZ9mBe1emXcrWjUr9DZYUNqTwl9PByfl5iyqbrcHekwD2PTiwET8XhW0un+l5Dy+WIeX2e3MQwjFwiVQlGaMmxybPz8xNTC6Dj2A0PNibbmXfXV7EiSXPnt1eviZf33mh+SlKxP0+MSp8cl\\u002fmmm8f2zJ7+8UOKkPQ5+Xji2f1lqZpx0aLKXI\\u002flcfvRzwiwkJOy5Qj6XhgiE2XPpHp\\u002fL+7h4z80ZAKSYYpBlIz4lKvr8S\\u002f3FtOmL0tJ+qqxsNuhFPF5BdMw143IVQnSmxvCZkZjYdKH3DW83heJn2RCyUc5qU1u1sb3TpuuyGyyk3U6TGICQ4EsIYZRQESVSZshiMuVxYmLkDvpf3LQvMVwxLyfNTw8QDodT3am2OpwAMC42sq9su4uQx8MxnGJo9zIDnJF03tBziDw7Yrqsj27dfSnV97TjmB4+pa9qRxzDp4dPdvUKL9GfH9qsMZuUJ5kWPjoFNdPDp6AhAADg47wwgcq1pqiJ9KhsGPW3LlGcoODLDU6jzql\\u002fufLNe9NujxPFoBv14YKhgmJoAEiSJMSIfFdJC3BBijSpylgNABXGqtBMuAcrTxX3j8nXuZeamBSe\\u002fEjO\\u002fBfPbS3qrmNvtrP1Qom2GcewD2bdUXgpvU5geIo0IiXdo5GLzmH5pPooADxfuHZZfJ57y6sSC5wM9fSZjW+U77s6aaKUN\\u002fBDzQB\\u002fZ84wGUyjc5+PFfjtz+BzyrLPdHlQbL5S9sK+T\\u002fpChM9eFnxccFXc7Wj0ysDHBcgfFQOMk3YM+NqJzz+MQDRZqje2fIhGAQAgVTpuYfQ14+ST8Mu3CwciBN+NeVGrI4Wx3zW9M4AckZSnuCnpF3mKqehAf0Jnhw2dPWWsQFK9Bvtgv3SGXKfFfNOmr91Lp85PSn15wcoYaXBnPSHix4u9vRBdXlm0KpBsOwDoh\\u002fmfJk4q\\u002f8ucxXflFz62e\\u002fMFdW9TEAdFfVx65umZC1jbDtZIPpdP\\u002faQt5LwwI2v6T7OlijUYhBarx9UVBb\\u002fPjpCW4D+vvTk9Vwoa4e8eABgXGTXOs9vMSLpzYuH6C2Xuu0caGyxOp4Tv42rwmNZp029vKz7WXVmqa3Cd0vuHY9g4ReLMyOwVsYXJ0uH91zHbHd8eL6Fo5l+bD8aHKebnpN00o2Bc\\u002fPA+KYczFrlb3pntaIEeYltpJUXTAJAZ0+c3yLA62r3r++b\\u002foVFPsyOW3pT0ABq9LNSa6l03chU5HgMBqDP31DmlSpM9RzykSlJcN6pNtZ4jQylNmtJX5npY8XF+vDgWjV7irtgiPesvRv2tIzD8tuQb36v5GADKDOW\\u002fL3l2omr8ouh5haqCfmc51Jh6crVJ4kTPEQ\\u002fuqn+13fd88zHnhpTJyMLOeco4ADA6bXaaFOI979vutnIAmBud6c629+VwZ7WNcir44hWXsu1uqxMKXjy3zeC0HuuqXRqXi4wGLsDfeQwZW5\\u002fYNsrjSltQbL4adUoJfyfVSr6PWhAb1TvTf2B8\\u002fgSFr+cKKQJcKMTFSIH\\u002fABKIlw0ZT+H98u20deBpxIH+eW9s+Z\\u002f35A8cw9fE3z038iokftkLzXcjVzHlD+Pe2tL2WZF6NzrWhzBB1OyIldMjlvivH+9L6OywobOnjB0eVbDEyHY16RfFMHdvXe\\u002fOti9ISvvfyutGuPXKEDre2tOu0GVceOT0OH9H42xqq49v8yGXrgr\\u002f5up1a374rJHVnGNfY+1wJMFH8rkQ\\u002fRxARwjiWq017rsdtgYTqZPxVL1bBKbWdI59V+NoNzjVPtPuakcrGgqy7J1iSNrz24iHDfBTb4wqiIm5u3DSp2eLXXcNdvs\\u002fDx\\u002f666LFnluNYRWGlo9q9hzpLmf37ekXzTAX9E0X9E0f1ewpDEu7J33RjIhsdKMhUtLQRtE9v1ur1vDN8ZKCpJh+E+5n6lvYd7Njo2SiK+tPl3MFyoiKkAj4FoezrLXj66KSW2dMRLcAsJPkj2fK\\u002frXtoOvujVPGe45zRoLx0iLq0cJIz5H+GS491mdDFTeVoGfU4OzZfjgoeMM+edAnBU8+gER\\u002fKLx18yJnC3DBp\\u002fVf650GBpizutKzulIZTzoncuaS6AUJ4j5n1OqcPSeiR9VFR9VFnoM+INX9Y1e2IhqJuGvPKZp292i9aOgEgMkR\\u002fi6luFQbugAgQx7l3aCfhxPp8shSbUuVoWMwCfcAf2fOMDGQOjQUMLWjHQ0ByPkqNMQSJvBxRNplb82W+\\u002fgKDly3vQ0NASh9ne6FGpUgosPWzI44aLuDtvufKHC5kvFUHeDxbgCA1tEl4\\u002fn7GvLDOqA0Yo2pzOe6oDckPjwtPLiz2qAyCaEplN+NUv2xYu1hdiRMEEUxpIU0UQzJx4USQiomZJHCuBRpdookO1maPYDDIbYQ2WFDZE8ZQzosHn0XIsQS9t1R92NVWbm6d52ev85ZMnaz7eDVsWdyTBAr6p3p8JGSHQ4KgfCxSTOfOrDDHanTaxkY3AdEH0byudj6SbjHilNL9b0foAwwR7o3rYi9m7VJ\\u002fxrMF9psdeyIg7atb\\u002frPPWl\\u002fwb3OHOpM55EIBHm5z0RqkYg4yMnvl4Fn5i9oN5l2VF903f285KydJJ+ev2AkO9sMB53T\\u002fFrFzzvbzqIDQTqrrXvidN2U8Iyn8q5LlgSdPOpXZXs3EslLjEEi3u589zv23c9\\u002fcfPk1N5OvhzOZUnE5z0wf9obu48CwN9\\u002f3vvl8bMz05NilXIegTtISm2y1Ku1xQ2tJnvPvKUV47OvmYTWeHKGm522uycSiYjg+gPQDO2ke6YvCPy2FRZeOlNyLbM5TPysIjOsBMFXPIXOWzcjfOpEZcHezgM7O\\u002fZ2XWp9s6N9z872vdPCJ92WfHOU0EdOLdhfJpDJamNCgK1djKQNAJR8H809EGbSDgBSnu8\\u002fIdfTGZ2Dmv8b4O\\u002fMGSY6R+9JfrC67T4S7hHCPufTAECsyMdlng6bRzXcAPj8CbGifiZwhIIYURKSvwOAVmtdqnQcErwSRAiie6vtLum2tyVJMtFoYHQONRoKQInuKBoCGCefFGx+GQDMly5dj10h+25sbftyf+dG910cI5bG3Lg4+rphbTAQIjtsiOwpYwUDcKzF4zsiK8zHoeMo2t\\u002fUm7FUCkWpSlXv2FjjpCkL6TF3PEzU\\u002f9GmC8Uw+5vq0eiwGR\\u002flkSUjadpOkqLh6R87ks\\u002fl1s9PT5XmI5Ei9dYC5bx4cToS7wvJOLe1fYxGAerMpRua37gu8Vfu9bgAwEHbTmv3sLbqYQqm7qPbjl6QGfBlxrGLwPG3V6954\\u002fixt0+ecDVh+K7s\\u002fK6a6uty8xanpxfGxo3FDjOnNDXPnvtK5wji6ot\\u002fpzU1dx59\\u002ffe5165JmIqODc5Fz4S7iM\\u002fLiA6tbxQOJ3Q8vGCGg6Q+PHSSpOjaLk1tl++2EnKR8P55Ux+YPw0d4Aw\\u002fAS7AAHMtoeOgPZq29QvHcAEucD3KcSl97JP90mJTYiLQg0K2YH+x0BdSb52IEF4Vt3xV3LLz+gsHuo6c0p510k4GmBOaM6X68t9mP+rda0h46RpDujQ1Q5bmOeiDn5Y7Y0uAtXsSQgAARmfPv50fcr4IAMyk738mVzpexgvuShgiwN+ZM0w67S00Q3vXIQWiy+4xP9IlSuivli1BnO7+SHdrszWy7w5Am62nBZYbBlicOAUJhqBEcfo53TEkWG+uHOH8XYiIEvoo9+nyOr8OXPuA\\u002frTqzRVoCGBm5HI0FAAz1TPdauwKzXfjYNdmdrYdA2xd0qOTwub1bjE8QmSHDZE9ZazYVV\\u002fdZva42DMr3sel31HELgk3Oux1em2aMow1PpbwcULE49nI3uksZqe\\u002f8wi2jRcvtJqG7FOiX0aHx2GwlM8fvgz4SD6XWz9PkCjOkhByC9W7bzhpx5cNL92Z+kysKLV3uz6QjHND8xtIA3e3Et0BtaN1RezdKdI8DDALafix5S2D08eVvXZbPQNMgCcD7bZ6JKLio\\u002fNkh5CToo40NZ5oaW4xGvU2G0nTyzMy75pYiG434nAMe2LW7Kuys\\u002f91+PC+uloGQGuzfVR85qPiMxhAokKZqFRGS6UqkUjC5wsIYki6aD02fQYaGiIbm4teLt9ED3X5m4MmXyxbX2fu+FX2anRsEFo0vc2hACAjOoLAA\\u002frr5XCuQBgGjy+dfd3k\\u002fJ9Lyk\\u002fWNdd3a\\u002fVWm4OkhHyeVCBICFNkRIfPykhZkJMm87uqKmf4YIBJeRJXx49uuyZdmopu4ZeSr+iydwOA1qFDx1jco0q+v+7DfblsGpKwhdpbhwFWoMwvUOabSNPujgNb2nZYKKuVsr5+8b3XCl+SeKb7FZd+mQxZ6j2pt7GHOACQIY+qM3WXaH0UBSOyFTEAUG3spBga6SpD0lStsRt89YThjCFO2tFha4wTp6IDAagx+pgcnCT2V2Ip5cnjxaktVo\\u002f5x02WagtplAy06ZbW0dVpQ1P\\u002fiZIMQcgvmgoAGTIfreqqTecXRl+DRq8ACRIfdXW1pgtoKDBm0mAiPU6LAuQzcZklm4CGAoD8qY9FIfhuqO3t29q+ZEdmRa4YgWw7hMwOGyJ7yogxO53SgZZstpmNzx\\u002f2KKsNE4kXJKWyI6MuXqYA6PkWoxlm3U\\u002ffPFI4Y25icqJcKebxx1wqJ1oiZfcrL+lqYw32qc1k\\u002fL+ig2h0OO1r9PhESpIPY6n0SD6XWz8JdxwjJqrmH1NvYQcNTvX71U\\u002fNj75xevhKKa\\u002fPU7taU+mO9k9arbXoAEuz5eL\\u002fap8RERIxITc4u71XAnFx0LYWS3WiJAsd8KXKeAaJRPqt8hiMr0rPvVl0HGmQlBEezr771K4daqs1Vip7cclSdnxkZEdEPrdwoYAgtl9qLwMADECTQd\\u002fE2gOHyjAl3Nc3HXulfBMaHTpf1R+iGOaJnDXowEB1GjySF2GygZQccjhXlKRw5S8XzYRFaJwTIlKlyef15QBQaayaHj4ZHfYrU5bmyhrXmRum9f3YenNPaQ+S0OfhPTPhSKbP6gy1Q+Ou8r6chOxbJ+PJrk1YPSti2nNlL5lIs4k0ndYWz4uczd4mQ5q2G\\u002fYDQIOl\\u002f5zyFWhxXM7utvL97VUX9G2uFUr7Mic6Q8YTGp22bS3n1yR6ZFi2tJSaSbuUJ5wZ5ePMnzOGVJvODyDhbiL13ukzHsZP9JUJYstVTEEeSDPUecOJ6eFL2MHAeRecAkCOvBANhaRESYacpzJ6zqi+aCzRODrCBR4z0K8EKZIsHCOQFdHqLZVWyjyAVS4vei7kFiAnbfduNS7hyQe2HOVF40B+h9ARmu\\u002fGoe4tyG+1MPpa9t3hEyI7bCjsKSNpzQ+fZYaFX5eVvyQlXUj0k0VkO9HW\\u002fOu9W5Dy9ocmTuVfOkYNEVdnjttUXe6+22kx\\u002f+3oXta4Bz5OKITCcJE4NyJqckz82oxxodaSflpsIjvhXtzRVtLVPjHK31zSVpPhvm0buiyBVuGwWUnnb\\u002fZuXTeuYH5SGoEFennibGfbx6Ueydtlqf7KBVxG8rkGr\\u002f9dZWbkmiLNduSjhGScezu+Pti5PlU2PkmcFSlMEBFSAS6yURYLZWi11taZSjvtvs+vchUzyg1F7IiNsvhc157ttHZ3IAl3vbO7wVyGBOMCboATOIphnty+9eeqSnTAi1wgXH+hDADumliYExmJDg+nss7OV44e2V+PHoiPLUe6Kl6r+AmNXoIBli6LSZfFZMhjkySRUp5QQgglPCHNMBbKbiHtJtLWZO6uNXfUGNvrzZ3o4y\\u002f5tuFwsiTy+qSZ6MCA6CwerVQVYq41KofDGdvyFbmuhPvh7qKbEq8TEUF8rBWqCo6pTwLACc3pm5Ku9TlfjWbok9qew6DxSo82\\u002fdJL5y1ah95Jk3zcx6HLWV0pGhoGPFb\\u002fdxttC+pNGJgQf+tiRNGzIqbv6tgHAJ02dPGSAmUejuE0Q1eb6jpsXTGiKGSDK9zqhIJPqo9VGToeOPrZk3lLl8XlKQViiqG7babT6gatw3J7ek8Rg5wv+uW4hf86v+NvJVv4\\u002f8\\u002fefUe3UWUNAH8z6l2yLcm918R2EsfpvSekEhICofe2sJQFFljaLp2FpZeP3gIhIZAE0nuvTuzEvXfLkqzepZnvDzmy9KxmW27x+x0OR7pvJDsaaTy6c999OGV+VBYFwx0ksael5JWiHQCA+9JnONvOIMNXkebkDHGPKz8uqU9BnWEAAGm83ICLVeSJZu6TbYaCZzsO9i7hTgLyvOowHAVgYMpd+w4D2DjRjCPy7e5BEpDHFTuXR9\\u002fuHhwJaDgjiZNV7bmsGkE6ijVn8sN6XBZRpD4Fh4LgtQM4HetNftnkMJTrLsDRYWVovhoVukL3uywKRzhQKyQPkQ\\u002fsUPikeFWjVi3Z\\u002fB0cDc621Tf7yoNbCcfeuuq9ddUMCjVPGj0tJj4\\u002fMiZVFB7hI9GsMpsON9ZtrSo92AAX4GaGRdydmw8FB928hJR1mTkby4I6K7YRDqXJqDQZK1XKbVVlr5w8tDp99HOTZwkYvTkZc5CkwWbVWSztnuvKEoCsUXdw6Qwunc6m9mx6wYLE1N8qPPKiD+7d9u2S67y2zrcRji0VJa+fOqy2eGSxgkeQYFdt5a7aSimHOy8+eVZc0rSYBC7d52FKbTH\\u002fUHzxo4JTFkfXdTsaTrku3cv8FchA\\u002fqy+C3A2BgAIo0fmhy04o9wFDwBgJ21VugtVPTlqSxhxNyY8\\u002fVPda+W6c\\u002fBYN+7XDC+o9k8KXxKwj83h9s1QmTwGsHh26Pt5vX70iCvbzqbRssRiCYe7s7LCcysAAJiblPzNxQIAwP7a6oFMuH9VcP6NY0edDdyHL4VF+9KlXwgS\\u002fi4BAEjjRS+JHjdPmithBjsZpNWkOiC7tLOloFrfBo8B8F759nGi5CRuCCZlW2weF\\u002fwFrN4ceREEQYaO2eLpvzdvtxI2vV3\\u002fVe33D6be7TX569Xk8Ak\\u002fN\\u002fymtmlazbL9ssPzpbPhLQDYIzugsCgBAEKaYHK4x1l4JFNKw6k2wm4n7edVFyaHT3AfBQBYCdtfrbuhYH+g43QGznDWgzcYm4SCYP\\u002f69NrQf+lMjs5Tcw4VrucS0YVTwiccV5wmSOKr2h+ezvy7+7I9Q8HCve\\u002fBIQCi2cK9Cx513T3YVn5aXquzm\\u002fU2S61eAQBoNqofOPUTl8bkURlcGvOetOm9S3ZTcconk9c\\u002feGpDhVb24sXtL17czqTQrITdec4zTZLqSrgDAG5LmdJq1PxQc+rxs5s4VIaIwVZZjM7u7esS8+9Mm+baEgCwrbGwWN2is5l1NkuFVgYAKFG3PnhqA5fG4NGYXCrjkay5UGsaZNDVGyqaTDWxPSkScpCOQ3IvE0BzBIHnm0YwolK5OVV6j8xCvaH8suZ0dhAPh5zrONi9+3ASJ8t\\u002fK\\u002fkhZUr4wqPyP6GrFycUu8YJZ8SyU9yDI0GOYBKURgQAHGz\\u002fPU80q0crDWhtqt6ldykYlY4zrYRH6sfg8EhFBelQ+1ZzD1fwHmqG5quh8ewDbCftDtIe8FJfqAyRD+ygf1K8sjjspUo5HA2O16wLxOKwn2xpONnSeczn0xnxfCGXTufR6TScYrTZ9DZrnUalMHkvqA1nsT9ftMpXWn9wvTFrUQSL\\u002ffEFj+LgYNgJ4teyS4cbazcsuz5FGAYPu9lXX\\u002f17RYnOZtFZLTqrVW+16KwWXw3WzXb73I1fO29TMMyZeefRGTwanUtn8Oj0R\\u002fKmpHpLoAMAFiamZoRFlHd0VcM067RLf\\u002ft+aXLG9NiEeL6QQaForRaZQX+2telAQ027W2H7\\u002fWMnFshazrQ2uSLBkxn0G0qLNpQWYQDE8QUZYeJ4voBPZ\\u002fLodAdJGm22Fr22vENxSd7m6PZme3j85B6tVTuQP6vXgjomLoy8pVx7XmPr5efWBcfwFTH3YwC7Lu7vn1c\\u002fpbS0wlt4uibqzgPtG412LQDAQTp+bfjv7Ukv831fOy3RnjrXsQcKRrGSuFQhFOyjCqXi24sFAAAGhfrktOk35uSyqFQAQPL778KbApAXHY1jGEGS51q8NF\\u002frJ99dvPDqES+VJsPO26V\\u002f6O3wdbYIBu+BtMVLovOCT\\u002fc4RbFENyXOvDFhxo6W859X7VZYdO6jNsLxZsmWzybe7x7sHZvD46oPvf9XY0AQBOlXfBpvZfTSTU1\\u002fAABOKM9obLrr465N9VwJU23TlGjLG43N6+KudY9TMeptiTe+X\\u002fkZAOD7+p9JQM6XznYdwAmS2CM7+FPDJufd9fFr3QvJAQA0nJojGF2gKgQAfF\\u002f\\u002fSyRTmsiJd43KLcrPa76Rmft6ihKkdF7qJU0xAGBj45ZEdjyf1st+x0Ea9Jfuj+a\\u002f2i2KKeH5mbx0Gu5RXEMC8qj85Ell59eSHM\\u002fieqd1casvaUq0Nl2xtvQ\\u002fJW+vj1+Tzkt138BKWCt01RfUhSaH+d7k292Hhogjsspf6zxqREwO6xFZpevuusT83iXcAQBRLMHGWfdsayzc3VxSpm3T2swcKiOCwc0Vxa6MHwNt\\u002fM+cxbMi0zfUnLmoamwzaQU01qSIpHVJ+dMlHi8pAGBXc\\u002fFhmUcJiNZmco88mDkbJdyHoL9afrg35YXgT25PK\\u002feqrPDnl0lh5wo9mjv5Mj9yTVUVXMr3Z8sPqdwcJsV7xaJXBrtud9svcBSAedLVcGgIC2dE5olmQnX6DtKxoeH9R9Le6NELchUYJ5rxV+sPNs\\u002f1tOWW1rMdByaFz3cP+rer7WfoSYLHowqUVo\\u002fvgDbC0mZuiGR2\\u002fRULqMlYfUzh0Rp3mBqCrwaLwnHfuTbCsk+2eVHkDW6b9KMh8oEdCp+UQae1Wi4rZHDUhySB6OslqxP4QnhgsNkJ4vfKku8uXwj+39KdzKC\\u002f+c9Nf625JYzp8x1YqpT\\u002fVRO4SUZ3DpLUWMwazwr06zNzfCXccQx7c9aidds2upd1Wx2O3ytLfq\\u002f0t9LADZk5T0+a+dnF071LuLuQADRoNe5tbfxbnT7qwbE9vt7vNJA\\u002fq6eCygMycPZNCf\\u002f8suY56MpqT82X3pTAGQUAYFG4dyT9+9vaFxXeFgBxSueNnxi+pNlUdUF10BmRW5o\\u002fr356UeRtOcLp0MkoCchTyh17Wr\\u002frPq0yWzAdivTdxsuXnVf\\u002fXp+\\u002fYFVmFjzsiUWlxvD4jVpNjaoDHusfTVrt60ePwNFh6LK64Ug7fDjI5Me8N\\u002f4uAc3nUSwgHMOWxeRPl2Q9ev7rcm2z+1Chuu60snJSeODmRf7RKBSL+6rQlh63x0UQBBlqVsZc02RqdnY4KdaWvlhcyqfxpAwJg0I3OywKi1Jt0wAA4tlxUMIdADAxbPzK6Gu2tuxwkMS3dRv+aP4rnZfKpXL0dkO5rlJj0zo3Wxw5f1qElxOga2OWFaovO0iHxqb91+VXMnhpUSypg3S0mdur9TUOkkjmJArpAmdmuV8tipzrTLjXGRqeKPxXJj9NRBNaCavBYeywqhZK58wSh\\u002fisY3BfOjNhPiw\\u002fdlh+jIbTYlnRUqaEQ2ETgNDYtDX6OuceBwDMk8yKYXnpQh5OD3s07YH\\u002fln9odJgq9dUvl7zJo3IjmVIGhW4lrCqrRmFROs\\u002fcxglz4Qf3myRuRPHKl+CoDy+OWfbimKAafbCpdF9Pm8aX+Bqi49Q1CePXJIyHB7yZIk6eEkSv9k8mr4dDPvTud0b6SbX+8l8t3y+Lvg0e8KbBWPFny\\u002fdwFIBJYfPpeFDdrpI5o0YLJhZrzrgHO6yyr2tfvyf5X7TgnsTsMH1Z84rWpoLiKdzs9GHSwN1lUdSNlzVnLIRH\\u002fa\\u002fC0vpx1XN3JD0TRu\\u002flFFi9XXNZc2Zy+AJ4YAhjUTjjRbNPKfdA8W0t38SwkoKsIL6kOXW+4xAcDVoCJ0NphTNfBaoj10TdDAV9UVnl39a9OXzzmO6G4KshZsRAH\\u002fz9st\\u002fqDGX5YXPiWKlCejgNZwR\\u002f+bAXhsIHdih8UoYLCobdlZv\\u002feP405tCrRKxQKf6+\\u002fy9oWkAkh5siDOMzmFCBAkmSVsKut9pkRn2DVm337CfRatC9c\\u002fb4qzOCev\\u002f0t7GSqI\\u002fmL\\u002fv7\\u002fr+Mdu8V9BAMgL\\u002flTX5iwnQAwIzYxDdPH4W38C3oVupe0HDKg+MmPpo\\u002fLcjnGMif1XfBvt2jWMm3Jr7wY\\u002f0rAZut+zJPeuMMcVelg4AWcV\\u002fK23+1fHFRfahrIwAAABjA8sMWLo2+BwPYGOEsV8IdAKC1KTc1vrur7dsU7hgxI4ZN4VkJc4dVVqo9rfWc1uRExWjjRHPgaJ8db2wAACQIhQGz7U5iDqdRq1EYe\\u002fnS9dSPhRetnhXWLnQKZZRYkiwSRfF4XDqdSaW6VlQbgn6o87hqDQDI4Md8mH8Pl9rLUjJ3Qhrn4\\u002fx7Hz73RanW49rdL\\u002fVH+55wZzNo7gl3hW6Adj2CIEj\\u002fwQD2UOo98ey4rS1\\u002fmR0WAIDWptPaPKYKAeDz29X1cdeK6KJfGn8zO8xqm+ZMx3n3UQbOWBO78poo72eoyZzE+1Pu\\u002fL+ab22EjQRkma6iTFfhGk3hJj2W9uAh+TFfWeMQGifMXR2zfEvzdgCA0WGEfqKrv0poDeJLR7+yJpuNsNUa6msN9Z7jAMfwhdK5N8WvheIuGby0f2c\\u002f+1n1N1X6GgCAzq7X6fXwRgCI6EI4hCAjzxH5nyQgF0eu978cYrW++Kf6\\u002f3VfDJlJYc3pybqF18bcXaMvNjm6JpIDAOoMZV\\u002fUvLI27kExw8tVNHdt5oZfGz9pNsH9eakYbXXsvVBw6BPSwpfH3L658VMoLjM3fVj5zwXS6yeGz6NiHhN9\\u002fNDbNVX6yxdVx8p0F+g4I8j83dAxT3rdedUhKD9rI6zf1L5xU+JjyZxR7vHuzqsOb2r8tHslXPDSeLkFKriA7Lhi5xjh1JggOi+V6y7+3PCB0Q6fogxTQ\\u002fDVyBfN6t5NpVpfXK0vhoLuMIDRcSaLwmZReVHM+Fh2Sio3u0d1+i5D5AM76J+UAfPT0ut\\u002fryzeVlVWq4GvsPoXxmSvSstan5XrqxZ7cJUq5Tds3+heOb42I\\u002fv+sRP9d4ZxMtlthxpqXz992L22emPZpWcnz+LQ\\u002fP0RHzALElP\\u002fvO6W547uc7UA8mVUuOSFqXMmR8c5746OkIaz2EoffYG6Y1Npv6+66bvigt21Vabg8vsAABaVtjAx9dH8qUkCETzm20D+rL4LNuEOAEjgZD2Q+t9fG95tNlXBY36xKbyVsQ+O4k+G4kwK+7q4v8+QrC5SH2k2VVsdJh5NFMGIyRPNDaN3nuGlcMdEMhPbzHXuD9TZOi66ZeH9yA9bGPJ+MgCAVr0OADA2MsBpqAuLRgUAuGdg+9WxBvj7MAAgWyK9f8KEeUnJjKF3UdErtc1wQl7mHqFg+L9GrwlJtt2JQ2X8K3vtbSc\\u002fsLv1\\u002fT+jrFJZ9SI6123DHgvnslWGruvtxU1wSQKCIMhwhAFsRfSSuZKZxxQnL2tKG4xNervBRtroOD2MLoxhRWfxMqA24u4WSGdPDMs72H70grpIZm43OkwcKjuSKRkjyJktmS6kCeAHuJkaPjGNm7xHdrBYUyq3KCyElU\\u002fjxbKip4RPnB4xhYLhXius+8N1sStyBKP2tx+u0FWrbGqSJNlUlpAmjGfHpnKDqmbqhcF66VbHLM\\u002fkpV1QX6o11LWbFc7dTcFwNoUdxYzM5KdNj5gcxYyEH+Ypihn58uhnLmtKz6oKyrSVapva6DDRMJqAxo9iSTN4aXnCMXHsGPhhCDIiHZX\\u002fVaw5O1+6dpQgn02BT0fbLc2nFHuOK3Z6TdAsibqZTe1Bnys+TXRD\\u002fCPf1r4BPVudoex\\u002f5U9MF18zTjg9ipXoPuTUbKopUB05odgFLZ3ldG3s3QGT9cEgAWlxmM2E0ewwaGxeJgrLzI1UnMbE2UwKO8i6fv8mhs1tMJSf6TgAxQ123R\\u002fNXx1s\\u002f32McFoKNzuJk9m9Z4XerpFbWuSWljZTQ5X+sszc6HUfDRcCWtgC6dodrT9BcZ1d\\u002fXnVS1MjFk8JXyRhwsdtO2mr1hc7K51dQTrOsBI9nuw7mj+RTeEaPTuV2wjrFzWvXBd7b44ATiy4NBqrDrVvvaw57f76M3AWVAo9vAzBV2OsaMbZjoM1Bngyun8kIC2EyUKY1DZlq6nOeRUhnp0+Q7x0THC9sNwNhQ\\u002fsoH9SAADvzV363tylcDTUEgXCx\\u002fKnPZY\\u002fTW40FMhaLitkTTpti14nM+r0VqvZYTfb7QAADo3OozP4dEaKKCwnQporjsyPjKHiHkXiQ4fV4Xho33b3bPtrMxasHzXGbRN\\u002fWFTakuT0MZLIeRu\\u002fcWV+7QRxvLlhYSLc7s\\u002fp4bzJD+f5\\u002fMD2h2Rh2M\\u002fLr7+skO2qrTzX1lyvUastZqvDzqRSw5jsBL5wjCRyXkJKnjTa\\u002fVEYAOdvfdA9EtA4adQ46VIb4bjY3nauralGrWrUaVr1Or3NarbbzHY7juE8Op1Lp8fzhFnh4jGSyDnxKRxasFfF3A3kz+qjnuVew+hR96W+VdCx74j89w5rgA7sAAAqRpsYvnim+DoO1edXQQkjbr70JjjqZp50\\u002fU\\u002f1r8HRIHCogrnSfukj5kydO\\u002fu2B8NkswEAeIwQnAgGo0HTdYXN6abcMS\\u002fPmYv3ZfbFgDutqHDPgwMAro2bnMoLwem7u2Su9Lr4KRvrj7kiBEmcUlQsic5z26rHYsIEVbKuKRdynaGooS03PkBKAkEQZFjgUjmLI+cvjpwPDwRBQOOvilm6KqY3XwzEjAg\\u002fldQTw8b\\u002fNOkLOHpFr3+oV+m8VKgXuS\\u002fZgiw\\u002fv5XLf8f8Bw51M1gv3Sh+5ih+JhztuWxBVrYgqHmBCDLSXBd735am\\u002f3Olezqs7b82fow34bGsFAEtnEsT4ADX2lQKS2ur2UtVjVMWP29K+EI4GkgWP29Z9G3bW76F4nbSdqh966H2rSK6WMqM41IFbArH6NDrbZo2c4Pa28Rip5ni5RPC5sJR38wO04H2LWaH0ewwmglj5w2HwUwYLQ6z\\u002fxTYzw0fuG7jGM7AWUwK25l\\u002fZ1LYTAqLibMZFPYC6ZogO+QAAFbH3qu3a0u0His3OGlsHUfk24\\u002fItwMAqBiNQ+UxcJaVtFgcJith9nrtYVibKV5RprtQo4czqiQgjyt2HlfslDLjxIwoPi0MA5jBrtPZVQ3GSqjUl0lhP5D67\\u002ffKn\\u002fS\\u002fK7tjUlizJCt2tm6A4ka77oe6dyTMmFH8CdGsBA6Fj2G4yaE32LVNxppqQ7HS0gY9ZIZ4WQQj6vcmn3\\u002fmhr4h+GpQMMqtSU\\u002f+VP+\\u002fSl0RPNZDDcaKn+orClRH1sTdz+threRQ+MAO7idl4InZnEVJaYuS+toSYCj4vbKkRt11NXdufHLw2XaXaC5\\u002fXkLyn9Xlrki123MOEdkR0uwIKRztBzScMiEyZkIkfJGpPwzkz+q1YFPGLhjAxoctyAubX2coLtWebjCUycz10JRGNpUfy0rN4k8aLZjK6laa0VOZ\\u002fAm5whlF6h60EAIAYABbHftI33+6V+FsdqtO1+ZtTrRXtWo1AEDC6ZdfpjuLZz+ZOL7gpdlzhle2HQBwUVULRRZHjYMiIbEkapx7wh0AUKSu62PCPT0y4nCpxwTbD\\u002fee+PzOa4fdXkAQBEEQBEH6VRovZ2n0LVBPdoIkGoyVAFS6B32JYiasT3gUjgZnhngpCYi\\u002fWn7wmuhRWeXdl2b1ZYZ46bLoW+GoXxbCeKj9DzjacwRJmBwGqD2O0yzxiuAT7jhGuTXxH5uaPvPfVdlO2rwW3V9NcAy\\u002fNeEfH1U9p7B4L7OTmRtl5kY46oaCUdYnPBrFTIhgRMl9r9zmywzxssuaM41GL3Pr283N7eZmOOpNnmjmsuhbu+edh50h+GowcNYs8QqlRdbRrb98L5Rqz39W9cIDqf\\u002fh+q7U7G4ofGAH\\u002fZOC9NqOmq4uiwCAG7J6uZ4Q1H+mI+hOLMhVr8cJdycMYEmc7CRONgCABKTBrjE59HbCSsMZLAqPQ+XDD+iblTEPqqztjcauq0b+YQBbHnNfOq9POVM\\u002fMiPErTrd2ZZmvdXKpQdoz3S+pUVlMgEAJsQM0LUXKYfTpNW67k5PSKAM1Vk8ftTq293vRjD4owSx7pFQyeDHSJkCmblrWkCdIdjvFb6MT4r54qBH5ERF\\u002fXO\\u002f7n525RweM9gzfgRBEARBEOSqp7drZoqXk4Dc0fKj16y3f7HslLuTnmPgLHggaDPFy0V08caGj61E18z6HqFg1BUxd\\u002fSixH4IwjHKuriHEthp21u+C+Eik8MRm8q7P+XlL2teaTMH6P\\u002fbHRWjrU94NJM3DgAQw0rqRRqRitHuSPrnR5XPdlg9vhUGb2rE4pUxd2IAi2BECekRaqsC3mL4GFKvhsGuPab466RiD9Tlpo\\u002fkltava177W9rruOcylf4NhQ\\u002fs4H5SkF67rPC4VpTT2xpwaH1gDBVZIlf04FjmCwYwLlUoZsRGsZIjGDEhz7YDAOg489bE5zN4PtvCumPg7HXx\\u002f5gQtggeCJ35yckAAIPV+uaxI\\u002fCYJztBvH7ssPP24pQBmnczNa43C48MNc0mj6vQKVxp\\u002fy10nsz1aPbSbPQ5TzZIE1NiuUz4Ssy2gtIlb33zyh8H9hdXNyjVBouVIHv8nQpBEARBEAS5mujtWgDALPGKO5Kf6VF1JwBgcviCB1L+3aPW7V7lCCY\\u002fkfGuM+nTU4mczEfT3746su0uk8MX\\u002fiPjPT\\u002fdsYMUw0q6JupmODp88Gmiv6W9Oj5sNjzgVwQj6sG0V7IFE513o1lJnuPB4lIFD6e90Yu3JQNn3RD\\u002f8KqYu1zfH1O5OZ6bDD9D5NUoUB15q+yR\\u002fbIt3bPtdJwhoIVLmbFSZly3\\u002f2LD6FIBLcz\\u002fQqZNpprTHfvgaBAG\\u002fQM7uJ8UpHfUZo\\u002fFDPi9bQHdqOuq3QQAhLPgNQOQEauXFe4Dj0nh3Jz4XIFq\\u002f1H5FoWP635UjDZGNHue5AYezWNOR8itzhr1yZkzzTrtT5eKVGbzU9OmxwuE8EYANGjUz+7fV9DaCgAYFxk1LX6A8uB3jMv7raTYcSWZWyrva732oDDYPUpswhh9\\u002fSLhR5jnEql6zx\\u002fdCwwqdfm4rJ9PFkJxlcH088lC9zgF93kV4fbPN8Gh\\u002flH0+qNwCEEQBEEQBBkQRntn3iqTN+6pzA8Py\\u002f84qdxrtOs8t\\u002fKAASyVl7Mo8oZ4dsgKekR08Z3Jz9YYSg63by3XXSRIAt7CE47hadzcGeJl6bweN70dFkR08S2JT8jMTccVOy5pThn87hF3GMCiWYlpvNwxwmkxwz+DRseZ6+Iemhg2d0\\u002fbr9X6y\\u002fCwJz5NND3imunipe551b68CBwq747kZy6ojh5u3+ZnDQMXJoWVJ5o1X7oGunaVxs091+E5AXkYGvRXY3fbL\\u002ftlv0HBaFbi5PCFqdzscEZkMBVyRruuxVxfrb98RrlfZ1dDo\\u002ftlmyeHLwjmeSCD\\u002foEd3E8K0gtMKtVg62qOrTabOTS4aDIgi8O+v77aPZIRFuF+FxnJMHK41diSgGw0ltfqL7dbGo0OnYOwMSisMHpkLCs9lTe2n5q2d3eyseH2rb\\u002fbrnRLz4iIyAiP2FZeBgDIkUrHSCNL5PLCtlZn1pvHYPy+bn2ySOT+DP3q07Nn3j7e1Zd849p1A9bQJlSm7X3W\\u002fUT\\u002fhoTpf89Y5jYeSh+U\\u002f\\u002fVzfdciATiGHV\\u002fwutt4b7Spdcve+c5k9VjeYGgqfvMxOIQgSDcnFHs3NX0FRz1NDZ+\\u002fNu5uOIogCIIMrCF7xG4wVnxU+RwUXBFzx\\u002fSIa9wjdtJWrr1QoS9qMdUpLW1mh5EADjrO4FPDxMyYZE7WKMGEcHovJ78HQ2dXl2sv1hhK2s1NHdZ2s8PoIO00nM6icMLoUgkzJomTlc4b09N6\\u002fOGLIB11xvJ6Q3mTsabD2q6xKS2E2U5YqTidgTMZOJNLE0QwoiWMaAkjNomT2fc5B0OTwtJaoj1fayhtNzfp7CorYcEBhUXhhDMiY1hJGbyxabwxFIwCPyxE6g3lVfriOkOp0iozOfQmhxEDGB1ncKl8EV0SxUxI5GSm88bQ8B6nzIajgX81Tin3bmn6P\\u002fcIBrBl0bdNF1\\u002fTi\\u002fw4AMDsMH5V+2q9waOPNgDg4bTX49hBLU3vy6B\\u002fYAf3k4IEae7Gr90XTX195sIbe97G\\u002fc3TRz69eMZ1l0GhFtz2EIfmbyYHMnIMmwp3Fwxg8ezMeHYmPDCwpsTFf3TNsid279RbrQCAcoWiXNHZB+2STHZJ1tUNKozF+nTpioHMtgMAHpgw0UYQ75884byc8sCf275ZtTpH2o\\u002fn5SHHwKkmR1cXtr5Xnfuhs3tMJqKG4o9fpJD39LJZL23pzZw4BEEQBEEQZIQgyM4KHhcqRhstmDj6Sp+BgcejCvPDZuf3sD3CVQzHKMmcUcmcUfDACBPBiJopXjZT3F9VUP4lcDISOBlwdKQa4FdDY+vY3vIdFFweczt0sbBHmBT29XF\\u002f+2\\u002fZ36G1Kyr1RX1MuA\\u002f6B3ZwPylIkCZHx7kn3D84f3JxUpqIGeyCKARJfnzhtHu2HQCwIjUTZdsRlxD0cB+xFiSn7Ljp1uUZmb6WJKVg2MrMrO033jwo1eWPTJr843Vr08LCAQAdJtO1v2x4eu+eM81NdiLA\\u002fNAhgkNlut\\u002ftsAQ7KawXoCfnev7oXls7KedvC6bAUQRBEARBEAS5ohcLpSIIggyk44qdNsLiHolmJU6LWOIe6QUxIyqalQgF1da+LqiGIMFYneZxSabVoFv9x4YzrU3uQa9shOPP6vKVv\\u002f\\u002f4ztmurhIAADaV9lj+VPcIMsINvwr3ISWWz39\\u002f8TX\\u002fmjnrZGNjYVub3GjQWSwsGi2czR4jjZwenxDJHaAWN15NiYvbccutu6sqn923V2uxbCq+vKn4Mp1CSQsPjxcIo7jccDabz2CwaTQGlUrDKTjW1xWV5yYlw6HeimKJFBat6269sfcLqQdUa\\u002fBY7T2KFbLpCA\\u002fMn5weJX7ljwPtWnhVGWTkIYsUrzfpd+IYPUN0TxL\\u002fBngcQRAEQRAEQZC+ObL1\\u002fKk9l576+HZ4AOmtS+qTUGRi2LzedZKBiBkxzaZa94jB3pUEQJD+kx8ZsyI1c1tVmStSq1Fdv+2X0RGSmbFJGWERUVwui0oDAJjtdqPd1qLXNWrVlxXt59qaTXa4dTAGwFuzF0Vz+VAc6YXLmpM\\u002f178NAEjijL475T\\u002fw8PCBEu4hIGZzVmRkrsgY5C437pq12mJ5e0l7e7G8vbhdrrV0XY62OhzF7e3F7R4p5lCpefRxONRb8eyIS+qupWCajcoavSyZG\\u002fquOJW61laTyj2SFNKfMm90yvSMhD\\u002fOlWwrKClqaCOG26oJSKjITadrtb86b19Svh3NWcSghOzSDoIgCIIgCIIgAIBTey4pWtVwFOktvV2jtHa1zHVK4oQm+0HHGVDEQdqhCIL0k39Pn1+rUV2Se7y9ixXtxYqepcsYFOqbsxYtSwnNhwK5aqCE+9VpxtdfwqHhJleU+FfLeffI3rbC+1IXukdCYk\\u002frRSgyVhTi9cEZVOq6ybnrJudqTeZLjbJqmbJNo1cbTVa7w9XhR2M0n6ludH\\u002fUhORYISfYDmLI0GewdU1PI0mH2d6GEu4IgiAIgiAIEkKEg7h4tCw2NRIeQHpLY+vqc+3Cp4XBoV7R2j1K3wAADAr6CowMECGD+cvydY8d2LGnrgoeC9rEqNj\\u002fTJ+fERYBDyAjHkq4I0PUhDB4pZRf6o+uiJkQwn4vAIAGo2Jjw3H3CI7hk8PT3SMhxGcxp6UnTEtPgAcAqFeor3n7G\\u002ffII4um5iUOQvd\\u002fpJ9waHGu2zjGYNPQzkUQBEEQBEEGAUmQuzYc\\u002f+u7Yy11cpPe7D60s\\u002fVj1+3Lp6p+eX9XWUGd1WyLSZbMXzf52nvm4JTOBcz++OLg5y9s\\u002fvnSGwd+O7Pj+2PtTR3hkYKxMzLveHYFP8yjsar\\u002f5wEAbPv68KfP\\u002fbq5\\u002fL8tdfJvXt1afqGOJMjYVOmTH94Wl9aZOi+\\u002fULf3l1OXTlW1NykJgoxJlsxbO2n1vXMxvLOxyV\\u002ffH937y6na0mar2aaS65ZEPeR6\\u002fj8bP6RQu37cwS1nt39zuLakGQCQmhO\\u002f5sH5kxbmuEZBcL\\u002fPyGF2GOEQABQsBKkkEpAys0fNGQAgjC6BIgjSfzg0+v8tWnW0qf7DgpNnW5uCb0fAodHmJ6SuH5U7Karra37\\u002feav0Xo1N8WL2BjoemhUHkQEQgqMkgvSHKJYoR5jg3lXG7LC9Vvzbu3m30\\u002fDQvG\\u002fNDturlzfZCI85a1Mi0sMZPPfIwJAKBrPdPzIAxKxJSfzrm\\u002fQ7qThndNijNBz1d0MQBEEQBEEGwVf\\u002f+f23z\\u002fbPWT1h3SMLtSrDpo\\u002f2yhqVd7+4esLcrlUED245+\\u002fbD38UkSRbeMIXBohWdqPzy5S0lZ6r\\u002f9dU97gt\\u002fvX7fVxUX66cvy5u5Iu\\u002fSqapdPx2vL295d\\u002fs\\u002fevo8AIBTey599PTP42ZmrbxrtkapP3egJCJK6Brd\\u002fMm+84dKx8\\u002fKnLI4125znNxV+OXLW0x6883\\u002fWOrcIC41cvFN0xwOx0dP\\u002fxKTLFnz4ALXY3FK1w\\u002f66j+\\u002fb\\u002f5kX2yKdPFN00iSPHeg5KXbPrv7xdXX3T\\u002fPtY2T\\u002f99n5KDhNDgEgNamYlLYcLSHqvXFKqscCkYxvVSnIUi\\u002fmhGbMCM2QWbQ76+vLla2V3QoWgw6g9Wqt1kJkqTiOIdGFzKYYjYnni\\u002fMDIsYI4kaJ4mi4l2X8fqVwtKssfXjooZIPwlN4nIkszkctWqV2my2OTp7g\\u002fgxLT4eDiG+rY2f6p5wBwCc66h68sJ3b4y9lUnx8le\\u002fR\\u002fR28z8Kvi3yfH4AwM2Js6DIwGDSqDwWQ2fyWPwducrkRjybG\\u002fEsHEUQBEEQBEGQgWK12LZ\\u002fcyQ1J861rGhiZvQ\\u002fVr7b0aaJT49yRrQd+g+f+jlncuprGx9x1Ya\\u002f9dC3B7ecPbX70pTFuc4IAKD0XO37O59KGtU5d\\u002fP59R+fO1hSeq42Kz+pR88DAPjgyQ3\\u002f\\u002fvHBMdM6ZxuTJOmekX\\u002fo9XUsDoPBojvvrn9syZ1TXty94YQr4Z47NS13aprVYvvo6V\\u002fCpILFN011Pdal5GzN5k\\u002f25UxJe2XDQ3QmDQBgfsb63A0ffv3KH+NnZSVmRbtv7P\\u002f3GTkENC+9Mir0hRJmnybs2knbjtYfoSCO4ak8j9kGCDJgpBzu+lFj4OgQUKUvhEPIcIAS7r13SSb74MzJo\\u002fX1VocDHvOh5u8hW1PUv+tGddUmDF\\u002fzpDlfcfbVGzwuep9WVt5x6sO\\u002fZyybHNH7xi+HZJc\\u002fqtjRbIK70U2JyAh5A\\u002ffgSflclHBHEARBEARBEKT\\u002fdLRprBZbSk5XG4TkUTEAgNb6rq9dh7eeNxksq+6Z496JZfa1+Qe3nD2z77J7onzmyvGubDsAYNLCnHMHS5prZM6Ee\\u002fDPAwCYvnScK7sNAICy28IIj1nIbB4zaVRM4bEKkiBdXWUC2vPLSQDATU9c48y2AwCYbPr6x6\\u002f5140f7fzp+AOvrHXf2P\\u002fvM3IIaGF8mkhr82i2flT+54SwOQy8l\\u002f3W7aTt14ZPmozVUDyLn8+moJnfCOKhSocS7sMSSrj30tay0if37nateDnUvL1wMRwahnAMf3rUtQ+d\\u002fYIEHq206gztjxV8PSE8dUlU3kzJaA4VXtncF43NeLi9+M\\u002fmc1DhvBOLQn8qaxUcHUASAbdKpoSjCIIgCIIgCIIgISIU86k0SkttV3q9pU4OAAiTCFyRysIGAMC\\u002f7\\u002fg\\u002fV8RFrdC53011S9wDAHhCDgBArzE57wb\\u002fPACAtDH+poNbzbZdG06c3V\\u002fcXCPTq01mo8VmtQMACIKkBJ1wr77UCABIy\\u002fX4QWm5cQCAiovwN0T\\u002fv8+IMlow8aRit3tEZZX\\u002fWPfuzYmP9yLn3myq3dT4SYupDopjAJsjWQUFEWSEsxLmGkMxHEWGA5Rw741Greaf+\\u002fa6su1UHJdyuAwqxXMrJATGiZLXJ874qe4IPADAWWXVWWUVDafmCOOTuZHJHGkcJ5xDYbKodBaFAQBpsFtMDqvebm4wyGv0smp9W4mm0UF6v0aCAez57OsjQ7oia09J+ehiPoIgCIIgCIIg\\u002fYjJpi++adqf3x75\\u002fIXNkxbk6NSGDe\\u002fupDNpy26f6drGmTFffd88QTj8DSU6Sex+Fyo8hwT\\u002fPAAAfhgHirgYtKYnVrxTX946aWHOqnvmiqOFbC7zq1f+cCb0g2fUm6k0CpvnseogT8TBcMyggZcG9fP7jDTTI645rdxHkB4z+8t1F98rf3Jh5Lpc4ZRg1lA12HVV+kunlfuq9JfgMQAAAJPC58ez0+AoggxzcktTQcfBOkOJwtpqcRgxgDEo7DC6NJKZmMLNyeCP774OarOpukpX2GqubTPVKSwtrgrUly+v99wQAABmiq9dFHULHAXgYPumfW0\\u002fAwD+OeprHlUIAJCZ688o99QYLmtsSpIkeFRRJCshjTduvGgejnVNQqrWF31d8xIAIIIR81jGh6445H\\u002flDysszQCAO5NfSuF6TFdyZyMsheqj1fqiZlO10a6zEEYqRufRRGJGTBw7I4M\\u002fPoqZCD8mEJND\\u002f1X1C63mOgBANCvpzuSXWUNyZkzgw2KNvmi\\u002f7OcMfn4GL1+K1q8AAADwfeFFi8MOAAhns5+fOXtxahqdgrLt\\u002feXBtMX1BvkxeSk8AAAAwEbYCzpqCjpq4IEe+lv6NXOk2XB0YElQwh1BQoEEZKOxps5Q0W5paTe3qGxKi8NkIcx20kbHmUycyaSww+kSCTNayoxJ4WRFMCLhp\\u002fAGx4bucd5O2GSWZpm5WWZp7rDItXaV1qY2OPQ2wmojrATpoOBUKkajYlQmhc2l8jkUnoAmEjMiIxhRUmaMOLhXYNBpbKqL6pOVuuI2c6PernOQdg6Vy6Hy41jJKdzMUfw8DtXfd35fmky1JZoLlfriDmu73q51kA4GzuTThGJGVCInPYs\\u002fJoo5JArcRshehujt2mZTXYupQWZu1tg6NDaV3qG1EVY7YXOQdjrOoFOYXApfzIgUM6OSOZkp3MxelNoNTSqrotZQLre0KiwyhVWmt2ssDrOVsNhIKwWj0nEGHWdwqfxwuiSMLpEyoxM56RKGR\\u002ffhYWEEHrERZCi479\\u002fXKVrVW7889Oe3R3giTsa4xKc+vt29g7kzJT1t6dhRE5K7HuaN\\u002f0YrwT+Pf1u\\u002fPFhf3rr6vnn3vLTaFaTSevxJ5\\u002fBYdpvDoDVx+F1\\u002fLHQqA0mQHEFflwC9iokZ0TPESw+3b4PiSqvs54YPtjR9kcbLiWTGRzCiWBQuHWcAQNoIq420mhxGlVWmtLTLLE1tpnpo5rq7eHb6ipg74CiCDGcESexq+\\u002f6EfDv0zrfbNQa7ptFYcbZjDx1nPpn5GZvKd9\\u002fglGJHgeqge6QvdLYOHlV4QLbxgOxX999EaW1VWltbTDUTwrqWmA6tcx37drf9aLRr3YNW0qy0tCotrWXacycUfz6d9SWlJ6dtJof+65oXndn2WHbq7UkvDM1sOwgm4X5Zc6LBWNZgLNvb9qOAJs7gj0\\u002fn5Sdzcmh453IlI9DJxgYAAAbAVytW5UqDOu9Heg3H8NfG3Pxc4U9H5SXwWCjgGPZI+tJ1CdPhgQEnEaAaisFnI\\u002fRtxkNy40mtrdpkb7MTRgAICsZmUEQcWjyfni5mTQhjjqNgARoZkaRjW+14OHoFkypZFL8HjgbHZG9r0G1Tmgv0tlqrQ+sgO2fs+iJmTZoa9bl7pEL1RanqYwAAj54yN\\u002fY3Z9Dq0LQY9rQaDhjsTWa7AgCSRuHzaEnhzPEJvFVMqsT9GYYmEpBl2otnO45U6C8b7PAkZSezw2h2GIGto83cVKwtcAYjGNIs\\u002frhJYbNjWIkeW3ui4wF2+gAzOQwVuss1htI6Q1Wzqc5B2uEt3NgJmx3YAAB6u1ZhaYNGOVRePDs1jTs6R5AfZDJrgOnt2r9afznbcQT6Z2psKo1N1WKqP91xkI4zJoTNnC+9VkgLc9\\u002fGj2p9ya62zVV6+I+L0aE3OvRt5qZLmrPbW36KZ6fMlawYI5wEbTYARtRediEBWWsoL1KfqdBdbjX7K120EGYLYdbZ1K3mBqAB+8FWHKOk87KnhM\\u002fL5o8fjilXgnSU6YoK1aeq9CUdVo81bNwRpNVGWA1Ap7IqGo1dNQccKi+VO2qMcPJoft5QO2RBRtoRG0GGmksnq87su\\u002fz0J3fMWuX9fDV9TPz+TaeLTlT0MVEequepr2gDAExfNs4VcdiJpqr2ri2uwHEcAEAS3hO76eMSKosaKgsbxs7IcAUrChtAtz4zCGRx5PoWU12lrggeAMBCmC5rzlzWnIEHgpbMHXV74j+pWGdjfQS5OuyTbTgu77xMJaJLo1lJbArPQdpV1vZWc63ZYQQAxLHToWw7ACBXOCOK1bm4oI2w7Gn7yXl7UdQt3T8mMaxUKALR2VT7tRsPyDYCAOg4U8KMpeFMrU2ptrY7SEcGz\\u002ftfgb7b3vzFKeVO110GhR1Bj2JQWDqbSmNTWgkzACBPNKdH2Xazw\\u002fB1zUstploAQDw74\\u002fak5xmUoXutNEDCnQRkqfa0667GJj+j3HVGuWtC2KIVMfe7bTiyNGq1AIDx0TEo2z4waDjljbE3f1q5+8e6w\\u002fBY33CprJdy1k0TZ8IDgwG1lBlcJCCq1N9UqL+2EwZoiCC1NkKrt9XLjEcr1V9RcU40Z8GYiGfwQGn30CJIy2Xl\\u002f+p0m0jP6Zy9ZrA1kKQDwyhN+h1FitdthEfKw2E3m+3tctPpCvUXacI7M0T3YaBrotmQYidsRxW7jyp2qawKeCwICovsqHzXUfmuJE7GLPE1vvKqLMqQuCSmtLZfVJ0s1hbUGysJHz2yespg15VqL5RqL2xr+TGKGT8lfO7E8Fn9Vyb8zKU7neeXkHmSFcuivUyTLNMWbmj4RGfXwAOerITluGJvgerE2ti7xommwsOe7IRte+uGo\\u002fJdfiqtXBqM1d\\u002fW\\u002fS+dl31T\\u002fN\\u002f4NCE83A+ugr3cO2aH8ZhizynlAaXVSw4lGATpKNMWlmlDubTUo2n\\u002fSeD0+wx3hUV2VLGzQHVC71kE1CMGu65QfbpQfZqOM8YKJ8+RLI9kxsIbDbYRdcRGkCHrwtFywkFEJ4t9rTg6e\\u002fWE797c\\u002fvvnB2avyo9MiHDFVXIdh8+kM+CMjy+hep6ISCEAQN6iygKdSahNH+\\u002fRqeGTdgAAlUYRSfjNNTKb1U6jw+mOJTdP2\\u002fHDsZ\\u002fe3ZGVn8Rg0QEAFpP15\\u002f\\u002ftxDBs4Y1ToI0RdxSMcnvi0xsbPypSn4TH+oCCUWdLViyQXj8cL5MjiB8WwnRC8ScAAMfw6+MeyxFOcx8lSKLJVHlJfTyVN9Y97pTGG5t2JW52GFwJ98nhS7r3nwnovOpAieYUnxa2JOr20YIprgS3xWEs1Z6VMD3W4QiV08pdrmx7LDttYeRNSZxsV+MaEpANhrIS7ekeFddfybbXAAASOaNuS\\u002fpXL16NgQT\\u002fBYIoLM16uxqOAjBWNBsOjSRmux0AkCwazH7fIw2O4Q+lL5kUkf5myZYmY2hWFp0pGf1k1soIBnw5cbBIBCjhPmhIQJxpe7TNeAQe8MZOGDTW8gDZdgwTsyZbCbXVobY61A7SDG\\u002fQQwRpOd56b4e5K4uEAZxOEQCAWR1qEsApORrO59BiObQ4Mct7MgIAQJA2g72p1bCvpMNnazYAAEHaylWfm+3yseIX4LEhoEB1\\u002fM\\u002fWn3uXuIHUGsprDeXJnMzVsbd3r51kUwfzE2qw686pjhaojjcYq+GxkGo1N2xp\\u002fvav1o2zJEvmSVYOZJVok6kWDgFwQXXix4aPgk86mxyG7+s\\u002fUFhlC6TXwmNXWAjT59Vv1BrK4QG\\u002fKnSX36n45\\u002f0pz0X1z1kpGBl72RcbYT3Qvu2QfIfXizFXN4Wlba\\u002fs93OqY1Bv3L6wEpYzHYfPdhwZxc9bFn3j0Em7j5AjNoIMfYtunLLrx+OPLHrTeZdGp0rjwpfcMu3ae+c6W8TwRZwnP7zttXu\\u002fenD+67NWjhdHC9UKfUNl26WTlV+ffEkaF+7xdL6F6nnmrpnwx5cHP3l2Y1O1jMGkFR6vKD5TnT0p9fLpKnhTAGatHP\\u002fHFwefWv1e\\u002fpxRDrtDo9Q\\u002f\\u002fNaNzqGU7Ljb\\u002f7nim9e2\\u002fm3BG\\u002flzRwEAzu4vbq5pv+XJpdDqr0h3NJx+c8LjRYKTf7X+oPI9DStIOEYZK5w2T7pGzIiCxxBk+JObm22EFQAQxUyCsu0AABzD49kZ8eyuqTb9p1hzkk8LezD1bR7NI4fJoLDHima5R0LF4jDuafvReTuDP\\u002f6mhKehZR4wgCVwshI4We7B7jC3zvJmh\\u002fGb2pebTdUAgGRu9i2Jzw7xbDsImHCvN5TCIQDCGVHx7CFRETxYwlnsNr3O5gj2+z8SKvlhKRumPv5n87kf6g61mlTwcNDyw1LvSpk3VtRZHzFEoAr3QVSp+so92x7GHCtmTWJTY6g4myCtFodKZ61WWS5rrRXODRJ5a1wbe4UBfGrUZ667BGnRWqsPN3up3g3SJeVbrmw7gxI2Kuzv0Zz5VJwDALAThhbD\\u002fpKO9y2OzmtRkeyZkyI\\u002fcD3Wjwr1l026v5y3BYysSPYMNjWGgrMsdqXCfK7NcNCVyq\\u002fXbZGyp0Vx5nU9eLAZ7LqfGz4r1p6HB\\u002fqmxlD2Tvkz86WrlkRdj4Gumi\\u002fOIPWGq9aXnlDuK1KfsZM2eKzfWAjTnrYtp5UH18TenS3or2mGkCZTHRQp1p7vUbbdZUfrRgbOnCleAg8AYCFMn1a9Wm\\u002f08v08IK1N\\u002fWn1q4+m\\u002fSeMDq\\u002fz1kcjZy97VaG7vKnpC4VFBg9c7eykbW\\u002fb7\\u002fvbtzpCl2p3RwKyWHu+THdxlviahZHXMQb1a8kIOWIjyLDQWqd49Z4vBeHcJbdM4\\u002fBYAACTwVx4vOKLl7ZQKJSVd892bjZ5Ue4Hu57+9aM9Z\\u002fZd1nboeUJ2VKL4tqeXi8Q9K1cKyfOkZMe9\\u002fP3937\\u002f1568f7qHSKKMnpryz9YnKogavCfc7nl1JZ9KObCv45f3dTDbdvTc9AOD6hxfGpUk3f7Jv54\\u002fHMQwkj469\\u002fZkV7s1qEP9yhVNGCfLPdxy+qD5eayjp6XkaBaMmcNJH8yeOFU1zLuSIIFclVyG51qa0k7burWAG0tLou6Bse78qUB101tAwcNaauEeCWVTZK9dkXIvD+G3tv5uMVQCAVO6YmxOfGRZNzgP8s1tMXmqsMngT4NAIkx4e3qbXVXaEps4a6REaTrk2btKquInnlNV72wpPKyvazQH6DDhhAEvjRU0XZy2OHhfH7prPOHSEcdmf39lVkpkmHYq\\u002f5FWJBESN9hfnbQzgEyPfjWTP9tjiCotD0WI40GrYH8v1ksjzA8cYDEqwJTzdGWyN9drfnbepOHt69DdcWtcS1lScE89bEc7MO9x8g43QAwDajEe01io+PUA3NwBAo247AICGc8eJ\\u002fxPFmeM+lCy4UWMpPdn2sMXRWYpYof566CTc6wwV39a9p7F1wAOhQAJyr+z3OkPFLYmP8KgCZ5DbrblevyJIokhz+kD7dvcGzQNMY1N9Vfv2LPE1K6Jvdl+5vp8Y7DqVVSGidx76lNb2n+o\\u002f6em3OJetLT8mcTLi2HDH2F8aPu9dtt1JZ1N\\u002fX\\u002ff+I2kvh2Ti8wjcyxASkHvatuxu2xxMb5+rTIOxakPDpzJzMzwQag7ScaB9e5Hm7B2Jj0Wzuv52DKSr\\u002foiNIMPL+09uULVrvz71MovTNcPJYSduGvvMsT8vuBLuAIDErOinPr7ddRey6p45q+7xOHsEAMxaNb57X3j\\u002fzwMAWHHnrBV3Bii0zJ87On\\u002fuaPdIYlb0gnWT3SNOdCbtjmdX3vHsSnjgiimLx0xZPAaOugnm9xnJqBhtUvj8SeHz9XZNjb6kzdzQZm5QWmRmwuhc+JogHRSMSsWpTJzNpvL4tDARXSxhxESzEmNZybQhMLUOQfqblBnPofINdq3Orv6+9tUVMfdGDNLi9iwKd7TAy6Gy\\u002f1TpOxd7yBXOYFN4noM9wKCwAAAWwvRt7X8ajRUAgHRe3k2JTw\\u002fu1YvgBUi4d1jb4BAA6bw8ODTCLEvPOFJfV9wuK5HLR4lDXOaGBAMD2ITw1AnhqQCAZlNHpbal1tDeblYrLXqTw2Il7BjA6BQqh8oUM\\u002fiRTFEKLzKDHy2kDemGnjiGTc9IhKNI\\u002fzPb21214WLWZF\\u002fZdgAAgxKRxL8+iX89PNDPmg27XZXmSfx17tl2Fw4tNlmwvlz1f867LYY9wSTcAQAYRpkS9amIkQMPACBgZOWJXz7Z9pDzrtpSbHYomJTBvxRUpiv8pvZdK2GBB0KqUl\\u002f8QeULD6Y870wBMylsCkb1v2plCH1e83qF7hIcHQyH5Ts0NtUtCX8LSYrZvyZTrfPVJgH5U\\u002f1HJoeX3qxBIkjHhoZP\\u002fpHxhntJxWH5jovqU25b9Ua9seqIfOdsyTJ4oOdG5l52IUjHD\\u002fUfXQxpK9jh4kzHoV8bvxyw4wkAQGFpe6\\u002fy+TWxd00MG+gU0kg4Ygcvnp3+1phNcBRBBlbpuZrcKWnu2XYAAIWKU2kUg87kHkQQ\\u002f7hUQa5wSi5A7e8RBIZjlMVRt\\u002f3W+CEAoFpf9F75w+m8cflhCzL443td8d070axk91mAA6DZWOm8kcz1uEraUwyc5SAdG+reajCWAwCy+BNuTHhygF+9vgjwi3ZPuGMAixuQNkND2bVZo366VFjY1vborr9+XL1WwhnSadyrXgwrLIYVNhsOI0iwHORQ\\u002f2rRYb7oui1lz+wa8CRlz3Al3FWWYs9Bn5L5N3jNtjtJ2NO4tAS9rd55V2W+BBXCD7xLmrPf1b0\\u002fMGkUhUX2UdXLD6W+4GwhwqPy1f1TodldrmDCEEnFAgAuqk9ScepN8Z2XXvpPk6k2RzABAHC+41itobODU6+1mZvOdByeEt45LUNlVfzV2jmXpY\\u002f2yv6YHD6XSWHDAz00MveyE0ESP9R\\u002f2PfrH8MOCchtzT8eknf28hpINsL6c8OnOrtmnmQFPNZvRsgRG0GGF56Q3Vgls1ps7muWHtlWoGzTTJzv85wQQRAE6ZE80Rw6ztje\\u002fKXeriYBWa4rKNcVsCjcsaJZk8IXiRmx8AP6B+fK\\u002fL8BY3DonDdEdKnnSM8wcNYfTZ9W6Ts7686SXDeMsu0gYMLd3K24TEiXDP3O9P2NgmGfLVtx3\\u002fZtRbK2RT98d2de3pLU9GSRCMcG9KpRT5EAlMnlZQq5ymzWmM1ai4UgQzyD++U5c+EQggx5LGo0jtEJ0goAUJjPdJgLw5j+JpkOPLO9a1UiDs3nH2Y2tWuSmsXtIf4lBirYFzFzXQl3V3uZwdJgrPqh\\u002fsOByd04dVjln1a\\u002f8lj6a2wKh0cTDlj6ZmLY7J1tmwz2zpOVQXeu42giO21axEJ4IKSajLUAADth+7N1AzzWK\\u002ftlWyeFzXF2Svm9+TvnykV9Z3Toz3Qc9tojvkdG5l522tryfS+y7Vwqn0cVsqlcNoWDY7jBrjM69Dq7VmdTw5sOSSQgf238v1PKg\\u002fDAAPqzZYOVMC+JDHDkD4mRc8RGkOHl2nvnfvnv3x9b+vaM5eO5Apa2Q198pvrCkfKIKOH6x\\u002fv6pw1BEARxyRZMTeflnVHuOaXcobK2AwBMDv1JxV+nFDtGCSYvibpNRJfAjwk16sAmqa2EmbiyOpGrCXvvXFQf1rqdy22of6v70q9DWYDX3UqYochgdR0aFPO\\u002f\\u002fwYOAQAAoGA4l06n4jgAQGMx\\u002f+\\u002fkif+dPMGgUMPYLBbV30u679Y74NCAKGht\\u002ffL8uVNNjWozvENDCyXckeGIgjGiOHOb9bsAAARpP956T4pgfbLg5qHQO8XJThpdt3Hgs2EZ5vanlABBLcHHocV5bVDjjkEJc922EYOZGVRZFV\\u002fWvN2XnCkGMApOdRD2HjWMVlhk39e9d2\\u002fyM9wBrA6g4fQZEYt2tW2GB3qod\\u002f9kr\\u002f5o\\u002fiGTPza8P08Km0y1AICzqiMamwoeAwAAgGM4AJjrHC4gpbW9WHs+RzChWl96SXMWHnbT0+4TJ5X7+55wH5l7GQBwQX3yiHwXHPUBxyhjBBMz+LkpnKwIRiQ8DAAAQGVVVBtKK3SXL6hOhGThWQbODGdII+jSCIZUFLo1cn9r+qaP2XYcw3GM0sd9vadtC4fC6\\u002fsb2L8RdcRGkOHlugfmCyN4O3449uuHu80mK5vLjE4S3\\u002fjYklX3zOYJ0dRtBEGQUKLjzOniFdPEy6t0hQWqAyWa03bSRgKyWHOySl94S8I\\u002fk7jZ8GOGA5uPboE0nIEBzHny1seOglpbBwawSeFL6gzFbeZ6ra3jx\\u002fo37kl55Srp4d690Q+TMoL+BteovH\\u002fh98risLfqBjMV5VWLTvfsvr1H6uvgAQRB3IwOe1RhOm1xqAAABGmtVH9brfkxkj0nnrdCwpqKDWBTY6\\u002fouNAAGp23LQ4lnSL0GL7CYu8qP3fPkvvBp6fBoW7cU\\u002fxk0InOkCNI4vv6D3T2oBZJdhHRI7J4Y1O5o6TMmHCGhI4znX\\u002f+bYRVY1MprbJWU2ONobRKX+pcSN2Xct2lHxs+0vfwp\\u002ffR9IhFB9q3B3+aIqJHxLNTpIwYKTNGRBfzaUIeVUDD6c5\\u002fstlhNDoMBruuyVRbZ6isM1TILa3wU\\u002fhlJ21bm3+4M+kJeCB0tDa11qY+LN\\u002fhHsQxfJxwap5oWjw7xbkQosqquKQ5u799qzaIuuYL6pM5ggn72v+ABwDgUHmTw+aMFuRHs+IZONNKWJRW2bmOYyeU+\\u002fy\\u002fHwAAbeamNnNTJNPnjJMgjcC9bLDrNjd+CUe9oWCUyeFz50pWODuE+CGiR+TTZ+SLZiyPuvGQfMdRxa7g87xcKt+VW4+gR4YzpBEMqWvtzRDa1bb5uGIPHPVLwohO42XHsZMiGbFCejiXKqBgFACA8yCmtilVVkWTsbbOWFGhuxz8uwgAsLXlBzEjMos\\u002fDh4IkRF4xEaQ4WXe2knz1k6CowiCIEj\\u002fwACWxhubxhtrdOjOKHcflf9hdhgtDuOG+refyPy0750qB57JoYdDAAAAMIAxKRznqMYmjwWp8BZB41GF6xOfimdnqm2KTyuf0tvVTcbKLY0fXR\\u002f\\u002fGLzpkBQg4U6nsOx2j0IhGoZWlB42SuTyO\\u002f\\u002fY0m6A+wIhCAJhUSNnRH93rv2fakuJM0KQ9hbD3hbDXjpFGMu9Jp63SkBP93zQwBEyRqksnY2eFeazPHqK53gnhfmc67aQMcptxCcmJUAaa+g42L69ridNvVO5o+dKlmfyx3S\\u002fcowBjI4zxIxIMSMykzdmDlhmJ2yXteeOynfXGMqgjV0uqE7AoX7GofImhc0+qtgND7gR0SMyeWMyeLlJnAw+TQgPX4EBjEXhsCiccLoknp0yNXw+AKDBWH1YvqNQfcoR9HWUS5qzjcaaOHYyPBA6b5c\\u002fpbdrXXclzOjbEx+NYsa7bQJE9IiZ4iX5YTO\\u002fqX2nSt\\u002f5mfWlRFNQb6gs03b2\\u002fnOZHD5nZfStTErXPEc6zohixi+PXj85fO7n1a8pre1um3tRrC3oe8J9BO7lP1s3GLt1LOyOS+XfnvhYCjcLHvCLRxMuj16fJ5r6Ve1\\u002fVdYALbDyRNPWxt7t\\u002fh7oPyXaC3vafoOjPrAp3MnhcyaHzxUzouAxAMCVg5iEES1hRGfwcgEANsJ6SXP2sHxHg7Ea3tobZ0L8yYy3Al7M6J0ReMRGEARBEAQJiE3hzZasyRFO+7TyaZNDb3ToSrSn80SDvEyaO1efdIfvaaNqm6J7QxSXaFZytb4IAFBnKB0tmAIPBy2CERPPzgQACGkRNyU+\\u002fVX1C3bSVqg+KmHGz5ZcB2899ARIuLMoHKPb917g+yLGVemHa9fAoeGjQaO5cfOvOksPyp0QZCTj0OJnxvzYqNtWqf7G1bIcAGB1qGs0G2o0G8KZeWnCO6TsGW4PGiCx3CW12o3O21Xq72O5y2g413MTYCcMVervXXdjOEF1Yabiw+NauszcvLNtExz1gUvlr469Y5ywB3\\u002faqThtrHDKWOGUMl3h5savAmZaB8xsybLjyr0ESUBxKTNmjHDSWOFkKBPdI\\u002fHslFsSHl4adcMP9R8Gnxo7qti1Pv5BOBo67tn2KGbc39JeYvuYWsemcO5Ofuq9in+1mZvgMTcWwvxB1UtQcEnk9QsjV0NBFzEj8v6UZ98sf9JO+DzLBABU6YpDsvjkiNrLSmv7aeUhONpNOF3yUOoLInov+3rFsBKfSH\\u002f90+pXm0118JibC6oTU8LnpnJHwwOhprS2\\u002f1j\\u002fUTB9UagYbbZk6Xzpyp62vKTh9DzRtDzRtIvqU1ubvw+md7nZYfq54dMHU5\\u002fvnuPuoxF7xEYQBEEQBAlGOD1qjHDGKeVOAIDKKoOH3bgvE2pxmAZgTU3WlS9fWluHnbR57d9SoT0Ph9ykcHOdCfeLqsMLIteH5HeOZ2dcG\\u002fvgpsb3AQD72jZImLGj+EN9nlaAhLuYEav0nIxsvLLa7EgwLb73X3EH3VN7dg9Ytj2ax5saHz89LkAnaAQZ4jCAx\\u002fNWxfFWyIxH67W\\u002fy0xH3TuoKM0FyrYCMWvSWPFLbKr3qsN+EsYcK2FPazceBwAY7S3HW+7MjXjOfWVXleVSoeI1o73ZeTeGs1DICCqFhIFB7pYTpO0tG4Lsrx3NSrg76cle5+kyeWOezHzz18YvC1TH4bHBEEYXjxFOdpVqsimcPNG0iWGzQ1h9HEYXP5z64p+tPx9s\\u002fxMe8+aC6uR1sXf0NBvYC3SccUfSE76y7U4MnHlj\\u002fP3vVTzvP5UJ9XwfL5ruJ9vuFMGInC9Z6b+7er2xigRk35OVI2ovH2r\\u002f0\\u002f\\u002fOAgBQMOptiY\\u002f2+lPsxKHybk987J2Kf5odJnjsChKQvzZ+8VTm216\\u002fSIQKCcgN9Z+YgijqlzJjbk14JJrVp7OpscLJadzRP9R\\u002fWK4rgse6qdKXHJPvniFeDA\\u002f0zYg9YiMIgiAIggTJQnT2x2NR4Fo6dzScQceZznLyNnP9ACwZGs6IpmI0O2lzkPZSzZkc4TRoAxthPSrfCgXd5YfNP9j+q42wGh2635s+vT7+0b5\\u002fYwIAjBXNarc0HW7\\u002fjQTkpob37019LYqZCG80lARIuEuZCWVaj0XG9PYetDVHBsuh2tozzf4q\\u002fgAAOIaxaTQMwwAAvUvNc+j0f06fMS0+PlHY7595BBkwGMAj2bMi2bMsDmWTfkeDbpvWWukalZtOH2m+aXr0V1xaktuD+t24iBePtNxqsrcBADTWiqMttzEoYWxqDACY0d5icXR1ThAwMseKX+x65PBXYygr9nsJ3SWaFf9Q6gv+U7QBMXDWzQl\\u002f41L5R+Q74bHBMFey\\u002fILqRCwraYZ4cZ5oan9kBnGMsiL6ZhthPRZEg2k7aSvTFo0R9ntBwVzJcrGPFTLdxbNTR\\u002fHzgnyHAAA4VN51sXfAUW9miBfvk231swKnyWFQWNp8Nf3okRGyl+2E7WzHUTjazfLo9SG52BDBkK6NvfuH+g\\u002fhATdyS9sJxb5+XT70tPKQn84nLincrLuS\\u002fuEqKeoLDpV3T\\u002fLTP9R\\u002fUKg+DY91s6ttU37YjJD8XKcRfsRGEARBEAQBABxq39xhleUIpiVxR0Pn9iQgL6gOFqqPOe+m8ca6j3aXwMms1F0EAOxp+yGalcyh8uEtQoqK0VJ5Y52p4D9bvgpnREWzus7MVdb235o+6rC2dT2gGw6VP1uyZm\\u002fbBgBAkfqowa5ZELk+ju3RoVdnV9fqL7WZGxZG3uQe929B5Hq5palEc9pKmH+sfe3BtLc5\\u002fbDwUqgESLincHMPt3uUdyktrXq7mksVugeRoeb7ootwCAAAgIjJXJedMzU+frREKmAwcKzzKlP6B+\\u002fZic7J7Ouyc16ZO89520oQKpOpw2Qqbpcdb2g4VFert3atQmawWmvV6vW5XWW2g8LssFXpW6t0be1mtdyiNdotFsKOAcCg0NgUhpjBFzMFKVxpKi+KRaHDD+43r249GBvGn5GRlCwJau1KZAhiUMJTBLekCG5Rmi9UqL90FpgDACyOjnOyp2fF\\u002foIB3PMR\\u002fYhJlcyI\\u002frZA\\u002fi+FqbNRu8XRYXFATQOweN6KnPCnh0ujmCDtaP0VDnnDownvT362j7kbJwxg18bcprNrhkIX4FhW0pMZb0Wz+n3G1bUxt7WaG6v1pfBAN8XagpCnYiE0nD5LfA0c9WFaxIIgE3wAgLmS5UHmFtkUbip3VJkObv7uTmZuDknCfYTs5TJdkYXwWW\\u002fuxKMKpkcE1RErGONEU3fLfms3t8ADbg60b5sesRDvn8WxDXbd9paf4Gg38eyUe5P\\u002fScdDtk4SBaPckvCIxvZywD5CRodhf\\u002fu2ZVE3wgO9NcKP2AiCIAiCIAAAi8N0vmP\\u002f+Y79VIwmZcaHMSJZFA5Jknq7uslUpbN1ljJPDF8oZgRYFGpK+FJnwr3FVPtu+UNJnFE8mshGWE0Og9amnBy+ZHxYZwYvVOZI1lbqChykQ29Xf1L5ZAInS8yIcZAOpbWl0VhJkI4YVgqPFgbVZ7ubJblOZm4oUh8DAFTri6qrijhUQTg9koYzrIRZbW3X2dUAgChmYo8S7hjA1sb9\\u002ff+sz7WaatU2xY91b96V8nJ\\u002f1CqFRICEewJ7NJvCg9rI1OiLcoUz3SPIkKK1WI7Vd3Wgdrkpd8yzM2exqF52OoNKtV\\u002fJpJvsNgremUZk4TiLx4vm8bIlknXZORqL+f\\u002fOnfvmQoHZ3jlT+OuC8x1G4zuLl4RgfkgPtZs1u1ovnFSUX1LXO7q1vu0Ox7BMfuzkiPRFkWPjOf2yRJiLwWLdeKrQQZBv\\u002fXkkWsSfmZG0dlJOZnT\\u002f\\u002flCk\\u002f4Qzx02J\\u002fLhJv7NA\\u002fi9nkxmNtUJhOitmhTgb5R+LGpkmvEtjKbMRegAAnSKwOXQYRqXhfC4tIZw5Lp63kkOLgx82zLWYGqoDrYoJAMAAdnP8Qzzf60n2wo1x9zeb6vyn6gbGAORhAQA4Rrkh7v7Xyx7r3kwcUm\\u002fomvPRT3IEE5iUYK8bpfNyWBROMC07qDhtcvhcOOpbwIR7uyVkb4+RsJcva7oWdvZlWsQC91aVfYQBbGbEks1NX8EDbjQ2VYn2QrYgHx4IhX3tW42BVj\\u002fi0YT3JD8dwmy7EwWj3J746BtlT\\u002fhpquN0RL5zjngZh8qDB3oOHbGRq15ZQ3tBZRODRr1uZi48hiAIgiBX0PDOck87aWs2VTeb4GXtcQyfHH7NkqjboXh3Gfzxc6XXH5D9CgAwOwylnmnugOUsvRDLTl0T98hvjR\\u002fZSRsJyDpDSZ2h6wQvlp12U8LT5zv2+0m4YwC7Pv6xSGbiofbNzn44BrvGYNd0267HqUQ6zrwl8ZlPK5\\u002fS2dUNxrKtTZ9dF\\u002fcwvNHQEOArDY7h+WELjsi3uAdLtWdGeMLdQZKUnr8tBszppiZXubrLI5MmPzplKhR0YVKphisJd6PV5\\u002fR5AYP55LTpi1PT7tn2R7uhM7XxR1kpj8F4eU4PUhh9VKZt\\u002frp6\\u002f3FFKUEG6APrjiDJEk1jiabx6+r9Y0VJtyfPmRTuMaUlhArrWx1E5+\\u002fWotL+cqowJ04aMOFeUNfZgNspPVLMZQ5cST4SUCx3SYf5omvxUpXl0gAn3Gs0P19SvgkAYFIi8qVvhjPHw1tcjY4qdsEhb\\u002fLDZqbzcuBo39Bw+trYuz+u+jc8cPWKYEhzBZMuqk\\u002fCA57kllYLYeqPBt8uo\\u002fl5cMg3CkZJ4WZe1gQucs\\u002fkjWH7bZIIiWOnwCFPSqscDg15g7iXg+msMjk8xBU6+WEztjR\\u002fC\\u002fXxh1xQn+yPhLvBrjuh2AtHu1kf9wC3f6YGC2hhSyKv\\u002f735O3jAk42wnu44NFeyHB7oOXTERq56P+w9v\\u002fNMmYDDRAl3BEEQxI+50nWJnNHlunPNxmqVVWZ06O2kFQcUJoUTwYhO4oweK5oZwYiBH+bDPOkNqdyxZzp2NxjKtLYOEpBMCptPDYtkJUCtWkIlVzgjjp1+SrmzWn9JZZXZCAuHKpAw4nJFM8YJZ+EYRcwMUJiPAWyWZPXE8IUXVIeq9UVtpjrni0DDGHxamIQZl8TJzhH6zFL6IaBF3JT4zy+rn7eTtgLVQQkzboZ4FbzREBAg4Q4AmBax8pRyh\\u002fOKhFOx5kS7pVHCuNrqKIO3qfjyJ2dPz0lKXjsqO1sigYcH28W2ViiSFxX9yOQpUNAdg9I1k9po85lwd8qRSn9bd+OKDT+qzJ3vih8KL46NjLw2a5TnhqGnthn+V7Z9T+tFeKCHLqpqHz1fOz4s5alR18aze7lUlx\\u002flbV0NtZ1GxUqhSHe3fOoxC\\u002fuHB67PSwz2+IsMDAEjw3WbILvaKw0ArbXisvJt5+1JkR8IGf3+cRsKLIQpmKXw6DhjadQNcDQUUrmjRvPzirUF8MDVa65kecBULAnIVlNjIqdfzu2c0rij4ZBfSZyMYBLuo\\u002fk9u0wlZUbDIU9aG9TWaXgYlL1sdBgUFn\\u002fdHgEA4XSJINQrQTFwZgwrodFYAw+4KdcVhWT9W8gh+V9WIsAaObmCiZn8fmzNNy1iwYH2bZor05Z9OaHYO0eyrI+vADpiIyPBmbIGOIQgCIIg3iRzs5O52XC0txI4mQmcTDjq2xzJ2jmStXC0J0R0qZ8C\\u002fGzBlFdzPYqzvWJRuFMjlk2NWAYP+BbMM8ex01\\u002fO6SyFHLICNyBmU\\u002fmLIm91j5CAPCD72T0y0hyur2vSan8ovFjVoYTHhoAaFfz9\\u002f6GJE13t2r1iuvWZCZhwBwDE8PnvLr7G\\u002fRlfOHhAYTS6BULvXEf1jcff7Xu23eV8R\\u002fUtJ977sznwDPeeqvRMuDNp1BRJuHsEGVIIMkBCxEVt6ZpIxaIGXs4xhJoNe0lAAABwjCagd+X9r24l2osB01UAgAlhs0KepHOZK10Bh65qcezkYPo8qG39+OdPQBMF8zu4i2YlwCFvUrlZcMgvPk3kvydgwDzm0DQoe1lmbiJBgHlpCZw0OBQKiewAT2uw65SWdjjaNw7SflK5H456wgC2LDpkzdO9omDU6RGL4Gg3Smt7TRCd\\u002ff1DR2zkqlfZpFBq+\\u002ff7DoIgCIIgV4fACXcAwMTwJZn8Ce6RYs3JM8qd7pERpUwhBwBQcXx+coDJ5oOiQePRF4lNo81MTHKPdNfThDsAYFZi4qLUrm+wBqv1o9On3MZD7I+m038\\u002f\\u002f5XaGrhFb49YCfurxZs\\u002frPgLHuib5g6PXZAiCafg\\u002fi54IIOrVPXJ8Za7GnTbuq1B6o6s122p13ZeaMUALmFN89ygfzl7xwMACNJ2Qf6C2lJsI\\u002fQgUPZquCtSn4FD3WAAmyVeAkdDJ5mTGcWMh6NXtWRO4Cs66v6s7I5mJcKhQCLogS+Acan8CEbgzdxhAPOfGTTaA7TnHrIGfi93BNF+Jz5QD5\\u002feiWUHOAsCALSaQ1y1ellz3mD3WAOpu2xBfkgW3fVvvGh6MKXrl\\u002ftcGI6O2MhV73RZPRxCEARBEATxJnBLGafr4574tvalBmNX882\\u002fWr8U0iXpvJ7Nzr46yA0GAEAsX8ClD8UW2x1GjzUTxkRGBew4z3BLuBtswTbKuH\\u002fChF1VXYuqbSkt+eeMme65+1DZ3HjyndKtcDR0NtQddZDkoxk9mOTiX7vW48KAiBvKHrhI6JGkwnxeYT4PACagpwsYmTx6MgMPo+JsgrRbCZXOWttuOmGwNboekcC\\u002fjkUN3CboCtJOGC2OrnkPJEmYHXIqxqHiwS4LGcWZU6n+xplhb9T\\u002f1ajvfpUIo+JsBiVcyMiKYs+J4szD\\u002fVbmDn0O0l6qvQhHu0ngpPV3xipPNPWv1hAn44ayZE5Wofo0HPWkt2vhUOhIer5DRfQIDGD+C6ijmL1phceh8pRWn7XP5n5YpGhgDPxeVlnhZmvd9VMrczaFB4e6CeZ6QI+c7TgMh7qZGjEfDvUDET0ijp3cYKyGBzwVa86vjL4ZjgYNHbGRkeBUCXprXc3UVtNHl4\\u002fva65sNWrpFEqmUHJrWv7yhBHRxXFQjNn8js7mZV7Uxese59OZcBTxBr2GCDKUBZsbpeGMWxL\\u002f9UPdK66cO0ESP9W\\u002fPk+6fob42mAKZ64mzvYskdweLLw2kHRWj2NuNC\\u002fw78mkdiXmdBYvh2yvcqWRCUJhvVrtvKu3Wo\\u002fW1y9ICXF52nF52f\\u002fKtsHRKzCAJXOlyVxpCi8yjh3BoTLYFAabyiBI0uiwGO0Wvd3caFDUGGTVurY6g8+kycb6Y\\u002fHsiNVxk+GBXlEbu9Y8AADwWQz3u8gQRmqs5RprORz2JGFNzQ5\\u002fAo66qVB\\u002fKTMesxN6G6G3ETo7YYRK0S0Oxe76BQAADOBUnEPFOTScR8U5UZy5qQKPFl4uIkbOOPFLl5Rv2Qlf8zxIO2GwEwaDraFZv5tLSxgveX1Yt3qvN1YFs+T6OOEUOBRqY4ST\\u002f2r9BY5evcLoYjjUjY0I9tJsL4TTe7w4CgWjsKlc\\u002fwXFkYEW9vHK\\u002fyKrJsdw7S0w8HvZHMRrxaJw4FAosKmBn1Zn95ia1kcmh6FUVwhHPXGovPTQtfX0L5U7KmDCXW5pVVkVInovF7ZBR2zkqqcxmAsqm+AocrUw2q3X7\\u002f2hStt5bdhGOM7Lm87Lm5oNmvtH9fuBC0EQBLn6BJtwBwAwKZw7kv\\u002f9e9NHReojzghBOva2\\u002fVCtvzhPuj6e3YPm\\u002fcNdFJdX2aEMsvXKwLM4OltPOAkYga9tculdCXetxWJ1OOhuy6j6MTk2zpVwBwCca2kObcJdYdG+dOkXgvRStJjGi14SPW6eNFfCFMBjPrSaVAdkl3a2FFTr2+AxAN4r3z5OlJzE7XGipzuLze5+V8AKvAuQQSRi5jD04RZH4G7FNJybJrwrVXArhvn7gGgsZR3mi3DUGxIQNkJnI3Qm0AYA4NB8VuCqLJcUprNYcH3AAAB6W\\u002f2J1nunR3\\u002fLp6fCY8NErT7AlQ+n0YJ+n2glZkQKaWGhba8xlAWTnQxtKhbSu5Qfh8Lzn3APZwQ\\u002fK6ULHfd3xZQgHQ7SQfF7QBiaBn4vW4NYaJpFCXbST48Ek8e3BdF8PHgVusvElT5gvozm5+ED9c4JcvHbBmN17z59AB2x+4dCYzh6qbagsqm8Ua4xmDUGM0mSLDotQsCJEQtGxUvGpEaPT4ulUQO\\u002fkSw2+5HCmhMldcV1sg6tUWs089nMcAE7Nzl6Zk7StOwk3G\\u002fzw\\u002fwH3iMIUiLk7nrzHgBAk1z90R8nzpU32h1ESkz47QvzZ+QmO7ckSfDX6ZKfD1ysa+tg0Kg5yVG3L8wflxbj8XQAAAA+2HLs291nAQDZSZHf\\u002f\\u002fNGZ1Cu1u84U3b4YnVrh7ZDZ+KzGRIRb8qohMUTMlJjArw5v9xx+pOtJ5y377pm0kMrp3qOwz7eeuKrHZ0TfR5cOfXuayZ5joPGdnVFk7yiSV7RpKhokrcquyb9aAzmvPv+57ath1sWjH9szUw46k1ZQ\\u002fuhwuozpQ1tKl2HzkSj4CIeOzU6fMrohIX5GQJOUN8gSuplN7+2wXl779v3hvM7j3g6o2X7yeJjl+qqW5VqvYlGwcMFHKmQOz49dsroxOzESP87faT5obLAlW139\\u002f7lozekjhXSWfAAgiAIgvjVg4Q7AICK0dbGPZbAztzV9p3rm0mN\\u002flKN\\u002fpkYVur4sPkJ7CwxM+6qL3ifHBdX2aGs6lDaCYKKB5v\\u002fGjDQq28nCM+AFzy6R0JBZtDH8YPKYmdLpBvBJdddZ3f7EHq79A+93aNaHAAQweA9kLZ4SXReT99pUSzRTYkzb0yYsaPl\\u002fOdVuxUWj9SMjXC8WbLls4n3uwd7x+Z5zYPeD212kBCK5iyIZM9Wms8rTOe01kqjvdnsUDgIM0FacYxGxTksahSfniZhTY7kzKJgA33CTZD2IsVr9brO9vEMSriYNZFFleKY+9cwwkFaLI4OrbVSY+mch2Qj9EWK16dHf+W22XBSawicvhHRI3pRDd0LqbzR5zqOwtGrlP+abicH6XFZMbSCWc+zO\\u002f+ZcdDbPD6dEuBpHaR9WCbcB3wv24nAZQoUrJ\\u002f+XHq5bA8hvV3a77VyXREc6iaVOxoO9Zsgp3c0mmrGCOGcY5DQETu0WpTaL\\u002f86\\u002fdfpUpsdvnJjszu0RnNNq\\u002fJoUQ0AgMtibHhufaxYCG3mQpLgj+OXP99+sl2td4936IwdOmNlk+K3I0XJUeGPrZk5LTvRfYPu2tV6vcmi1BrveGujWt85oeFCZfOFyuZ\\u002fXD97\\u002fbxxAIDXN+zffKTz\\u002fW+y2I4W1Ry\\u002fVPvqXUsWTfC5bkRta+fVkc1Hit7ddNhs7TryKLVGpdZYWi\\u002f7dtfZ1TNy\\u002fn7dDA5z4Pp53vDKjyZL4ANX79TJVO9tPnKkqMY9aLUBg9naJFcfKqz+YMux2xfl37F4Yo\\u002fS4u0qvTPhvuN06Rs\\u002fH9Sbui4l2uwOY7u6sV19rqLp8z9P7XzjbqkocLutkeNwi\\u002fdpQBaH\\u002fbSsYVGczzcwgiAIgnjVmy82E8OXpHDHflf3ssoqcwWbTVXNzVUAACaFE8VMZlN5LAqHSeGE8LvTfOlNcGiQrM\\u002fO\\u002famo0Giz7ayqXJ4+5P76CphMpbFr4rYmiBYxEWyPmrJ6tTrIhHuYZ+12kzaU\\u002fV4vqxuOtJdAwUx+zHvj7xLQel8Eh2PYspj86ZKsR89\\u002fXa5tdh8qVNedVlZOCu9aDLZ3aBSKxd71bcEQxC5ABheO0cSsyWJWaHoKTZD+Fw71wUX5i66O7Vlhf0sV3I77Pa6qLJdOtN7v7DyjNJ832pvZVI\\u002fKsnTRPemie9wj\\u002fmWF\\u002fS0r7G9wtP\\u002fVG6vgUDep3AHqmRPHSjkHhnf6JnjBlAOHMjfZDZ8qhENBCJhwF9DC4FAQKIHOlAgy8FXtIWjg9zIDD1yqaXL4apnVJ0Z74KcN+P7pkQpdVy2CL8ncgZsbGk6X4hge8L3abKqDQ0FDR+wQOlxY\\u002ffw3u91TpX6IhRw\\u002f2Xar3fHit7t3nw1wOaSmVfnwh7\\u002fft3zKfcsCnAjVtHa8vfGgK9vu8u7mwzNyks5XNrmy7S4ESb760\\u002f5p2YlcHy0WDWarXK3fcODCd7vPwWNXECS5+UhRZbPio0euHcicez85WVL\\u002f5GfbjX6z+Qaz9eOtJy5Wt7xxz9Lg\\u002f8lyjSELgK92nP74SrG\\u002fV2mxESjbDmkx+vwa62cIQRAEQXwJ8DUSACC3NGltSo1NobUpNTal1qZ03jU5PKokXMwOQ60h8NeMXhg6CfeMiIiHJ01+\\u002f9TJ148enhQTK+EE\\u002ftY6kPgMhnvCXW2Gz4m7k3j2oy+TK6bHJ7hHfBEyPb4\\u002fu\\u002f\\u002fcvvuhDl5wLIMf82H+PVxq4C\\u002ftAQlpnI\\u002fz73343Bel2ib3+C\\u002f1R\\u002fuecGczaO4Jd4UulC8LMqJ0mC+6su0JvNXpwrs9x70QMXISeWuqNN8576osxVDCfVgw2HXBLNgYw0qEQ\\u002f0jmuWz28\\u002fVB+\\u002fh5KGQ41B7kwXAsQATzjhBrJzZXcCnDW0Z+IAZ+L3MCqKJjbF\\u002fEu7B5PHplBCcWjjp7Bo\\u002fC+06MXBWMG30QwXHcA6FF7BPfa9XjkVH7BDadbb8ua92uM+4wHEsI06SHhsh4DApOK7Wm2rbVKX1MmcDw2unZ3dt6okkwZOfbT96qdYViYkQzBmbmhgp4rEZGoO5qllx6GK1q\\u002fL98+0nzVbb31fPcG3f3cdbjxfXySZmxl8zKdNmd3y353yTXA0AIAjy+73nnfXac8amzBmXarLYvt19ztmGRW+y7DlXsXpGjueTdXnvt6M7z5QBALgsxuwxKdlJkUIu02y117Qq95yraOvonJNaWN3y3Fc733topceD+82vL9xCEF17Qmu03PJ6Z+cWPpv5wzOdbXC64\\u002fttBXOqtP6RD\\u002f9wXJmCzGHS54xNzU6KFHFZFru9Sa45UlhT1th5DDl+ue7ZL3e899AqLLhjtlyt33ai2JVtFwu5Y1OjxQIui05V6U1VzYriOpmDIGbmdLYAQoLhXL8NQRAEQXokcML9g4qH4RACwN8nTQEk+OD0yVW\\u002f\\u002fPT8zNmLUtOGzl9iEZPZdWYNQIUycGfqBIHQ\\u002fe6Ftlb3u344PKdgm9yyzH2kthlOyDs7YzhRMPxfo9eEJNvuxKEy\\u002fpW99raTH9jdGq2eUVaprHoR3eMKRE+Fc9kqQ9d1juKmrrkgCNIjzYY9rtuJ\\u002fDVuI\\u002f649223OtRdA8NHu6UFDnkTzQrq0mDfRTHj4RDSP3AM712tccDMOIfamwN7T3uXIb5wgmhiE0zSthcCJpoBACJaOBzqrRZTPRzqJpIVO8BvLR5NEPB1UFsDnzF6hY7YoVLeKH\\u002f5uz2uk2s6lXLrwvz188YJuXA7O6vdUVDZtPdcxbLJPucNfL\\u002fnnCvbjuPY42tm3jBnHNSf5PG1s77ddfbzP086f+h3u8+NSYmePSbFfRt3Z8saZ49JeeeBFc7vPdOyk6594Vtn6v+3I0UAgPXz8v5x\\u002fSznxmNTYtb95wfn7dOlDX4S7s5s+9xxqc\\u002ffsgBqXP7giqnvbDqy6XCh8+6Ropp9BZXz8\\u002fpaHBOMmAiP+b7udf0YBuIkwq6xoHXojM9\\u002fvcuVbV+Yn\\u002fHsTXP5bI9\\u002f8v3Lp+w4XfrvH\\u002fZZbXYAwNFLtT8fKFg\\u002fL899G19OlNSfLK4DACRHhT+6Zsb07CRoA73Jsq+gMjspCoojMRx+g14FRwEAACRwRXAIQRAEQQIJnHBHupMbDTK9IVcqXZSatquq8qEdfwoYzGypJIzFpvtt6f72wsVwqB+khoUXtHZlzFt1OpXZLPIsRYckizxOI042NjgIguL33+KkMnmUz3td3bR3Tisq3PPgAIBr4yan8kJ8dpjMlV4XP2Vj\\u002fTFXhCCJU4qKJdFBndT6EhMmqJJ1fWuV6wxFDW258ZFumyBIUPTWrstnXFpi14BfdrKrnJOG96aqd9DJzEGlbyIZA1S8z6HyGDjTQpjhASTUmHjvO4b5x+yfBTmRIEmZgT+tjUaPdsahUm8I3O2kd2vqetUcRMJ9IMvbnWhBXMeyEhajQx9Mf38IOmKHyju\\u002fHnImrwEAfDbzw4dX5SR7P\\u002fWlUymTsxImZ\\u002fm8hqHSmT7dftJ194VbFqyY6mXZADqVcu+yyWwm7d1NR5yRV3\\u002fcP3V0It33Qqz3LJ3kqjKKDOPNHpvialnDpFMfWDHFtWVabERqdHhVixIAUNsWYBnb7MTIt+5d1r1fOZ1GfWb9XKXWcOBC5wf5y79OD0zCvT98svWEUts583X2mJTX777Ga8nWNZOyLDbHf37Y67z71c4zq2fkMumBv7kfvFAFAJiQGfe\\u002fB1eyGTR4GAAui7Fqms9ZESPZrKiUkzIvR28Rgz1JOlyv4SEIgiCDKHBGFelu0hefr\\u002fj5x7u2\\u002fbGrqtIZ0VjMxxsatpeX\\u002fVZa4uc\\u002fz6fpL6MkEihySdYGRSAJQiGX3tUcUG02H6qr6xr2rVTuMfuYzwj8dS5IF1XuZfoAALA4ahwUCYkl3Z62SF0HRXoqPTICiny490QIr0YgIwfp1kWZAP56fbpTmM65brOp0W4jw4bSbY0QX6g4jUvzqD7rV0J6yApgET96V94eEBWnDXBBMQKJZSUH3AU1+lI4FAoB1\\u002fPEABbksqLBaDU1wKFuQlhQHyQq5iX11l0w\\u002fe67Q0fskCiqaT1X0dXn8MXbFvrKtgfj10MXnfXRAICJmfFes+0uN80bPzqx85qTUmvYfqLYc7wLh0nPSvC4OjUmpes0Y0JGHNRtPDm685TY1RbGlwdXTe2ebXd5fO0s12hFk7y0PvBbbghS6Ux\\u002fnuo8yjkvJHjNtjutmpYdf6WIXqUz7TrrMfHXjzAe+817lnrNtiN+3Jw2Pk0Af4Oj4vjrE69hUdCLiSAIgvRY4OvkyLAzWgwn3A\\u002fV1s5MSISC7nAMGxsZdayh66r+5+fOzksO3N1vX021+10Bw18dfY\\u002fU6j36n0Yw+KMEIfsy7C6DHyNlCmRmjStSZ+hlD1OX8UkxXxz0iJyoqH\\u002fu193PrpzDY\\u002fZLLgm5WrGoXRMj5MaTMdzAs2TkptOthgPO2wxKmIjpcwb3UKYP1PoAACCiRQTM34WQiBYhMzfD0aHKTtp0No3WptLa1WaHyUyYLA6ThTBbCYuNsNpIq4248h9p84zYbKQVfroBRMM9kjWhEsyKncPO8NrLTApLzIxq91sKrbS2d1jloa3+Vts6AjY8iWTG9aKs25dgOqEfaN9+oH07HB0CevfGQEfskHBf2nRMSvScsT77ugRjl9uzrZs9xm3ECwwDa2eNKa7r7GK37WTJdTNzPTfplBgpgiKRYV0T6dLj4O8gIl5nMxyjxd9bS8BhTszwV0QcHc4fnxZ7trzRefdEcR2U9x8W9p6vcF0FmT0mRSz0d9jBMDBnXKprIdnTpQ1BVqbf6K0HERIQm0rbOP+Wjy4f39dc0WLUsii0SZL4h7On54T1\\u002froXgiAIMpKhhHtvPD5lGhwaSkZLJCwq1b2d+v6amhdmz3HbxIsZCQnuCfdzLc3fXCi4Y5y\\u002fziq7qiqrOjzmhyYIQ1a71GzyeOYUrrT\\u002fvqclcyPdE+7Nxl72MHWZmBLLZdL1Zo+vFtsKSo+W1y3OTZ+SlpAWGR7OZbPotKHT+h8ZmqTs6Q26P5y3Lyv\\u002fy6On8Ok+p1ETpKVWu7mk4wMSdPYGTeJfjw3PmUw6W+BWzgIa\\u002fLW\\u002fX7F71QF8YJgchgZjTbOpTm5paTe3tlta+qkX9gCg4f1SR4ZjPtsjDBdXwV4ezc\\u002fzn3AHAByV71oZcwsc7YOj8p1wqJt0XlBprCBpbAFaZwxlNsICh4KAjtghcd6tvH3lNH8F6QF16Iz1ss5u1DiOTc1O9Bj2ZpZb3\\u002faSOpnJYmN5K5EO48OrH7tXUktF8MvOoHUeewmCtNkdNB+dakYlSP2UtztNzIxzJdxLGgKsSzw0FVR2XQSaMspnOyCX9Niuq4+F1QEOni4LxqfDISQ4QjrrX3nz\\u002f5U3Hx5AEARBkJ5DCffe+NvESXBoKGFSqTMSEvdUV7kijVpNuUKREQHPknO3ICXl9aOd3RudXjtymEunrx3t\\u002fVvo5fb25\\u002fZ1NhZ0yY8OWXdOg92j82YYox\\u002f7UId5LpGq9\\u002fzRvcCgUpePy\\u002fr5ZCEUVxlMP58sdI9TfH+7uP3zTXCofxS9\\u002figcQoaMKPYcAT1DYy0HAJgdikPNN8RwFkpYU7n0BBrOIwEgCLPF0WGwN3WYC9tNx62OrktHQsaoNOHdXc81rARTLxnCitRgsClwlmFwmR2mcl1Rme5ijb5cbml17z40rFGxfqlwpwzPK09X2V4eL5p+sP1POOrppHL\\u002fgsjVofq4WQjzCeV+ONpNnmgqHOoDjb0z0Tkc2Yhge5e5Q0fsvrPaHVXNCtfdsal9OqMuceu4kiAVMWiBv\\u002fEJOEyJkNuu1gMAHARR1tA+Ls3L7yDwXN4TAIC7rfkErXcKPJezJn03VwxmAdKkqK42QbUtfS2OGRSXa7sW2eo+V6A799fT1fndPz6b6WpEgyAIgiDIIAp8+vX86J\\u002fhEDLkLUhJdU+4AwB+Lb78\\u002fKzZ7hFIolCUFxVd0NpVPeEgyaf37tlZWbk+N3d6fAKT2vluqVAqfi8t\\u002fe7iBbNbEb3TlLg4KNJrFsLjyQW0flzsju\\u002f55JZefduE3D17wh\\u002fnS0zWAE\\u002flIHx+9\\u002fAzhIwcGEaZIH37eOu9JnsbAIAkHU36nU36wAWbYtakCdJ3cCzwQX5oMjr0cKibAS5gZA2N9I2NsBZpzpztOFypLyE8V5a+OrhnZ0II65+n7SdX616OYSVGMmPbzF1lvN1ZCPMfzd+tj38QHuiVP5q\\u002fNzsCZKkkzOh4dioc7S2jw2APxVnEYOndRR10xO47td7kWuwHw0Afc6Zqvcl1W+K3b4k7V8IdANCh8\\u002f7BoV+pWPfKVwF7QN0z9d2F87tO17XG3kzFGHQKt6T5HW9tdBsJzGZ3GMxWqEV+d2FXevggCIIgCDK4Audi6Fdj29Or3tzkZCqO24nOthIAgC0lJU9Om+5Kmnt1z\\u002fjxD\\u002fwJT1c8VFd7qK4WACBiMpk0mtpkcm9W4y4zQpwXFbLlGRk41eToasnS96pzP3T2rq8lAABqKDoPRAp5Ty+b9dKWffAAgvQQhxY\\u002fK+bHYuX\\u002fGvU7QBCpED49NV14Twx3Iei3LkwDIJgqSyalH6\\u002fDdTfoTcA1NtXB9u2nlActhMch6yrTf93DhoWrfi\\u002fPlSzf0PApHPV0tuNILCt5pjjwkhX+nek4fErZuaCFH\\u002fMlK+FQH1gcV+eO8w8dsftOa+g60WUz6H1sOag1dOWjA6ZoXbisrnWGNG6\\u002fjzsqxd\\u002fFS0pvf20m3Uv7Goj7P0RvGn4Jd6vN7mrg3js2e+Drr6ygdzeCIAiCIP3KX\\u002foVGb5ETOY16enbyspcEY3FvLuqcmVmlttWsIWpabnSyCJZGzwAAABAZTYDs\\u002feTb6d7xo+HQ33AoTLdE+4dFp3bYIhBT86lhuZL2tpJOQqd4aO9J+EBBOkhBiUiT\\u002fJqVtjDLYa9HeZCnbXa4uiwk0YASArGouAsFkXCpScJ6OlS9gwuLRF+\\u002fDBkJwOnb6gDW79PCcWluN4xOgw7WzeeUh4M5mUZ7vqpwn3oGyF7ebxoxr72rQE7uW9t+YEExCzxNfBA0E4q929p+haOdiNmRI0XTYejfWAj\\u002fK0MebUK5k07co7YveNeJUNxa9LSO+5578AX6q9wldgPPLuj65\\u002fvi8ev19vMvjtHED80hGwD++MQBEEQBBlcA3ruiwyk28eOcyXc08Mj7s3PX5qe4bkJDAPg1Xnzr9v4s9URuIACMjsxaVXWKDjaB1EskcLStQZXvbGrr2XI1Ro8Vl6KYgVuqhikB+ZPTo8Sv\\u002fLHgXZt4NnWCOIfixqZIrglRRDK5QSHLDsZuApsgPMpg9Wf52zH4a0tPxrs\\u002fXjRcYgJQRpl2Bk5exnH8KVRN3xT+y484IkgHX80f1+tL10TexefJoSH\\u002fTI69NuafzrdcRAe6AYD2PVx94R2QV0bOTIT7uiI3Vd8t97oerOFJPuUUhZwuvqKGM3Bvifdtwymx0sImSyBr9m4b8NjhaCO22gJ9pUJCQ6TTqNSXFXqL966kN\\u002fDFzn4yQoIgiAIggy6YXYyigRvbGTUmMhIJpV63\\u002fgJs5OS4GEfRksk\\u002f5k77+m9e+ABv+L4gncWLe7D9wIv4tkRl9T1rrvNRmWNXpbMlbptEhqVutZWk8f6Zkkh\\u002fSnzRqdMz0j441zJtoKSooa2QaweQpBhxBFE+gYHA5q+GeBsEQDASlh+bfzivOoYPIBcRUbgXs4VTMwTTStQHYcHurmkOVuivZAnmjo9YlEcOzlgu6F2S8sxxZ7TyoNWIqh2E9MiFqRyQ1krAAAY1g3cew0dsftOyO1KkRME2dqhjQ7nu433jHu787aOYK\\u002fktam6tgzjDWgLIF8t4925+ssDAHqaqvZKrfc3c7c\\u002fCLks+ZV\\u002fRVaCJD1W7DmOIAiCIMjVAyXc+8rmcBxvbDjT3NSs02nMZjtBLExJvXXMWHi7wfDj6jUceo9LIdaOzrYTxIsHD7hPbvVjTGTkFytWiVghXqInV5T4V8t598jetsL7Uhe6R0JiT+tFKDJWFOz1iSAxqNR1k3PXTc7VmsyXGmXVMmWbRq82mqx2h+tF1hjNZ6ob3R81ITlW6FaghCAjSjCpGRIEdYwKlQCpvlDT27WfVr\\u002fSYmqAB3qLTeGwqTwWhc3EWUwKm44znP8xcCadwqTjDMaVuwqLbEvzt\\u002fDjkX4wYvfy2ti7ag3lKmvguWsO0n6248jZjiMsCieBk5rATuPThCwKh03h4BjF5DAY7QaDXdtoqqk1lGttavjxvqVws1bF3AZHkV5BR+y+YzNoMRGCZoXGebeoprUvCffRiVIcxwiCBAA0tqtNFhuLEaBJukpnUmgMztsUHM+Il3iO96+6tg441E1Na9c2KVHhbiOd3Fvx2IOYrVvTqoRD\\u002fWxUgvTwlYR7VbMCJdwRBEEQ5CqGEu59suFS0YenT8kMHt1CUsLC3O8+tXe30mSK5HBfnTffPT4AepFtd7oxJ3e0RPLCgQO++rk7CZnMe8bn3zkuj+F3LdbemRCWCkV+qT+6ImZCCPu9AAAajIqNDR5FdjiGTw5Pd4+EEJ\\u002fFnJaeMC09AR4AoF6hvubtb9wjjyyampcY4x5BkJGDilMtgZIzwTQxGKZ0NvXH1f+RmZvhgeBwqfxYVlIMKyGCESVmSMPoEh5NQMUCZFtcagxd638g\\u002fWck72UmhX1H4uOfVP\\u002fHHPQSoyaHoUxbWKYthAd6JZoVf0fi4\\u002f1RBE3Dgzr1ouH0gAX7g6J3iyiM8CN2qORnxLoS7n+dKlk8IUArSD+4LEZGnKS0XgYAIEjy2KXaBfkBTm4PF1W7bmfGi9mBEvShVdYoD3hV4HRp17XJrAQvs1G5bn1mVLoAxxa13lTrlsEPBtaXLj8AAAAmZsYdLux8nY8U1V4zyd\\u002faWgiCIAiCDGuhz5OOEA6SfHzXju0V5fBANzw6Y3NJMQDg1jFjMyIi4OGhKlca+ceN6081Nf5VUX62uaVWrbI5HAAANo0WwebkRUVNT0hYmJLK7W1OP6AolihHmODeVcbssL1W\\u002fNu7ebfT8NC8b80O26uXN9kIj2+AUyLSwxk898jAkAq4cAhBRrBg8oYEGbh+LYTIHqw81yd20vZl7ds9zcNSMVoWf2wWf2wad3QEIxIeRoYYtJfj2Mn3Jz\\u002f7afVrFiJAXizkYllJ96c8y6H2y9\\u002f6IBPu9yU\\u002fk8K9enJtI\\u002fmIHUJzx6VtPV7svH2iuK6gsjkvrfeFFyunjnYm3AEAGw9d9J9wJ0hy0+Ei192BTwRbbfZDhdVLJmbCA1c0tquLqrsWW56Wndg1dkUEn+O67b6xVztOlzmCm8vr4t5C3WC2Ogiip8vbzs9Le\\u002f+3o1a7AwCwr6CiTjYlURrKQiLk6iY3G4611RbIm2p0yga9WmezGGxWDANMCk3EYElZvBR+eLYoclpkYiLPo\\u002f6v\\u002f9gJ4ryi6XhbbYlaVqvtUFlMeruFguEcKl3M4jp\\u002fn5lRyVkiqderVdQefoKuSkP\\u002fNeywGC8omktUsmqtssmgbjfp1Vaz2WEjSJKOU+gUqpDOFDHYsRxBIi9slEg6LiImktUvp1jdVWoUP1UVnJTVNRu0DpIQ0VmjwqSzolKuS8phU\\u002f2dj7UatRuqLhxtranXq\\u002fQ2K4dGj+UIxoXHLInPnCJN9PpS98VQfg1darTKk+31pSpZlVbZZtSpLEazw04CUkBnCelMIYM1SiidJImfKImPYHb9tR1gDpIsVclOt9cXq2T1elWLQWOw20x2KxWnsCi0cCY7jiNMFUSMj4idKIkXMQa\\u002fXURoEpcj0OtHj7iy7WwaLUsslnC4OysrPLcCAIC5ScnfXCwAAOyvrR5GCXenybFxk2PjnLdNdjsVw2iU0JeD+bI2fqp7wh0AcK6j6skL370x9lYmJfBXO\\u002f\\u002f0dvM\\u002fCr4t8nx+AMDNibOgyMBg0qg8FkNnCqrtLIJc9ahBrHdnCa5Nc6gM2I\\u002f7vem7BmNXpWFAEkb0TPGS8aLpTMrgn1UgQUJ7GQCQwEl7IPW5b2rf1dh6VmfaF3miaevi7qXjDHggRIJMuFuIgW4e3a9G8hE7hKZnJ6XFRlQ2KQAAJAme\\u002fXLHZ49dlxjZy8TZymmjP\\u002f\\u002fzpLPQu6CyefORojUzc+GNrvhxb4ErO89nM1dNy\\u002fYcHwifbD0xa0yK18p6kgTvbj7iWgYpK0HqtRlLdlLXZcg6mepUaf3kLC+TSgEACo3hi79OwdFAqBRcyGWp9SYAgN1BFFQ2T8jo\\u002fJYUJLGQu2p69q+HCgEABEE++dn2L\\u002f9xfTDr01ptdjot8KesP6itprzf\\u002fgcFcQy7vPZJJiXYX+mkrO6mAxvgKAAAgOfzFtyRMQGO+jZ7+ycNejUUPLf60TCGv1UHjHZr9qb\\u002fwtHgjBfHbpp\\u002fKxwNhcyNb1oJjyuRt6bnvzQebqBKkOTupvIfKs6faoe\\u002ftwIAAAn0hEVvszTq1efkjRsBAACk8iNuSB17Q8pY\\u002fznHvlCYDd+Un91UU6gwG6AhOyAsDnuHxViubt\\u002fRUPpW4cFEXtjNaXk3po5jeX6F51DpKstAX3QfOobya+ggyTPt9fubqw63VldrfXbfMjvsZoddazU36NWFyq7LnBlCyZK4zHUpY6Ws3tcUZm9622jvWhdnVlTyN7NvcN21EY43Lx78pvyM+9X1NpOurVl3oLnqf0VHXs5fuDxhtNtgJwdJfHj52CclJ9z7J2ut5hKruUQl+6mqIDcs6s1JSzOEErcH9cZQeA2DoTQbfq6+uLmmqEHvsa6hi9JsUJoNAIDz8qYfKs\\u002fDw0F4PHfW30ZPg6M9VKlRbKy+uK2+uPvnBQDgcNgtDrvaaqrWKg+1Vn8JTlMwfKo04fqUsYvjMil9nqDWa8H+mUTcVSgV314sAAAwKNQnp02\\u002fMSeXRaUCAJLffxfeFIC86GgcwwiSPNcSoNRiiHP+GwfSPGnOV5x99Qa5e\\u002fC0svKOUx\\u002f+PWPZ5Ah\\u002fpTr+HZJd\\u002fqhiR7MJ\\u002foY\\u002fJSIj5A3cgyflc1HCHUGcmBR\\u002fX5ycTA4vf277T5DLMPZRtb70hHIfHPWBS+UvjbphUvicodmbAvEF7WWXBHbqkxlv\\u002ftL42WVNb87ge4RF4SyPXj8lfB48EFKsII5dAABL0L10hoURe8QOLQwDT6yd9eD7W5y919vV+lvf+OW+ZZOvm5nLpHs5CW\\u002fr0J0orrtQ1fyfOxbDYwAwaNRn1s976vM\\u002fnXff2HDAaLbdNH8cVJRtszu+3X3us+0nXJHH187039qlnzQrNH97f8trd18TGeZR1me12d\\u002f+9bCrEwsA4J6lk9zGu4iF3FEJ0pIrVw5e\\u002fHbPRw9fmxYLVzuV1sv++eUOjaE3F73y0mIOXKhy3n530+HPH1\\u002fDZwdOl7u7f\\u002fmUo5dqW5VaAEB1i\\u002fKG\\u002f\\u002fz4+JqZc\\u002fNSvRbLK7XGs+WNe89X6I2Wzx9fAw8PCCGdFccVNnrmuAmSrNDIc8Oi3IN+nJM3waErLiiag0+4G2xW6DcBAMRwBP6z7cNIiarz3etSoGh+6dzuyyp\\u002fXV67q9IqXinY91nJyafGzFmT7PNKW+84SPKL0lMfFR832q3wmA91uo5XCvZ9WXb62XHzlsV3rVUupLOaDJ19tEaUofwaVmrkv1Rf3F5f4jWtGaRydXu5uv3Dy8euTxnzeM7M8FAURLt\\u002fOiwO+\\u002f1HNx9urXEb96C2mv5+YmuDXv2QZ57X4rA\\u002fcOy3Qy3+Sl6KOlpX7\\u002fnui1lrp0oT4bHgDNnXEGJx2D8qPv5N+Rn3CxtDUL1O9U7R4R2Npa7r7sFwkMTRttqjbbXxXNE\\u002fx85ZHOdzCl2\\u002f8nL2hgS08fJl585+ff6CVZlZ8LAnFpUaw+M3ajU1Kji9i\\u002fiHY\\u002fjTo6596OwX0LzgOkP7YwVfTwhPXRKVN1MymkMNtk5NYzMebi\\u002f+s\\u002fkcVDjvxKLQn8paBUcHkETArZL5vPiJICMKjyYAgfJRRrseDvUna\\u002f9XpJKA3NLssZaDH8mczNsSH+XThPAAMrShvQzhUHl3JT1ZoDq+W\\u002fZbu7lfShNwjDJBNHNp9A08qgAeCzUGzmJSWAF70+vsWjg0nI3MI3Z\\u002fmJgZ\\u002f\\u002fC109\\u002f\\u002f7ajzrt5keWfT4Y\\u002f+OJ6bHJUgFQk4TIIk9SZrs0JT3aKUqXQAAInQZ+3b\\u002fLy0m+bn\\u002fbSvAABAkOR7vx3ZdLhw9tiU5KhwLouuM1qqW5QHL1a1dehcD1kxdfSKqV5KAvtbbnJUUU3rxeqWVS98Oys3eWxqdDifbbE5alqVu8+Wu\\u002f+G8\\u002fPSZo9JcXuohzuXTPzHZ9udt+Vq\\u002ffpXf5o7LnV8emwYn2222mQdujNljecqGkkSYBhYMjFrx+lSzycIYPWMHFfCvbxRvual75dPGZUaE8GkUc02u9ZgVmqN7Wr9gvHpXpveAACEXNb\\u002fHlxx51sbjRYbAECm0j39xV8CDjM3JTomnM9lMWx2h8Fsbe3Q1rWpXD39x6ZEezzLwMoJi+qe5i5VyXqScG+EQ1cUKHzm4rsrUcu6p1uC\\u002fzWGvjJ1O+m25vP\\u002flZ56u\\u002fCgoyc5JncKs+Gp038eaq1+Z\\u002fJyRtDTEfyTmfSPHP\\u002f9rO8d6kebUffI8T+OtNb8O3+xc3rEUGj4MPCG5mtIArC\\u002fufKL0lO9+8W8cpDEz1UXdjeWvztlxcyoZHi4h+Rmg8JscDYzefH8bj\\u002fZdpd3ig4n8ETu1yeeOLXdf7bdyeSw3Xd085+L7krgieAx34b+a+iuQiN\\u002f4OhvtbohnaIkSPLLstPvXTpidvR+HaAGverBY1tmR6e8M3m5aMCvzobmyDvSHG9sAAAkCIUBs+1OYg6nUatRGI3wABLIOFHy+sQZP9UdgQcAOKusOqusouHUHGF8MjcymSON44RzKEwWlc6iMAAgDXaLyWHV280NBnmNXlatbyvRNDpI7+0aMYA9n319ZEhXZO0pKd\\u002fnVyYEGWmCyYvp7KEs6AhI3\\u002f8JshJtQYupa1E4P7L4Y+9MfIKKD0IRItJHaC97lSeaNk40da\\u002fs952tv8JjfcCicPJE0+ZIloXT+zo1OHgiWkSrI8B3LZVNAYeGs5F5xO4nty3MZ9Fp72w6bLN39pqw2OxnyxvPlgd4U3n1xNpZTDrtqx2nnXebFRpn\\u002ft2r62ePeWrdHDg6IO5eOmn32fK\\u002fTpVabfa95yv2nvfSpRMAkJcW8\\u002fLti+Com7njUlfPyNly9JLzroMgfD3bAyumLsrP6GnCferoxEUTMnafLXfeVWgM3+w667kJAJ7NbbpLjxV\\u002f+88bn\\u002fh0W2O72hnRGMxHiwInjwZLTljUjgb4hSpTt0MRXwiSvODWJwHSYtTKTPogeyZ0LwAHAOSGD+bViNBydoaJ5woBAC+e29277g2QHQ2lSrPhuzk30vG+Noat16luObihj\\u002fXUm2uK6nWqb2avY1Pp\\u002fVG0O8QN2dfwq7LTr13YD0dDocNivOvwr+9NXbk0PqjUmR8lKtnMqOR9zZW\\u002fVhfCYz68cHb39MgkIZ0FAPiu4lz3Q5kvBpv1ubM7f5y7Hh7wbVi8hk6n2uvvObLJYAt2gsWgMNisj5z442BL50XuPjrUUr1011f\\u002fN3NttsjfH+iQQwn33mjV6wAAYyODvZzOolEBABZ77y\\u002fLjGQPpi2uN8iPyb0fHG2EvaCjpqCjryepf0u\\u002fZo50EBpWupOghDuCXBFM+kZlHdCMlc7Wp5PjYByV74JD3kQyY29PfKz\\u002f8rB2z6WkkdBCe9krEpBH5bv2y7bCA73CpwnTeTnZ\\u002fPzRgrxg1vMMLSE9vNUcIDeqHtjDV38bmUfs\\u002fnP97DG5KVGfbTt59FJNwNrWgE3eH1o5NTcp8n+bj9TJVPDYFbFi4d9WTV2YnwEPDBSN3vzybYtSosM\\u002f237KavNydMIxbM2s3L+vnhGw3c2zN80TCzhf7Txjd3gvsuGyGE+um718yiiCJOk0qtcf58fLty\\u002fishi\\u002fHSmCB3oiNTr8p2fXf7v73MaDFw3mACmPRKloxbRBmHbg4rWEvDTohHu5Rq63WeCom4uK5kVxQb33vCfcwwY0e9LfStWyeK7wf5eOhCTb7nS6veGfp\\u002f96d8oKeKAnZCb9jQd+bDN2TTfptbPyxtsPbfxx7vpQJYuHi6H8Gl6bmP1O0WFLH+qI\\u002fXCQxBMnt8VxhV4PJsErUcmmShNfKdgLD\\u002fimtpq+KT\\u002f7WM5MmUn\\u002f36JD8LBfJ2R15+VN48Wx8IAPw+I1BAAUq2R3H97kv50Ri0ITs7hCOhPDMJXFpDDr+9J2xjVrJ3gqi+mmAz8Ff2U3GG1G3U37f\\u002fpm9rq8iGD3ad+hhHtvOFPnwfc0N9lsAAAeI9jOJ4g7HMNfG3Pzc4U\\u002fHZWXwGOhgGPYI+lL1yVMhwcGnEQQmr+XCHIVENHFcKgbo0NvIcwMvGftU3tNa\\u002feZqggJg11XobsMR7vBAHZTwkP9t+QjAMBG+vtijPQF2steGR36H+o\\u002fLNP6rFeiYJRR\\u002fDytXa21qUwOo42wECSBYRgVo1ExGpvK4VNFApooghEZzYqPZiVIGINZ8ChmRHqvEXDTZu5BF4WhbwQesftbZpzkvYdW1slUxy\\u002fVnq9oqmvrUBvMOqOFgmMsBk0i4sZLRLnJUVNGJaTGRMAP7mZGbvLU7MRjl2qPFNVcqmlVaI16k4XPZobx2bnJUdOyk2bkJFEpXnqIDxiNwYzj2O2LJiyekLnjdOmRopoWpVatN\\u002fHYDKmIN3V04pKJmSnR4fDDvMEx7L7lU1bPyNl6ovh8RVNNa4fWYAYARAg4sWLBnLGpC\\u002fPThVyWc8sEqdC5Sm3w6FTKczfNWzdn7LbjxRerm5sVGp3RQsFxPocp4DCTo8Iz4sRTRiXAD+uGy2L8bdW02xbmHymqOVPWUFovU+lNGoMZx3EOkyYRchOkYdlJ0kmZCd3b0A+wnLBIDHj29+xJhbuffjJOBYqmoBPucCtzDIDsINJPDAr1lQlLVBajymLqsBhVFmOHxeT8v\\u002f+s08ArVcm4VPpHl4\\u002fBA1eImZwEXlgYg8WgUHVWi8pqqtEqdX4vaQAA\\u002fqi7vCA2fUlvuxhbCcc9R34NmCnm05mp\\u002fAgxk0OnUEx2W7tJX6dXaa1eGnydkzc+f3ZXMj+oD\\u002fXVYYi\\u002fhuFMznVJORuqLsADnqg4HssRCulMIZ3FpTEMdqvGam7Uq+SBmpVbCcfjJ7ftWnIP1dt6FUEqVbdvbyhxXzY5iRfm7PpS3NHm63f4sbLgkezpH1w+6l7QjQGQLpTEcQRWwnGpo01l8d6L4pfqC8En3IfFa2iwWR869puv4x6Xxrglbfz8mLTc8CgK1vVTHCRZrGo70Fz5fcV5tdVfD0EBnRnPFSVwRfE8YULnDZGU5bE0S0Baq\\u002fnWgxsC\\u002fpWJYHKi2HwRg8WjMfQ2i8piajPp2k3+GhjqbJY7D\\u002f\\u002f624LbUkL0wQko2JQx4i6czW7V6dr0\\u002fvalu1q1GgAg4aD65V6i4ZQ3xt78aeXuH+sOw2N9w6WyXspZN03cy5OP0EItZRDERcII\\u002fPUJANBuboljh7KfnR8Ki5e6qhAq0xVC61V4lSeaFstKgqMhZXJ4P+lE+g7t5e4UFtln1a8qrT7PqhM56TfE3SdlxsADQ1UsK\\u002fBBqd3SYiOsNJwODwxPI\\u002fCIPTASpaJEqeim+XnwQM9RcHzWmJRZvruf+3fu00fh0BX56bEFnz8GR694ZPX0R1YHLmqxXpkHHBnGu3PJxDuXTPQc7zGxkHv3NZPuvmYSPOBp4\\u002fO3wKHgpEaHP752JhztOR6bsXRy1tLJfeoSMCpB6mcX9B2XxkjkhUGtfrVWc4tRG83muwe98rNiqlOBshkOeeMgiUoNfHUkiR\\u002fOowW+OE3B8PWp4+AoAAAAK+HosBhVFtN5eeML53bDwwNua33xz1UXup8oZIsir08ZMyc6NYYDzyhykGSJqm1rXfGGqgI\\u002fnY5fu7B\\u002fQUx671J1\\u002fy08dLkDvtrhQsXx65Jy16WMzQ2LwjGPYlYSgAp1+87Gsl+qL0KJsE01hbQ+d7kZRob+a3hP5uRfqi92X5dSwuLOiU4dFx4zKkyaIRB7\\u002fYlNBs3hlupvK85Wa32uSFejVf5SfeHmtPHwQNCOtdWev3IBLzcs6sXxC8dFdJ4c2gjHV2Vn3i48CP\\u002f2AKgsxt9rL\\u002f9W0zUtaWl81uO5s5J4nfPDLA77+5ePflZy0rWBy4GWKgdJUjz3iB9D\\u002fzV8\\u002f\\u002fJR9ysW7pbGZ706YQmf7qUwgoJhuWFRuWFR92ZNfun8ns1uL6b7Nr8vvCO7z1OOSAAePbm12Nt8JqfRIumNqXmTJfFerzbV6jpOtNV9V3GuSgv\\u002fvXDSWs13Hf71z8V3coP429F3KOHeG5kR4lad7mxLs95q5dIDfFk639KiMpkAABNihs13xSEIx\\u002fCH0pdMikh\\u002fs2RLk9HnMahHZkpGP5m1MoIR+ExxYEgEKOGOIJ0kwSXXmk31A5O+0dhUViJA9VAfVesDFsUCAMCMiEVwKNQGuPPDiIL2MkRuaf246t8am89i5Nnipcuj1+OYl28mQ1YwByWCJJpMtUmcoIo6h74ReMRGQqtbdgIZWnLCorqvrVemag8m4e5KkDml8MPrdCr3VbUud7TZCSJgIrhSo7ASnQsbuPS9uwIdp0SyeJEsnrkPDRNCqF4H\\u002f0GUsrgv5y9eGJsOxV0oGJYTFpUTFnX\\u002fqCmPnth6QlYHbwEAAKDZoPmj7vKa5Fx4IJBLHa1fl5+Bo1dkiyL\\u002fN3Wlr3JRDIAMoSRDKLl\\u002f1NRPio9\\u002fWnLCfQ1YW7cderUaFq9hAk+0MDZjV2OZ8248V7QiYdS8mLTc8OiA+eZYjuCmtLz1aXk\\u002fVZ5\\u002fpWBf94+q0zflZ\\u002fuSLFZZjM6PR7447tvZN7CpXR3GaDjl\\u002flFTbITjf5eOuIIuT53+03X7zoyJ\\u002f8qb7zYIGBTqU2PmtJv0W2ovuccBACqLqUzdPlokheK+DPHXsN2k\\u002f67iHBwFAABwc9r4l\\u002fMXBfwl2VT6W5OWiZncT0tOQEMOknyn6NA3s2+A4j310eVjvha2jecKXxq\\u002faHa0v9KBJF5YEi\\u002fsxtRx2+qLXzy32+vsnwa96t8Fe9+atAwe6AcB\\u002frAhXs1PTgYAGKzWN495+Ty7sxPE68c6i7IXp6R5DiI9lh+WsmHq409lXRvVt9VN88NSP51w35tjbxk62XaAKtwRxI2QFsbAWXC0mxZTHRzqHwPQ\\u002fyGYH8GjCRM4\\u002ff6npMN3rTHSR2gvuzM5DP9X86afbPuK6JtXxtwyvLLtAAApMyaYdkDlOi\\u002f1QcPUCDxiI8iIkhvuJa9dqvZZgejSZtS1GLXukYWxGekCjyY5Foe9JIin8trAPafPCfchLi8idseSe\\u002fxk291FMDnfzblhWfwoeOCKX2sK4VAQ3rh4oHvFrtO0yKRfF9zqK1PsjkmhPp4764tZ1zMpI7Hic7i8hvdlTaZg+KLYjO\\u002fm3Hhw+QOP584aE0Sm2AUD4Oa08T\\u002fOXe+1ghsAUKvruNytMVRP0XHK25OWuWfbXR4YNdW55rAv2aLIZ8bNg6MAAAAez53l9V96MbgpOC5D+TX8qarA6xWabFHk83nzg\\u002f8ln8idNVESD0cBONxa4+uCX5AqNfIPi4\\u002fBUQAAANMik\\u002f5YdIf\\u002fbLsLjmGrErM3L7it+5Qgp801RWfaG+BoPwj8Wb2gOgCHBsk40Vw4NEhWZ4365MyZZp32p0tFKrP5qWnT4wVCeCMAGjTqZ\\u002ffvK2htBQCMi4yaFu\\u002flTYn0FA2nXBs3aVXcxHPK6r1thaeVFe1mDbyRNxjA0nhR08VZi6PHxbE9zvOGiDAu+\\u002fM7r3XdTZMOxV8SQQZMHDu5Sl8MRz1VG4KqF+67BmMVHAq1dksLHOomhTMQ\\u002fa8ajH1dhhrxBe1ldz83fKaw+PzOMFO8ZI5kIGpPQg4DWAp3VKn2AjzgqUxbuDhyLRwdtkbaERtBRhSvheQBG+wCAM52a+CeHRbZYTFCa64WKJq8\\u002fgh3XhPuY7xdCbhqZIdFfj\\u002fnRq+JRV8oGP7GpGuKOlob9F4uZp+XN3ZYjGEMNjzg26n2+pOyejgKAAAghR\\u002f++Yw1PUr+zo5KeWfKioeObYEHrmrD6DUcEx595tpHRD15h3SXL457YfyC58\\u002fuggcAAAAcaanJFvWp68ia5Fxn3\\u002fbuqDi+NnnMO0WdBa\\u002fd\\u002fStvvq\\u002f+MNFs\\u002ftiImAsKOL1e1a2TlX9D+TX8s977mohPjp3jK7\\u002fvFY5hT42ZvWbv9\\u002fAAABuqLkyVJsLRoD1\\u002fbred6Jr\\u002f5DJeHPvNrHUBJ0JB0gQR38xet3zX115Xsn2r8ODmBbfB0VAL\\u002fNne0vQhHBokQyfhzqBQ31qw8Patv9scjh2VFTsqKzIiIjLCO9OjF9taXzi4v0QuL2xrdc734TEYby9c7PEUPXGhtRUODVXjogbopAcD2ITw1AnhqQCAZlNHpbal1tDeblYrLXqTw2Il7BjA6BQqh8oUM\\u002fiRTFEKLzKDHy2kDelVSXEMm56RCEeHiWMN9VtKSy62tbUb9EabzymZG65bOzk2Dgo2aDR7q6tONzeVKxRKk9HicHBp9GgeL0cqXZGROTXO35Wqj86cfvfkcQDA6XvuE7M5AIByhWLD5aJTjY2teh1BkmIOJzNCPCshce3obK9\\u002fX\\u002fvy05368m8HAFgc9i2lJftqasoUcqXRyKBSo7i8aXHx67Kz068cVUamZE5GwPRNq6nRYNdxqD1biaUX6g39m74hAWm06+FoNwPQxtpCmFFxaD9Be9ldqfbCJc1ZOHqFmBG1PHo9HB0+sgXjAybc641VCossghHsPOUhbkQdsRFkpBkdFknBMPdGFgAAKGnuVfcVU0eLItUW08bqi+7BC4qW2wPVcHdPuFMwfFSvsk7DApfG+GzGmh5l253YVPozY+c+cOw3eAAAEoDjbXXLE3yWwHfnqwEFjmH\\u002fm7KyF7\\u002fekrjMNcm5XntAX62G12vYx0yx040p474uO9O9DxUAoKgjcOmJf0t9z+EAACyMTfeVcE\\u002fmh3uty3YZGx7dPeFe7+3alX9D8zVs0Ku8PlsCTzQ9MgmOBpIXETtKJO1+WN7TVG6wWTm0AG23vTrWVuu16lzM5Hw8bXVPs+1OqfyIp8bM+U\\u002fBXngAgAJF81l54wSxl\\u002fxMCAVOuCNeTYmL\\u002f+iaZU\\u002fs3qm3WgEA5QpFuaLz2tclmeySrOudF8Zifbp0RbLI+1W4YFy38Wc4NFTVPPo4HOp\\u002fMaywGFbYbDjc7+zW8w57DUl6qa9ncu6GQ1cvo8322K4de2u8d9pyhwEAHSibtNpn9+891gBf89dYzBqLuVQh\\u002f7X48rzk5A+XLGNSAxysZHqDmM15\\u002f\\u002fTJD0+fcp+yV69W16vVxe2yG7Jz3DYHIBQ\\u002fvS\\u002f\\u002fdqeTjQ1P7d3TrOuabGt1OHQWS4VS8V3hhdvH5j0zY6bX6wQjQRI3A8B\\u002fxGEkIEu1F\\u002fPDZsADIeUgHVV67xUBoWIlzMGspcmjep8WF0Jl2kKC9DLZEOk7tJfd7WrzkgtwmS9dRcV6\\u002fBV06BjNH78ZfBVwd59VHV4SeT0cHZ5G1BEbQUYaFoWWzI+o1Mjdg3W6DrPD7r8497zC4+Iun86M5wp14dHuQQDABc\\u002fNvOrewSZdEOH\\u002fpw9rj+XMDKZFvlfzY9Mj2bw2ow4eAKBY1RZ8wl1pNuxrqoCjAAAAViaM7vXqiP\\u002fInb2trthXi+qrzMh8DXEMuy09\\u002f6Xze+ABACp6WDAOoeL4eHEsHHWTwo\\u002fg0hh6b52750anwiFPmUIJHAJA5u1zNABC\\u002fhpeVHpP08+ITO5drmGaNLF7wt1OEEUdLVN6VeT+cfFxOAQAAODRnJkSVu97L9+SNv6zkhNyswEeAODHygKUcB+6FiSn7Ljp1rdPHNtRWeHwNvGBgmHLMjKfmjo9itfvtTzIQCKJDm3HHXbreXjgihGVcH98905nxplDp98xdtwosYRBodSoVN8XXmzUagAAOIb9d+Hi0WJJvFDA8DwtDmezi9s7D9MsKjVLLEkQCJlUartRf6qpyWC1AgD219S8cHD\\u002fWwsCLCEoN+jfP1X9\\u002fumTAAA2jZYaFs6m0dr0+iatxk4Qc5KS4QeE4qf35d8OANhbU\\u002f23HX\\u002faHA7nM0yIjonkck12+8W21nq1miDJry+clxv07y9ZCj1whEjipFMwqoP0MgXMXYH6eH+nb2oMZRbCBEdDykb4nBvhLpjG0H1UqDkNh5AQQXvZpcXU4KfpB45RxggnwtFhRUATxXNS6w2V8ICnE4p98yQrB2CPD4ARdcRGkBFoTHgUlHAnSLJCI\\u002ffTCsZgs5Z7VsE71x7MFIoZFKr7HP8mg0ZuNoiZPuciNxs0GqsZCuZcvf1kpCzuzWl5cDRoFAxbGJvxvbfC6grPnejf3uZKaFqDy71Zk+FQ0CQs7orE0f1UoD3UjNjXcJqPouk2z0UdeiqNH0H32\\u002fwEx7A0QUT3QnUAQH6g1KqU5SVrp\\u002fCWqB0YoX0NK32k6cd0uwIapLER3qfkFipbe5Fwr9EqT3srb49m83ux1LM7Ko6vTRnzSTG8yisAYH9zhZVw+H9H9RGcAEJ6JJbPf3\\u002fxNf+aOetkY2NhW5vcaNBZLCwaLZzNHiONnB6fEMnt\\u002faUYZMgyat+wW8\\u002fTGLPpzAUYLoSHR5LTzU17qqsAAGwabcdNt8TxOwsz5ySBm3LHrPn15xK5nCDJJq12VWaWxyMBAACwqNQ7x40vUcjXjho9NS6eTuk62Okslif27NpXUw0A2FJa8viUqZFcL38CXTaVFO+uqozkcp+ZMWtJapqrnFxvte6rqU4N87IQTR9\\u002feh\\u002f\\u002f7U1a7RO7dzqz7Tfl5D49fSaX3jX36o+y0mf27bU47NsryifFxq3P6dOfmWGKgbPSeKPLtIXwgKf\\u002fZ++sw9u4sj58BsRgyUwxU2zHYeakgTbYJm3KzAz7td3ytrvb7XbLlDJT2iRN0yRNw8zgOHGcmJkt5oHvDzny6EqyZVvmeZ88eTTn3JE8VzOjO7977jkXDHl6h1YpUKGOwHGiZT9qCjR+BvPaGS\\u002fxGgFE79Ce0R5FrTwBgv+WXVxst15oqCjCnwqc\\u002fZwpIZd1KLgbKf3B5u2zwgbDrOqQumPz8AxBRgRHeap7BZqGdgT3k83ViNSYpY6E1lQwEYgidqqpup3SoJ5xlACQE9xFnaj\\u002fsyp5VKdSKnviS0TzGvbui20+QrNHBEele4sF9p9l8f1aLA4gQ7YPk5UhQUKx5zyZlaY6XBnTDol+VJeN9KabA8Dwjjo81Nucn9FhR029RWD7sNaHTD\\u002fMR1nRDvG1BAcplO0na0vzUBMAAFyXMrqbN0MAWByX6VVwN1OOQ\\u002fXlM6O8hGYGilZNiqc7hEllS9Mznp85693LF325\\u002fKoPFy15ZfbclZlZvNo+WLHb9guEE5Uh34llt4gkyzz\\u002foTsMXrYWtaoJN+bcv9\\u002f2AAEAAElEQVSMdCnOTsQk+eikKc7XrmaePDBh4gdXLJ6VkMjVuwFAIRK9vfAKtVgMAAzLHqpCU0Ai\\u002fFlUGCGX\\u002f3btDUvS0rnJW+RC4fKM4dnh3n9fu\\u002fPp3Tz21w\\u002fudyakuiI17ZU5l3HVdgBYnjH8\\u002f6ZOc75+78hhpy4\\u002fBMkJ6jjKlWHpfU3eS8oEBDtjO609jFoDjRB3OwF8YfIjA3h32NO4ucMAVZ4uw3\\u002fLLiotpaiJg3jgq+0AMEY1RUp0PA7cVrfOQHnJTTcQGTp3bB6eIcgIb8K6Z5oXLic8EriPuJRAw1Ombz+rTL63D8oZvBHuS+KzUFMn8aUt+h+uy7CsZwp+JwuHpaOmTjIxPL4LucsHHEO8D73q1wBgpf1a8ekVf9Rhr+lHBDgR09G+SqEYNQFYaIf3FQq9QgD7UO8h3DvxetT+4GtHzxkCf\\u002fir6gJqAgCAOTGpqKnzpAaFSQjvF8uxRi9h9QFkYAjuOIaHi9urb8DD05uwTB0p6voSsMFEiUbjfDE8NMzdAwAwPKzVWKptbdYppALBzITWhVS1ho7DMZ6fMStc5v03qQt0+OndOfZGs2lzYWu8w5OXhHWE60bkSAUCAKg3GQ9U9uwvQb9lRNB4HOt4Tvtg0zYz7e\\u002fzQ2c50LStF7IT4BghJTo+e\\u002fWUl9MpUDTbG\\u002fY2bkGtPIGD\\u002f5ZdGNuVmDV274teBxYkLpgUMhu1emCmTWurvkCtA5Ohc8fm4RmCZKojPMsRFbRbN\\u002fV4I6qhOyPcASDHI\\u002f76pLcUEC48I9yFOJER5F1THujEyoKS\\u002fQjjbR+V0PvUtcVvna5Q12jwlggbALqQLwKBxPGxobGoddAxxPtQJfJ+ElLesjH7SZg3MR3Ba+h3lFSBd1QXzVckNdV3ufID2IfcLF5cfCnRHeK1nwHAawL99qkzG4r1zagVIFKq8DV32CkIDMsMjkCtAACQ11yLmgIK+qvZf4iRpIwPXrAs5r57kv\\u002f7XOaPD6W+g7bg6QfgGJakVi9JT39q2vRvrlqBugcpOBHL0DWodUjiirwWC7zcqV13YZrp4sRwtKJ1pZLR3sHoMEgkXpASgPlPLu1\\u002feneO\\u002fVBlpbPwQ3poaFyQCnUDQGta+VbV\\u002fmg1+sQyRJCTyhFB41CrB2ba9GftGtQaCGyMZWfD76i1Z1ALvczcIJQYvU\\u002f+dx8W2DWVn1Csl1OdJ4Dw37ITivE+6HdioHQXDN4Xlg4sZocvERPen5S45GqP7GncjFoHIEPqjs3DM9QQ4kR6EPoT1o7gTrMsUqNPSgoTFMHO154R7nkttTTrU0LyFNyHe5sAGByMCYSKqhB4rw7iS3Tz5JxHnzsR4ET2pYmT7pCp9q5\\u002fDSaGeB+KcO+CrJdnY78JEUlRkweehdPAd6g4F6GPW4qj8+p2oAhgH4p9rIcwUl3MmeNrMslr\\u002f7fP4YZy1AQAPlZWdQ1fCXC8Cv0BpOO+uD\\u002f1TdTUJViWpVmHg7GZaYOR0mrsDQ22yhpzkZn2EjoKACNVMyeHLkat\\u002fYO15\\u002fMvT0l1Bp\\u002f2As\\u002fNnIWa2kWA43KhUC2WBEslIRLpkeqqv231vnpXQpLJwSExSmW4TKYUiWQCAYkTOI4ByxrsdoPNprfZagyGC81NzWYzujOAVCD4ddW1Gd7Cewc3YumNZsP\\u002faNndhGA46htixAYFQVUlAJS0tEAy6i3WtDhfxCq93+A6RND2s9fBz0p2RHiHs9adpf1P786x59bVOV+kh4S6e9wIl7XO4Vd7C7EfIkwLXZCrPYJaPTjQvG2MemqCLA11dI\\u002ffq783Ul3JQ9cFwkVR1ZYy1OpOnbXSSOnlpJeTqptsrVt70XAWtfIEGv5bdiLpKNL\\u002f58qPH0l9JUigRh0DCjmpnBO+dHPtz6jDg99rvgsWho0IGo86BhpD547N030evmraw1d5X+TH0z8ZERyFCIg6u7XWrI\\u002fyJmSc19Sb3XWcTHWEa6yepAyRC0TcQEgrTZ3XNGRfyjnDRW+3VpvQdVGekv2gIctbJ3QWMeld52F9FPD0pMTgXYdKUYYEZKoj1WP+ZvDB92HAUQi8pzHhQnoLVPcVKs6FwHx9Kf5eNf2ZIB8ZYPT2Lq4L1Nq876jy8UHt4Dml6iRBHrAHAaWPM6feYmRYNuA6kgvvN2IuUeLWpAo9RK219Lzu8EnNTp3DbQXx5trPS01nrx72uMC\\u002fnKe9yf\\u002f99eeLu3ZcnpK6IjNrYuywnvpyLnH76DGoyW++On3qX3t2I8YxUdGL0tKmxsWlBIf4eW61WCwHKso3FxbuKSu1Uq0T42aHY9Uva965\\u002fIpZlzJvDBGE0qvstm26pkVCyVJSkI3hoQBu3Th00rgvTE755dxZAPg699QNOSOVIrdgig+Otj51X5bkIUhzYAFy62oPVFRcaG6q1uu1NqvRZrfRlI2i7X5HYQRLOp7u9kqXP707x95obl1N\\u002f\\u002fuFgt8vFLg7vaCzdiUV2uAgRZ4ZJY6rtVagDncYlvmm\\u002fN0n0l6Vkd7r5HSBPN2xQ807UGuPESdLOaU9hFrdYYE92Lx9fsRVqKN7HGresbXuV9TK0wPw37KTMFEHaoLG3vT6hSeXRt8wRj3Vz2Kz\\u002fZNZYYsONm3TOlqnYH3BsMxXZW\\u002ffEHf\\u002fGPVU1NfD1FmrhLgo2I+1F\\u002f4wdO7YPDxDkBHBUT8Vn0aM5zUNXgX3Yx6pq7khvRjAiODIQ\\u002fVuUY0nm6q8Cu4+ErijSWkGDQHUmLpDmcF7gjvXMoVu0mFC7UEA34cBRyboojYoI7u446Ah3seNpUDb2LUER+d9rHCK8\\u002fFB7eBrsVSgrhQAUAi9L\\u002fqhWUZjM4f4sQCia3QsuPc0UeLEKHHirPBrTmp2\\u002fFX3rYU2ulzn9Ue+LvvHzQnPC3Hv0xF9iNnhWHs+f+35\\u002fFil8qrhmVcNz4oL6nf3uy9PnXzFXW2fPGzY36fP9FVAsh2CJZIl6RlL0jMaTKb\\u002fHdy\\u002fLj+fYVkAMNhsd\\u002f++4b0rFi9ISUH3Gbxo6kY5X9jMv9rAi34xdAT3WYlJ46JjjtdUN5hMV3z\\u002fzX3jJw4PDRWTgnKt5ovTJ0\\u002fU1ABAhEx+9zifUXtbiwpfO7C\\u002fzFui807hawlY+3Tn07tz7Aab9xVYvuhCmrbBxOVRV39R+gZq9UBjb\\u002fqk5D\\u002f3JT\\u002fnTw6HDikxFXxX\\u002fj7bixENSbJ01OSNPQ2bZoReHpBjdLK38c\\u002ffqr9GrTw9A\\u002f8tO0mQpUHjJtTqjoky\\u002fFix+vea74crRiXK08NF0WphqJSQCXDhAJLgBbhwVdw9nxT\\u002fp8ObCcPS35a\\u002fV2kuWRx9PeFHJvRuYmdsZ3RHDzXtKDEV3Jn0ZKAEdxgyd2weniHICG9FSgu0DXNivDwGnvAogooEbucERyGC+6nmmpu525fwGvw4iCPcvU5g9D4NljZZhkt0gETefnKYPcog60OTw15qaKk0aesthkaLSe+wGh12M2W3Ug4rTTn\\u002f2ehLrymHvQdSn\\u002fvKG94hXchz0hP0YR9m+7hnnmiqui3di17RIb4Kb7iKY\\u002ftPhVGLmgAA4NljW5491uOlpyztBll2k35x2gEAjhHjguenKcZ+Vfpio63tmys35a+tfOfa+Ccx9wjiviUlOLiopTVYqUqvf\\u002ffI4feOHB4fE7syM+uK1LReSzXTPgcqKv61dw\\u002fXcvuYsc\\u002fOmNnNfgyXyf47b8HshMSHt2x2JqGmGObRPzf\\u002fcs2q7PB+nUQsgMiCXkJNQxUM4OPFS2\\u002fbsP5MfV2NwfD8zu1Ig0S1evXipWqx9zmzj44dff3gfudrtVg8IyExMywsRqEMEomlQoGEJL88fcoZRd4TdPPTu3Pskkt3iREREaMivf\\u002f4cUlWB2x2dyAyImh8oiyt1NRaZrYdKszFHxa\\u002fcnvi31SCbvXYae2h7ys+pJheTXU9TJocJAjWdRQJa6ZNP1R8eFvi493\\u002fWbQztvXVXx9u3ok6eHoM\\u002flt2Mlw5SoSLbUzHa3dMlOG4Zt9xzT7U4QcERpKYQIgL5WSQQhAULAyLFMdGioclyFJEeABEXj\\u002fJUIycFjp\\u002fX9NW1OGN3Y2bLhjOLI+5JU2RjfoCgY2xXjTkndIcPKc\\u002faWc6N\\u002fXrJ0Pkjj0QWbPmyOqPd7344vKZMzJQH4fffjvx7nvbnK9xHNu+7Sl3v3f2H7j4wgvr7r1n9jXXTER9A5muHVen+vAfL\\u002f92+nT5Z5\\u002feERLSmkiw35IRFC4iSCQJ+Hlv4ecAcMJLhLvbo6JniPopD43eiafgLiWF3S8r2m8JFndx2W5gaba2rsdF8CeJtj\\u002f4SnAxmBgEfVioa9pXV3KisSq3uabGrEfdvU6XdXPSZ7qYHqef9OH4sFgxQVo9xOWd1YUam0XtR8odLnq7dUe1l5GeiCBHd74KRaOPqanewep3Keku0MXztYdQCkJuS3z5s5LnWuy1LmO+\\u002fvCR5s2TQhZxGvYxf91069mG+vXnz\\u002f9+scCZ3JwFOFpddbS66sVdOxampK3MzJw0LK67T8ndwOxwPLF1izMI3cnto8c8N2Mmp0m3uDw17TWKcqWGt1HUw5s3\\u002f3nTzUIikDFZW2tPlRgbpoZlZAfF+Zn6pncQy+5ETUMYtUTyw4qrl\\u002f34fbGmRUSQFMvgGKYSiYeHhS1MSV2eMdxXDsGCpsY3Dh1wvr511Ognp073bInkaQkgAfn0Lh97iLR1mDUyIvIfs+a4O3m8sDT6xncLX\\u002fQnerHSXPLGhadXxt4xUtWJZ2MXBkq3ofrbE5rWmZjeBANslGqSP7UT83TH1lZ9cVXMrXg3wmAL9Llrq79osqEPsTw9Cv8tOxHioulhC7fX\\u002f4Y6AgrNUjRL2RiLgdLVcrR9HMNjJYkjgsaPVk8JEXZ6zV8XWBJ9Q5HxfIeJVpzUWis\\u002fKv5nqjxrRtjlWUFjuz\\u002fpQrN0taW02FhQYDhdbCygWfRZK+AMhTv2IGbWrOFxcSF6vfWTT3c1NPSZNDCg6VwfsiAWC3G8u1d6L0DieIYqPNe9FKrXVACVRm29u4AiIsiUoFCuxTNEvcKobbaaPJf2ewru2cGR\\u002ferZMLD0k9wXeh8VEf3Jhe0PCoEIGxy5sX0zcPuw2qT7pSR3fdnZSh+hx30F0dULv\\u002ffvGP2tD6WkcHZ0ypZKNJmtlaY+v3DkbzmzEHv7fHXxuJnyolPPiU6R+qjO6gujw2bpScm7Q7iqacDxLgb1IQpB8Kq4v60u+ht3mL6j\\u002fscRQdNlPVBArMtkh0dkh0c8M33Gvory9efzt5UUOzObWyhqfUH++oL8GIXyquGZKzIz44JU6M49z8fHjzWY2iZUhymD\\u002fjZ1GscfAK4anvlTXt7xmtblCGVazRenTt7rLXtGl1lbeThPW\\u002f5N6a4ggXRyaPqUsIzJoWlyMjC\\u002fTwMFloWLdY0Xapu0ZovObDVYbQwT4DvCc8u7JfhaKeqmdb8Wa1qywsI\\u002fX3ZluAwdJfvi9wsXnHe37PDw52fO9vob2HO5ywPy6V0+9pERkd9DLgDkNzaiPh5vJMjSZoRd7o9MCQBGSv9V2VvJ8sw54UuGK0f5KVdVW8qONO863LLLwXSxVnv3mRY6f2\\u002fjFn9UqgNN26osZTfEPdBhLmwEFtgCfe7Oht+LjPmojwOJC\\u002fhw0R6C\\u002f5adXBaxPFd7uNFWhzp6HoZlKszFFebiTbU\\u002fpSty5oQvSVOMQBsFFAEuvDv5qbcvPqdzaFCfDwqN5wqN5+SkMjtobIZiVLws1f8wcBNlaLLX11oqa63l1ZbySnNJDwWz+2KI3LEHKyqVdMyYBAD46efDHYvFPN7oVB+++OJy1NSPGREchQjuZYYWK00heR685JNRRyIFCWNkQaFiWZN7CPCp5prLYlK5FgdDF+mbuBbwJtYPJgTeSj72PshSBheSroYYI+AYRuKEI3AZM\\u002fohA7EPK43at\\u002fL2bizPp9n+mNEU63XdvAv02z68JW2cp+AOAB\\u002fnH5oakTg5Ih51+OBUU\\u002fX757zHOnQhO43Jm3A\\u002faAjMpR5YoiVJOaoZudo9LouVNh1u\\u002fmNuxPWcVv0CAsdnJSTOSkg02e1bigrXn88\\u002fUl3lVPGqDfr3jh5+7+jhcdExKzOzFqWmyYS9NFltp+nvzuRyLQ9Pmuwr0rY7PDBh4m2\\u002frXNtfnXq5J1jxgak4jYAaO2ms9rWWDCdw\\u002fxn7ak\\u002fa0\\u002fdmDDzgbTL3Rv2GbSjwGr+nnLksowew5WkIEckXUUKAvbQfrq89qt9J44WV+rMHci+3aSbgvsXp06cqqsFgLcvv8J\\u002fxRkAqvQ654tx0TG+fjmP17iN6QNIQD69y8c+LS6ewDCaZXPrait02j6ZlhtwLI66rsCQW2\\u002f1ni3Ok2JjfrExXyUIHq4cnSLPjBDHhAgjRITYqebQLGWk9C32pgZbdbmpqMh4rn3hD8eIB1Ne\\u002fK366wpzMeoLHKGiyJGqSac7KqrppNxU+J+Cx7OUY6eEXJYizyTx9qIJ7Iytwlx0TncyV3dEY0cfXBHmRixLkKZ9Xvo66uAJBPy37ESEi29LfOKDopdNlAH19SIXDGcuGM6kK0ZcFXNbuBjNbxBAVIKQu5Oefq\\u002foJSttQX2+MVL6w827DjfvAgA5qQwVRaoFIQpBkAATCnAhAFAsRbGUnbGZKYOJMhgoncbe5E+unp5mKNyxByh+Tmnw8HjFU+mmWbZQ1zjC3X6iERXcx4bGIBYAGB0as63KLSnByaYqRHAv1DV5ljLK8ZZNftDQ5RjewGL3IRaTgZsPEARaLO5vDKw+ZAE+OX\\u002f4nby9nllH+g\\u002f9\\u002fCesn\\u002ffhhPC4KREJB+vLEDvNsnfv++V\\u002fE5csGJaOuDzZW1vy8MHfPG\\u002fLADArOnlc2DDU2hG+5qUGB4EXYQPC9LCruII7ABxv2TY7fFV3Flb3KDKhcGVm1srMrDqj4beCgg0Xzl9oan3WPV5Tfbym+h+7dy5ISX1zQW+IxXvLyzSWtsc5AsPmJiVx\\u002fAFjUmysmCSdof0A0GAy7S8vn5WY6N6qi+RqyzwDAK+IHotY+gqL8WOz\\u002ft8Azp83HICh7Cetpq+liscliseQxp2lVmt4ce22AxfLUUe\\u002fZH9FBQCQOJ6gUqO+dnFNAjnzMnmy7nx+18qZ+kNAPr3Lxx4ply9Oz9hQcJ5m2Wd2bP9q+VWBmqkaxJC44JaER965+KKN6YRcpXW0HGrecah5h3MTA4zEBTRLMZ2MOFgSdV2iLC1aEtfT8s3S6BvO6U\\u002f4GbPJsEye7lie7hiBEVHiuBhJgoyUiwmpCBfTLO1g7EZar7U3N9pqG2w1fh7yOPX0RVHXWmgzBpjnTZgnIPDfspMo8bAHU178ovR\\u002f7YunvcAFQ97rF55aHnPz1NB5qC9wREvi7056+tOS\\u002f1po70ld28dI6Y2Uvgw191OGyB17IIIT2HffH9yy+UxjkyEyQrl4yeirV07orL7HsrBu3bENv5+qq9OFhSouvzwnORnNzsSy7KZNuRs3nqqu0ZjNbre7nTue5m76wmy2rVj5XkxM8Gef3o64brvt0\\u002foG\\u002fbq1D4vFAgAoKKjd8ueZM2cq6+t1LMvGxATPn5999crxrljItWuPffDhjnVrH962\\u002fezGjafr63WhIYoxYxPuvGNmUFDb2ll\\u002fjqvDz\\u002fKTnbvO\\u002f\\u002fOfG1ybv294VC73ko552\\u002fZzGzeeqqxstlgcarUsOTl83rwsZxb+lhbTyqvfe+ftGzZsOHngYNG4cQnPP7fs11+P\\u002ffDj4fBwxdNPLU5L69zqKH\\u002fwqnRf1DUhgvsxjwTuY72pMKNDUMH9lEcVPs98MuBN9+cJOL4GB5070duln0wt9BwDqA8ttOOhA+t3VhehjnYhcVwllCgEIrlAJCUFElIgI4USUiAlhVJSsKY4t8Xm\\u002fUF7UDIg+vDFsfOXbf3Ccz7A5LDft3\\u002ftvNi0OzMmjg2N9ZqB52xL3acFhzdVnPeag0UhEL08biFq9YP+tg4gsPRTwT1CHKcUhOgdzS6LkdJWmi\\u002fEyzI5rfojkXLFvePG3ztufKlG82dR4dbiorz6OhbAQlG\\u002fFZzvHcF9X7mbUJsdEaHyVrax+4hIclRk1OGqthHVnvKyQAnuZ7RliCVFEZUoR0e9fYLdus2sf4UUjpcqHiUFIzBczTIaynHGbHjHbHiDINOEkq6XHCioabz3i\\u002fWNhq48jfcJTuWaYph\\u002f791z2+gxUXI54Z92PCI84tf8cwCwo7SksLk5NaSt9hHDsmvOnX1pdw9W+QvIp3f52AHgySnT9pWXtVgsBysrrv31579Pmzk22i240kJRJ2tqdpaWGOy2\\u002f85bwHUNWaLEcbckPPJZ6X87K764YIH1U+XkMlI1cVb4YgCIEvu71K7LqIWhi6Ku\\u002fa36G9TRLjRLV1lKqyylqKOTpCtGXBt3LwaYlJBFiof5mXKap7Pw37KLSHHsE+n\\u002f2VK7Zn\\u002fTX72QW7wdKNbxa9Xn1Zayq4fd2XPxU4my9AdTXvy45N96hxb1DTqGwh17IPL11\\u002ft1OsvUqalCAXng4MXVq3dqNKZ77p6NtmuXz7\\u002fY88MPh+LjQq69dqJOZ1n\\u002f24ngYHSd38cf71rzy9HL5mZdf\\u002f1kvd7y40+H6+p09947Z+IEf8OApFLR1KlpO3fml5Q2JiWGueyFhXXlFc3z52c71XYA+PnnI8eOl4wblzh1aipFMQcOXFy9eqfFbL\\u002fllmmuvQDg5Zd\\u002fK7hQO3NGxqxZGWfOVG7adLq0tPH9925yNfDnuPz8rA4ZPy7xzTeu0+ks33x7oLTUe4LBn34+\\u002fMknu9PSIhcvGoVhWE2N5viJsvi4EG7Z2w8\\u002f2hETEzx6dNyBA4WvvbappKRhyZJRv\\u002f128o03tnz88W2cNwsMycpQKSlAUvcW6dz+fqPDVuhuAR8R7mM8jHkttTTLcjVEzxzxKqEkTt65SBeeLiAkvIdOU129pQ9BBkof2hn6jt1rDje4iUieSAjB2LDYnJCoDFV4gjw4RqZUiaTtDJh2VBcFVizuzwyUPkwNCn1m9NwXjm9FHQAAsK3q4raqiwqBaHRoTLRUGSQU4xiut1vrLYaTTdXt\\u002fCUEhr09ZVmsLAh1+EE7hXDnxKQoBD2iZHJR9uRH+Dy2PidFPuqkpjXCxUmRMbf\\u002fC+4uEtXq+8ZPuG\\u002f8hLMN9W8cPLCnvAxt0WOcd88KHSVXcDcDS5h7Go1zDeiQqMuUm9CB2uTQNMTSV1iNqwkyURnyPYZJnRYMDxaIZimFE3WNCyymz7osuFe16G79+BeDtVdzrXaTa7Kyd5eVAsCXp09+efok1yUmyTCpLCci4uqs7BnxCVwXACzLGP724YMaq9XscCz58bvLU9OS1cEkjlXp9XvLy6r0eplQ+MCEiW8dOojsGBAC8uldPnYAiFIoPlq09M6NvxlstpO1tVf\\u002f8pNaIklUqSQCgcVBNZpM1Qa9c\\u002fZ4dqK\\u002fj6ZDgeHKUStj7\\u002fil8rNeC8vNUI68Kf4h5+toiZf4rIAzM+yKYuP5PN0x1NHDZCnH3JLwKHFpJVmyPKNvpdjBDf8tO2GBrbFUEBgpJ5U6Rwvq7nUONe+gWOr6uPtQR+CIlsQ9nPry5yX\\u002f69ue7x2Gwh17wGHQWz\\u002f5+DaVSgoAN9005b77v16z5ujSJaOjolRoUx\\u002fU1+t++ulwXFzI6tW3ikQCALj5pqn33Pslt43dTv224WRqauQzzyxxWhISwx555LvmJmN8fCi3ZfvMn5+9c2f+9m3n7r57lsu4bfs5AJg\\u002fL9tleeSR+RKJwPnHAMDNN0258aaPN2\\u002fJRUTwc\\u002fnVH314S1JSa\\u002fjO00+vOXqsJD+\\u002fOjMzBvw7LvD7szpEoRCPGhUPAJu35PoS3Ldvzw8Kknzw\\u002fs0E0RrPQdOMw+Gm35Ek8dyzSx0OevmV7+zaff777++NilQZjbbNm0\\u002fbbJRIFOCHfQLDhqsjkIwxhe451s9q6pALPl6h9iyFCgAjQqIIDOeGN5opx0Vd43BVW4jVeQ\\u002fBfURw4CP3eTwR4qQJvEx5BjD\\u002fg92bGD2YGCh9+I8Tf7WjFOMYdllM6tVJI2dEJfWTAgP9kAHUhzemjq23GD84dwB1XMLgsO2tLUGtviFx\\u002fM3JS2dHp6AO\\u002f0BKgHB5dMSMbPXAvuH7G4zZ+4SJYhFLnbUMsfRnyrSaD44eWfLDd0t\\u002f\\u002fL431XYAqNBpuZsh0lZRuCcIlrQtwwSAcveP7g6V5rb1DU7GBnfxGg44lCNPIJrtUttdYJhEIJpNO84hdv95ds3WXlPbo1SKK8dl\\u002ffe67q66mJWQuCS9LdCGi5WiKvW6TYUXb\\u002f1t3ZPbtiKLj5Qi0erFy4JEYgCw0\\u002fSGgvNvHjrw3wP7f8g7U6XXR8oVXy+\\u002f6vZRY7wuaOo+Afn0Lh+7k\\u002fExMetXXT86snVVrMZiOVlbe6Ci4mRtTaVe59olUiZv24cHYHLI3BviH+idDGOp8qzbE54gsNZxQJQ4zt3fU9wY\\u002f2CyfDhq7UnGqqfdnviEMyu0k17+A4YgQ\\u002fxb1tib1ld\\u002f\\u002fcLZe94tfGFnw+\\u002f9QW13cqxlz7b69ag1oIQIwx9P+1ePpq\\u002fpPwyFO\\u002fbAYu7cTKfaDgByuXjJ4lEsy+4\\u002f4JbWo30OHChkGHbpktEu0TkkRD6PI38DQHOz0W6nUlMiXJbkpHAAqKnVtDXyg3FjE0NC5Dt2nmMvDYoYht2163x4mHL06HhXM5VK6vpjAEAqFSUlhTc1GVx7OZk9e7hLbQeAyZNTAKCyqvXm489xgd+fFRDUaqnJZMs\\u002fX+OyEATuiut3kp4eBQACAREVGRQepoyKVAFARLiSZcFg6ERCJ\\u002f\\u002fxTOdSpHMT3PNa0Cxh40LR53onEkLA1dadIEVZL3gI7jkhbktCeXoIhUCEmgAAQGe3oqYuwQZUd+6fDIg+PFhf9mPRKdR6iRHBUZsW3rF6+sq5Mal9rhT3WwZcHz6RM\\u002fO5MZcFJKVtqFj23ezrF8d1PTBaIRD5ElyMjl5SxnoOn5MJfY6UROOym23ViKUf0mw2b7x4YX1Bfl59PdcuIIi5vRWmarC5nZeBSuzlFURG1Lt\\u002fdHfQ2U2IJUXer2a3fPUq5jtdWwfsLSg9XtrBSY5jmFQkcK5275o0LxMJH79i2uSU+PhQFerrPN+dyX3z0AGt1UrgeHxQUIRcIbh043YwjMFmK9VqTHY7APyafy4rPPyWkaO5u4+Pidl60y1fnT65u6ysQqd1MIxKJE5Qq+YkJt04YqSzznBaSGhBk\\u002ffAn27SzU\\u002fv5rE7SVKr16667kBFxZ\\u002fFhceqqxtMRoPNJiLJUKk0SR08Ljp6bmJyemgnYsGGCGPV02SE4tvy98y0EfUFjskhc1fE3ubSbgBARiqUAlUv5IIQ4qK7kp78ovTNi4Y81BdoMMDmR65YELkCSaORLOszKXaIMGS\\u002f5Vprxbb633K1h7ucaaSn2VK7Jk2eHS9LRR2Bg8QFK2PvSFfkrKv6UttvJht6iEF\\u002fxx5YDItry6EHAAmJYQBQUY6GubRDeUUzACDJzRMTwribarWMJPHqmjZ53fk6OLhzMQQ4js2dm7VmzZHc3ApnPPjJk2XNzcbrr5\\u002fMzZlus1FbtuQePlJcVdViMFitVoczDJxhWIJoa8adAAAAhVICAEZDq\\u002fLlz3GB358VEG67dcaTT\\u002f30yCPfjRoVf\\u002fnCETNmZHhGrCsuZX4XiQX4pUB4oZAAAIrqkXusp+BeZdJZacoVpZjXXOvuhzE+BHcAGBUac1bjJtCfbq6+NnmU87XGZvZMYoDki+fpIULF0gqjlxkyz2+ka+jsPTIh1K8YEH342uldqOkSC2LT35qyrJ0AZB4nA7EPb0+fMC5s2Msntp1sqkJ9\\u002fkFg+DXJI58cOTtI2K2ULAKcCBXLGq2o+gcADZYeHDf2Dv3ui3chJVDB3UjpEEv\\u002fwUZT24qL1xec31teRrtX7M0Oj1iZmbU0PaOHEql7Ynf\\u002fA+pNPXiaNri\\u002fOUUHYE2TEzPtpibLSFGICD0l+gpCMNxh282yVgxz+05Z1uKw7SIEaCyMn\\u002fx4KBc1AQCASipeMWHEpJRhmTERSonIFXM98pl3KLr1u145IfuFK+c6XztoRmOyaEyW\\u002fOqGw0UV+y6UGq1ta9lMNnt5k3bVxJEuS5f5+MSx1\\u002fbvA4BZCYn\\u002fnHNZtMLLF0QzzOsH939y4jgArDl71lN0DpfJnpw6\\u002fcmp0xG7i8033ISaODw4YeKDEyaiVr\\u002fp8qcH5NhdTI2LmxrHB+J1jgzlyP\\u002fLeO278veLjedRX7cR4qLlMTdPDmm9prhEi+N7R74R4ZJ7kp7eUPPt3sY\\u002fUV\\u002fgCBIE3xT\\u002fYLLcS1SCQqAKE0X2eTXLwc1Q+5Zpltpat3ZHw+8MG7DRQk\\u002fAAvtz5Sd\\u002fS38NxwIQ+9MOI4LGpytydjRs2NXwRxeSlXefaEn8tND5afIujlv8Z9DfsQcQYk50NgAEKaUAgBQ1bR+L2Q4AEknbWhkAkErdNsViwaJFozZsOPnBhzsmT0o2GKzffHtAJCKXLRvDbeYPC+Znr1lzZNv2c07B3TOfjNFoffiR78rKmiZPTlm5YnxomEImFX3y6a4LF9A7m1rtJauJC3+Oy\\u002f\\u002fPCgiZmdFff3X3jz8d3ro17\\u002fTp8vc\\u002f2H7ddZNWXTORO9ngyjYDADgeYMXfK556N8OyxfrmLHXrfMaZFlRwHxfmU3AfExrzXeEJriWXo9cX6b1MBXkt3MoTcMLE3qfHas0G1NQlmq2BEZ37M\\u002f2\\u002fD3Oba\\u002fI8LlgnGarwd6cu705EtpnqxC\\u002fLwGXg9mFOcNR3c66\\u002fbfdPRxo6l+QwVCxblpB9c+rYYXIV6usS0bIgr4J7tan\\u002fKsB+0n8Fd8zjIcfOBGbpTQBhAY5WV60\\u002fn7+lqBCJKw+TypZnDF+ZmcUtxtg7yAQCbqR5qUbb5gs0JRq3OVuJwG0QH0AUAilq6jvEsluNmocMzTdKFI+TwhEYpmAZA+XItRjeoqliRfDf0R38wGCxHSwsR60A104a+X+LZ4gFXi5VEUlSdOst2GKnXOU6CRyPUimiVIrMmPCVE7L1FusXe058u\\u002f+k1dG64uybfSc1RsurqxZ2Z\\u002fGDhaLeO3IYAIJE4vcuX+SMB\\u002feEwPGHJ07+9MRxFqBU62WGfyAylI+9X6EShDyQ8sLBpu1\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FDXz3G8fcvXv7kz03781GrN2aMSX790WWoNdDUNOre+WFPbmENRdFp8eEPXzcjI6GvRTieHmZgXTL9lhJTwQnNftTqztXD7uxltd3JnPAlB5u26xze795OKszFqImHZ8ByzTUTr7lmIgDceOOUG2+cgroBACAuLmTnjqdRqwcY1vZuLrg7vvHmlpYW4\\u002fff3SuRCF1Gmmauvub9PXsvdEFwnzEjvZ0\\u002fbMKEpAkT3IJ+khLDFi5oW1y4YsX4FStQbWvO7OFIfvkOjwv8+Cx\\u002f+jAoSNJhGwyDe+6efc\\u002fdPsNigoNl3Df54P2bXa89jyKwxMlVKqFEa297eNHYLCaHXUAQSEz6cHW4lOwgQcHIkGgCw2hOIFehrpFh2UqjlnbP\\u002fTVMrlKLurIwl6drzI1J8SoWH2uoqDBq4uRdf\\u002fD\\u002fvewcahqk9Oc+bEcUojwCKzvFeW3Dofoy1DoYGYh9uLa0LarViVwgmhKRgBh7mWuTR3kV3KtMul9LzqxKHoU6Bgg+z48+pNiYe6R5E2oFiJb0lzivG3JG9lu13cljk6d4irV2mn5p187ZX33x\\u002fZncZnN7i7u9orNZfz13bv43Xz2zfZun2g4Aj07yPnbvGnGysGyVW+bNg00XuJt9SHNNgs3sfYWySfe0vmkFavUDg7Wtzi0ARKoU3E2vcFVyZPd2yI6NiAtRuTaNVvvBwvI2d2fAAB6fPHX7zbc9OmnK5GFxkXK5mCRxDJMKBGFS2ejIqKuzst9ZeMWB2++6f\\u002fzE9tPjDDiG8rEPNRiG\\u002fb+3f999okijNxvMthPnKx99fZ3R7O8Vx8MzlNnV8Adqcideljox2Kec1KOQmCBTORq1umOhTWYaDQLi4eHpkPz86rTUSK7aDgAEgRMEbjLxP6CDgREekeaVJu1FXSMikY8L7TijqZQUpLvX6DNTjkqjtsyg4RrBW2Q9T4+yMDbD6zMMC\\u002fDp+SOo1W9olvmp+BRqHaT05z4UEaTIR1Bzs0cEdKd4O29vt8TmgcOA68MGi7HWI3OLSijuc70iQxU+Lsz778V\\u002fc3dzC2sPLLyfHH3IRcOJNRVv0iyNOgCSFSNRE48PRkZGXp8z0rOoKQBU6\\u002fXP79zxws4d2RERU4bFJauDE9SquCCVXCiUCASu68xKUSa7vdqgL9VoSjSaI1VVJ2prnOUfvbIiMzOACdyd3JY094mTX7o2DzddLDTUpir69UgLw0MoqnXdaKewU27nfJDELT2lV2SitscYg8Vmp2ghp4xqO0xIjq1o1ro2T5ZWz8ns+mxWXFDQwxMnodahwVA+9qFDaXVzUaVbQgyNwXLifOXMsSlcIw8PD0KTre6c7gRqdeeKyFWoqRdJV+Qcat6BWt3R2pukku4uCubhGWrI5eKKyma7nRIK2x42d+8uaG42Tp7M\\u002f3oOBkaERO1zj9utNGp1divXAr5L4SGMDolBkr9f0DXWeahCObzg3rtEShUzopL21JagDoCfik\\u002ffnDY2tUtZyH8qPl1xKen\\u002foKef92G0VOlZKQEATjfXXJXYxQpkmyvOb6u6iFoHLwOrD3WclUkuas2GJqsptNs5cLrJYyOm37DzB9QKoLGZHzrw2zezrx2IiWX6keBeb6040LThlGYn6gAAgDBRbLjI+4wHj1eemzHzTH1dXj1auMYJC5BXX+\\u002fpdUbpWinKMz97O6SFhP5j9lzU2m2mhKbPiRjhyuTOAvtx0V\\u002f\\u002fG32Le6v+BQYEy3Zl\\u002fg2ZU0SqWHhFIXFbZtGgN8YGB3EtvsiMiQA469q8UNet7G88PAEnSC6RiAUWq7+JknqURq0RNQE0absVs8DDMxQ4qzvOQntjCbUwNE2BpmPuTUJFHeeGsjJenkx4eHja5+qV41d\\u002fvOuBB7+ZNWu4XCbS6S15eZUnT5aFhSluunEq2ppnAOIZbF5l0nmWvBvbUcVUJ6NDY74vOsm1FOubmq3oI1VOCPqhPD3NLWnjvYrFNMs8enDDuvm3+grv9UWxvvm107tQ66CmP\\u002ffhcHWEV7F4R3XhC2PmtZMvxRdnWmr\\u002fdrgr0YcDl4HVh1LSbeWZE5plHj\\u002f0+\\u002ftTr1QKOw767DkmRyTMikreXeslneOh+rIHD6x\\u002fd8pyAe5XgGmHUAzTYDVGS5WoI9B0fG3XWt3mrgOIg7FZabOJ0lZbiivNF2osXnrWxYSQhaipf0AzzJaiwr3lZadqa5stZoPdrhAKQ6XSnIjIaXHxi1LTBERgzonOIiLJr65cccOvvxQ0tVesDMFrrpj2SVCpv7lqhVTQQXq+rvFU5pUXDTVV5mbn5oHG89+U7r45cRa3Tf+BZe12224C78pYUCkRtxjbhpV6Cxoh4kmI3C2JYUWz1k\\u002fBXS2TcDdrWtDBMQ9P3\\u002fLI9TMfuX6mnaK1erPWaNUaLDqDRWuw1DTqfvizg4DZgBOmlqMmgKiwHv955uEZ6BSbClCTO+mKLgb+BAoZ2fGFbKf59Bc8PJ3mmmsmqlSyjX+c+uGHQ1arQyoVxsaqb7px6ooV4xWKvnye5wkUnsHmVSbtRZ1bEE+0VBkp7ThJJgCMDkXXSRfpmoyUnWvBALLVaB4bnp5mVnTyyJDo3OYa1AFwXttw\\u002f\\u002f51H01fIfRbAivWN1+\\u002f4zujY2j9sPbnPpwQNmxzxXnUClBr1v9QdPLmtM7V29hTW\\u002fL4oQ1WutOC0oBmYPVhtCxIIRAZPM6f\\u002fXWls\\u002f9YfVXiiMnh8cnKkDCJTEQIiF7PM\\u002fPK+IWXb\\u002fnM6+m9tfLCym3fvD1lWaIiGPV1hnxN\\u002fdrSM7+VnVuVPPLJkT2e1rJjwf3DwsdRU68TJAgdFzwftfYDdpSUvLR7Z7XBTa\\u002fUWq1aq7WopWXd+fxX9+19ZvqMZRnDuQ16DbVYvOaaVQ9v2bS7tKdmTSbGxn64eKla3FNDZ6VA+s7YO+45urrJ1trJHxdtVQtlS2LQwke9gFHzIEPXOV9bjB\\u002fZzGvd\\u002fSxNlTBMo0T+gLvdL5RiEVdw15o7FtzDlG6rfi7WNk1JjedafBEkdfu+mk1o\\u002fAgPT39ASBLhwYrw4LZHtYYWQ+8L7okxIalxYYUVbTOX8VHqsRn8iisenvZggS01XUCt7qTIs1BT7yIhOi6+h2P+PgP7icO61dRyJ9eiii71Z0A+CHBYN1v1bzJ0BU7EiOT3C6VXoy06CWXbZ2y+HrV6gGHSoKgOzsbBga42nbvOUqp6s\\u002fud3GXmz8+eP78vl7Dw9ChRUmWYWNbISVJcqm+5oG3gNPE3nwwAJCqC1SKJxta2oqhQ12Rj3PJtJilD5IJ+XURtsPL0qDnX7fgOtQIAwK6aolXbv31z8tIOJTCaZX8pyX09d7dmwOZi7g79tg\\u002fnx6a\\u002ffHKb19wG\\u002fzq1IyUo1M9ammbK8cG5A6vzD3p5o8HOwOpDDGDhsIxfSrykntbYzJ8XHPm84Ajq8ECAEwqBSCEUKQXitKDQEcFROSHROcFR3U8EHyML+se4BU8c+h11AABAXkvtgs2f3JAy5rb0CXFyFer2jcFhO9JQcbCubH9daZG+V7M7DIzx\\u002fZKYe0isRwKou8N7Rw+\\u002fdegganWn0Wx6bOuWvIb652bMQn29glwo\\u002fHzZld\\u002flnv7vgf0mu1uYQDcRk+TDkybfNXZcT098RUuCP5lw3xOnviw1NgAAw7L\\u002fPrf2tKbsiYylUrJXR11C8Xy79S+H7QAA0FQJDejSMBwPE8vulCgeQ+z+oJKJgXPtF9W3BvW3A7f2KQDkVtRyN9sBycVvtffUDCoPzyAAx7DXH1329ve7cy9WUww7PjPuketmiDhJaXl4eDwxU0YTZUCt7qiFoaipd3EwHY+LxERPhRQMNWiqyNRyPwDtfG3WPk6QKYSwg7q1PDw8\\u002fZbs4KhdNUWuTc88AH7mk3EyKiSG+25nNa1BTi48Y+q7j42mjA6bwWEzOGyen+hEb7cda6xUCERygcj5f08\\u002f\\u002fPY3JobHXZcy+seiU6gDAABym2sWbv50ReKIa5JHeVXcCnVN++pK1hTnXtS5LbsX4MQwuapE3\\u002fEz7yCg3\\u002fZhpFQxLyZta5WXOWkHQ9+y68d7MiffkT5RLXJbH8+lzmz4ufj01xePaz2Sg48OjTnVVI0YBx8Drg8fyp62ufK8ydHxGNgXDoZusZmdhUzzWmrXluYBQJRUuSp51DXJIyMlfi1s8sWVCdmFusbV+YdQBwAAUAzz9cXj31w8PjIkekZUUk5wdLxCHSlRSEgBhmEWymGm7CaHXe+wlhs1JfqWEn1zsb6pQNtAe5sR6QUGgGQwM3xluqJzCzF6gTXnzrrU9hiFcmFqanZYeLhMJhEILA5Hvcl0tqFha1GhM\\u002fj9i1Mno+SKO8aMdXuL3gIDuGnkqMVp6R8eO\\u002frz2Txjt2V3MUmuyMx8YMKkSLmXTAs9QZRE\\u002fcmE+185+8vehnNOy+aaE3sbzi2LnXBl7MQYaYh7855CKFkqlCwFoFtq06XKF0XSa7heDAjAun5BJYeHnC5vU8zrtAat2apyD0VHSAhTczePFFfSDEP4kSZMa3ILn\\u002fc6H8vDw+MiKlT52iNLUSsPD49vzLQRNXkgI7o1Iu8+RqrjjGpCvL0fYh7\\u002fcVi3OtV2roUX3Hl4Bi457oK7J2PDOrEccHSom+DuSU5INGrqiHKD5q\\u002fqi0aHzWC3GRxWp7De+s9uMzhsDvcgeq8U6hpXbf+Wa5GSApf47vrf+U8uEC5NyA7r69qDAefZ0XOPNVT6igx1MPRPxad\\u002fKj6tFIpTlKFhYpkAJ7R2i8ZmrrMYmznLILg8O3ouALx04i\\u002fUMUjpt334WM6M7dWFNOulgBzNsh+eO\\u002fjZ+SNTIxNHhkTHyoIUAhELoLVZNHZLmaHlcEN5uUGD7gYAALemjX8oe9q4dW8NBaFhYPVhrCzovSlX3rd\\u002frS2giWtqzfq38\\u002fauzj\\u002f4RM7M29IneM4b+c\\u002f\\u002fjZzdYjOvKfYShu+EBTjdXHPaPU0TBu1Wjuojuq4P9g7TQpdfFnEDau1rNBbLv\\u002fbtAQACw\\u002f4+feYto0Z7TnQvzxj+9+kzvj596tV9e2iWfevwwaUZGWHSPvv1VUskz86Y+djkKZsuXthSVHiwosJOdzzC4ELg+ISYmAUpqcsyMoJEvf38KSfFr426aWP1sdfP\\u002f+YcGxkp6\\u002fdle78v2xsuDspRxSfIwhUCiYKUiIjuLoaYEzECNblBEIIMDCMxLJDx9RnRaIHyc1X1U9PiESOXuBCVXCw0WltnUHRm674LZbOGJ7m38kJBrdvcuNK9+CoPDw8PD083MVN+CO5kHwvutZYK1OSBStBLk\\u002fqDHpZGSwoxDGrpLBgRJZRcyTIahmlhmRaW0bCsd1WCh4cn4HjWTeUiJYUZqnDU6psxHmncEboQ4X6mpfbVUztQa7cxUw4z5WiweP+ZGxkSPfgEdykp\\u002fHzmNVf+9ZUzptUXerv1ZFMVavXGyqScm9PGHW+sRB2Dl37bh2lBYQ9kTXn37H7UcQk7Q++qKWp\\u002fPgxhSXzmc2MuwzEsXRVe4J5palAy4PpwVnTyr\\u002fNu+fvRTWdbvK\\u002fs6TJWmvrXqR1bqy5+NuPqLpdgxQD+M2GRWij5+Pxh1Oebfqi2Q38W3CWEfFH0XSNVM1BHP+Cns3kGmw0Anp424\\u002fbRY1D3JQgMu330GAyDV\\u002fbsNjscP589++CEiWij3kUqEFydlX11VradpvPq68\\u002fU15VoWsq02kaTWWO1mB0OB02zAAIcF5NkiEQaKpMOUwYlBQePCA8fFRUtIXv1hKm2tDRYtfVWXb1VW2\\u002fVNlh19Vad52KQBqtue90ZxNgdDs3\\u002fD2pyRyRZhRMJqLV7DI9Gh6R7C0rbF9xxDMsZFnWwsNxl+Xz3cX8E9135bks+ecGdh4eHhyew2JiOK5GwfT027rCsq1KgEhM+lwDzdAqcQMc5ONHpeFUEgkyRqt91M7F2hmmxm7+3Gt52s\\u002fPw8ASanJD2FPBRIdGeEWntMDI4GscwX+tuCQzPVEegVp5eZJhc9cPcG27e9aOvmQb\\u002fWRQ3\\u002fNUJiwBguCqifwal9hD9tg8fzp5+Udf0Z2UHgyI\\u002fuTZ51D\\u002fHX+4McJ4cER9wsbh\\u002fMrD6sNasP9ZYoeixqhjHGytv3\\u002fPzN7Ovl5Jdj4V9atScDFX4c8f\\u002f7E72mz6nV\\u002fVTPxHh0vHB86eFXSkjlaivf7CzrAQAgiWSW0d1vBL25pGjPzp2tMls3l1W2ueCuwshQYyNjh4b3d1HnR5l5b7\\u002foqb+gVh2E2rqNsNjwsUC0upoW9ez+3zJ35fOamvhjSlp8VzB\\u002fWRZ9bf7T900rb3TclteYUlDC9cyzD0XPA8PDw8PTzcR4ELU5IGJMijIINTaWzAsc1Z3DLW6Ey7qIOKSx39I8XzQ\\u002f7ctqwwmFEiWubUICJgQJyIxHBX3eXh4Ak6oWBYpVdSZvZfr8L9iqhOZQJgaFIaUXXWRrgoTEf1RuBhSpAWF\\u002fTrvlgf2r8tr8bdymCf3Zk7+W84sp5IoEwiHydUVRu\\u002f5NAYl\\u002fbMPcQx7Z8qyl09Ivy86ifo6A4nj\\u002fzdy9l0ZbZLXpPD4Ly90MNYaHAyUPjyrqfs4\\u002f9CflQWeYayB5WRT9f8d3vjBtKtQR2dYlpA9OjTmxeNb99Si1RO7CY5hEd3LNe8nHad77h0wwIKFUaPVs6+Je+Kp4V8uiLql36rtAFCu1QLA+OgYf\\u002fJlExg2PiYWAMp1WtTHw3MJsYBE4tmrWnQX67xneXMxJzMZsby+ac+6Y62Z7j3Jr254ad12xDgmoV\\u002fPu\\u002fDw8PDwDDj8CQyvs\\u002fq1aLqHOKk5oHN08ICaLB+Omni6CkGmyII\\u002fIsh0DJMQgmx58FcEmYI24uHhGVC0k+alUxVTnbSTVaadD+LpTWJlQWvn3fJw9jRx5+c\\u002f4uTqL2dd++TI2dzkzkNw4UL\\u002f7EMBTrwyfuF7U6\\u002fschqQnOCotfNu4SrFADAxPK47ubwHFv28Dy2045WT25Zv\\u002fXJTxfn21XYCw3z9Q5u2y5bKgiMNHSdvbB\\u002fnOf\\u002fFzFVjOv+b4pVhctVjI2bsX\\u002frgLWnjUF8P0PFFvnLYo6gpQGCAk7hAhEsUpFolDB9AZam0VisAqCUdP0w6UYnEAKCzdry8mmdA0FwTqwrfSZBpqAMAgLWavrRbNjGsXiAYI1E8ihP+DhDnZqXsOOeW7GXdsbNPL5nFtSDEh6pGxUefLm+rF0Ez7PO\\u002f\\u002fvVX3sVVk3Imp8aLBa3XeGFd88aT+d8fPM0NoncyMbkTFY14eHh4eHg6JFiIFibxpNBwdpRqEmrtFWyMdUvdGtTqQYYiBzXxdAOB+HKB+HLUysPDM2DJCY7+q+oiagXAMWy0b\\u002fXcF6NDYn4sOoVaAaCjfPE8vQmJ44+OmHFtyugvCo6uLc3TtJuR3EmKMvSW9HFXJ40U4gTiylRHBCoLxwCi3\\u002fbhorjhM6OSfiw+\\u002fdWFY7XmjgvLAwCOYVMiEq5PGb1gWIanHKsUijNVEWc1dahj8NI\\u002f+7DFZr5514\\u002f5mnrUASAiyIXDMiaHx48IiYqQyBUCkcDjHONioykTZW+2mqvNukJd09GGin21JXYfBaj\\u002fc3rn+vm3otbOMys6eVZ08pmW2nWleX9VXqizeF9Z5QsBTowJjZkSkTA1MnF0aIxnJ\\u002fccGNvu5AaPV8Z\\u002furrZbL48Ne2DKxajPm88sPmPLYUX1RLJibvvQ308vpn819OoqVfoMId7c02sVPk8w9QzVDlOhIultxCCDKfLavzEpH8Zx0MAIxm6HieiVGHbMFzltr8PtGbrzH9+TNFt5a2VEvGuZ+9yieZe2X626JFvN6LWS6ikYrFQoDVZPHV2J2lRoesfDXyGnL6iocVw+kL1xYrGi+UNdc16g9lmNNkAw2RigVQiDFZKU4aFpcaFjc0clhAVjO4cCC6UN+w\\u002fXZJXWFNRp9HoLVa7QyYWKuXikCDZiJSo0emxE0fEC9v9Qv2EZeFUQeW+UyVni2sr67VGsw0A5FLRsAhVZlLktFFJ4zLjuJPQd73y05nCtokZHMcOffVYm9s\\u002f+rx7G1oMSx79lGuZMSb59Ud7IClBH0HRzNFz5WcLawsrGytqNQazzWSx2SlaKhZKRAKlTBwboRoWoUqODR2TERsZ2t11YCXVzd9uOnb0XIVWb1ZIRRmJEYumZ102Id0zfOF4fsWabafPXKw2WuxqpTQhOviyCWlXTMsUkO0NyPoVvX\\u002fJ9GH3niupO1VQdaawpqJOYzBZ9SYbSeChKllUqHLG2JQ541KDg6ToPj3AS+fu1zncMpghSAnZi1kfCvGeyiDZDj9UfHisZS9qdUdOKv+RtRrHOl7L2Ckc1q2mlju5FlV0qT8RMDz+YzN9a9E949rEMGlQ1AWOf9Ciq01n2TbhRqp6Uyi9muPn4eHhCSQOhj7WWHmwvixfU19qaNHYLGbKjgMmE4hCxbIUZciI4KhZ0cmdKp871OiffUiz7NmW2mONlccbKyuNWq3dqrNbbbRDRJAiQhAqlsXKgpKUIWNCY8aHDQsddFWCA0L\\u002f6UOjw3bVX18X6dHcCRjATWnjHhsxI6irIflOmq2mp49u3lFdiDoAAGDf0gdiZIFMIMkCFOoaTzVVn22pKzdqqk06rd1ioRx2hhbhpEwglJJCuUAYLVUmKUOSFCFJyuCs4EgJ0fVs8t2BF9y7woqffzxVVxsukx284+4O13cwLDv5s08azabRkVFrV12Hunl8058Fd+4mhomDQjcSguEAoG2YieGqoNC1AKTdstGguV+ieFSqeILbvh2e\\u002fHHLptNuE9SvXXv54tGtar5XWBauff+Hs1Vepiv94dVVC5eO6eKS+fOl9be++D3XMnb4sA\\u002f\\u002f3q1Hu1VPfVVW6ybQrHnttvgoNdfiSVWD9q9DF\\u002facKCoo87cfspKjVswdecXUzI6uYDfm3\\u002f+hzti6VCUpJuTHV29xuU4WVL3\\u002f875zxR3k4wsOkl63YOw180eLhV2XV\\u002faeLP7g531IRyEMi1Tft3Lq3Amt6zAe\\u002fd+6Q2fKXN5OqYe91r0d0qOC+5aD519avQW1ekMiEuz+9CHU2j2adaYvNhz561CB3uTvWqiY8KApIxOvmJqZmRSJ+jhoDZYFD3zkfI1hsGP1gzKJEAA278\\u002f\\u002f9xfbHBQajzA5J+HVh5ZIRK2DEoZl3\\u002fhm5687ct1bAQCkDAt77eElsREq1NH\\u002f6LlLpl91L8vCn4fO\\u002f7jlxIVy7zlw\\u002fWd8Vtz7T61ErZ3kq7K3crVHUKs7V0StmhdxJWrtYTbV\\u002frS9\\u002fjfU6sFlEcsXRV2LWrsNL7j3Arzg7oQX3Hl4eHh4eIY4jx\\u002f6\\u002fbeys4gRx7C3pyxbHJeJ2LsGzbLX7\\u002fjuWGMl6gB4ZdzCG1LHoNYhAz++7wrT4+NP1dU2mEw\\u002fnc27fkQHy42\\u002fP5PbaDYBwOzEJNTH0y7\\u002fGnkDauo34MQwhfodQpBJU2VGzSNmw+uK4C8AgKErxeIrnFeWULJEYP7RYd0BfgvuN04d7RLcUyNDbpsxbmGO18Q1bWAYvLTisus\\u002f+Mnuoex0yPT0hCWju6i2A8DwxIjk2NDiqrbJ0pMFlXXNhsiQLhagyC+pQxSxESlR7avtR89V\\u002fLz15MHcUqaTc4fnimvPFddu3p\\u002f\\u002f\\u002fF0LuvYHl9W2WO2UWEjSNPPuT3t\\u002f2upXgZQWnfmDNfv+Olzwv0eXdSFC2WanXv1y+5YD+ajDg8o6zTPv\\u002fzFnfNrzd82XioUquQRt4Qd92L1Dip+2nly99oDF6kAd7VLdoPtl2+lftp0ekxH70TPXoG5vsCxcrGgYnR57OK\\u002fslU+3ev1aD50pe\\u002f6jzf+7NI3xv292rvUmBwNAUWXjo2+s\\u002f\\u002fKl6xXSPohQ9pPevGT6tns1evM\\u002fPvmTO0nQ56QrcjoU3P+qX5ehGDlM2ksDJIp1\\u002fFL5+dGW3ajDAxzDp4Rchlp5eHh4eHh88H9fbTpaWLn2qZtClV0JVv1x3+n\\u002frN3lfI3j2Kk3H3VzD3ms5jUG7WMkma4O34n6+gE0VdbSMBUAQiLzcLxHVvry8HSBE41Vnmo7ADw6Ykag1HYAIDDsqVGzV277BnUAnGiq4gV3ns5xTdaI1ceP2Wn6pd07CQy7JnuE1yBOFuCHM7kv790NADKh8NrsEWgLnnaZE9F\\u002fe0wiv58UTgAAUpAtVTxm0j3vtLOsHcPlrmYC4XiL6RPXZofkxEWOGBYpFpC3zxw3IyMRdftgeHT488vnPv\\u002frX6ijXWKDg15dtbCbAciLpme9++Me1ybLwp8H8m9d6lbow3+2HDiPWBZPz0IsXAwm65Nvb7DYOidTcjmeX\\u002fHAq7989uJ1akWnxTWGYS+WN2SnRL24esu2I50LnSusaLz1pR++efmG8OBOiNEWm+PxN9afLKhCHb7ZeexifYvh3f+7Sq3sdAaJvu3eIQLDsq9\\u002fvWPdzjOoozNkp0ShJt9cKGvISIh45bO\\u002fvMrBTvadLN557OKc8Wm7jhf6koOdVNZpvtxw+OHrZqKO\\u002fkEvXzLQd93brDPd\\u002fo8f65r8yhTZa4wIGr+26kua9Z7QzAnFOD4p+c99yc9GS+JRX6ApNV38ufLjems16vDG5JC5amEoauXh4eHh6THmvfhpg864fGLWP66b7zI+8eUf23MLQxTSna\\u002fcw2nbH2FZViISYF19uFowOi0pIlhnsr61cV+dtnMZinl4eHi88tkFL7EvERI5Upq1+4wKiVGLJBqbBbE3WU2IZUjBC+5dIVqhuH\\u002f8xLcPH6QY5u87tn1+6sTlKWmZ4eHhMpmYJK0U1WA05jU0bCm6WKrROHd5aur0UGlXnt55+iEYJsfxtrBrDFcxrPOLbku\\u002ffskVzDKdu8V8ftcKmUiIWjviqvFZNMP8c8NObgr4dhgxLPKDW5epZd2VQS+fMvyDNftozodu7qrgTtPMtsNu6XREQvKyielcC4JCJl46M\\u002fvnv06hDgAAwHEsSCZWysUkSRhMtiatkWG8SGBVDdpn3tvoZ4AwQkFZ\\u002feG8Mk+1nSRwlVKqkkssNnuz1mS1e9GbNHrzcx9uXv3MNTju17icZeHF1Vt8SYdiIRkerMAwaNGZDWYb13WuuPbv7\\u002f8xbninS+P2eff2MhKRQCYRmix21NGTfLfpuC+1XSwkQ9VyiUhA0YzBbNMZLJ75SQAAw2DZrE7MTRaUNdi2nWrSGAEAx7GoUCUAVDfokGbf\\u002fHFs+ujkt39om05TK6UqhaSmUWdzP5\\u002fX78q7Y\\u002flkZx6VfkXvXzLQR91rd1B\\u002fe2uDp9o+IStu2ujk1GGhSrnY7qCbtaYzRTV\\u002fHixoaPH5GI9hEKZWxIYHTcwOgPwtJ5WjVJNOaPajDneMlP6dwheujLllYshsDPy6H3aWCnPRtvr1Z3UnUIcPpITs8sgBcNfi4eHhGWQICOJMuVttwLPldXKxXyu9+pz\\u002f3eZXdTdfBMulE9PiAODLncd5wZ2Hh6f72GhqV3URagVYlpAtIgIsBeMYFi9XewruzbzgztMFHpo4qUqv+zX\\u002fHAAUtbS8d\\u002fQw2oLD\\u002feMn3pgzErXyDFgIMsFu3SqUtA6q7NbtwNI2ywaCTAQAlmlTPVhWj2GdE7W7oLY7uXriiOEx4a+s39F+Pvcgqfi2GWNvnj5GRAbg8g8Okk7JSdh3qsRlKa\\u002fVnCupy2o3qbRXDp4p0xjcbtCzxqbIO8qlcP3Csb\\u002fuyHUp\\u002flKxcOKI+EnZ8ekJEcnDQoWckoMmi\\u002f1kQdWabaeOni13GZ2cLKg6dKZsck4CYu+Qn7eeqmlqk9KEJLF8ds7scSkj02IIorXOHk0z50vr1+3M3XLwPCJJ516s3rw\\u002ff\\u002fGM9qL4XWzad27PCfT3UkASK+aOXDw9KzUuzGXU6M37TpWs3ZHrSrl+9Gz56Qt+BXUi9G339jKzxqbM+vhBima0BovOaNEZLFqjRWe0ag2Wn7ae1LqfnAGhqkH7ybqDiDExJuTqy0ZNzkmICg3ixkjRNFNRpymqbDpxvvLouXKXhjs+Kz42XNXWriOOnC07nFcGAPMmpj9y\\u002fcwwtRwAzhbVPvXu703atvHQ+dL6j3494NRwE2NCnrl9Xk5qNAAYzLZ\\u002ff75t57GLrpZmq\\u002f1YfsWssSkuSz+hTy6ZPunen7aeyi9xkydCgmT\\u002ffnDxqPQYrhEApo9JvvuqKZ\\u002f\\u002fdvjL392iXVLjwu5ZMTU2PCgmPCggVZ1dXBax\\u002fJT2IMN2MBNsZ2w\\u002fV35yqHnnvIjlWUFjAyW7N9nqzupPHGvZW2NB70vtsyL2dhnZieVH3abteFnWRln\\u002fcth2047zDF3JskYMBBiuwvBQUjiKEE4QiOdhWFeSFbigqULKdpim8hlHPkM3sKyOZcwYJsFwOYYF4YI0QjCcFE4ihePRPXn6BJZy2P5yWLfTjjyGqmJZC4aJMCKcIDNI0QyhZCmGB7U1xgTgZe7bX3rt3KCpizbDew7bfpbV4ngIIZwglt1FCEcjzRzW7TbTl7TjNLA2DA8jheOF0utI0WSkWYf02nEBAADtsO2lbUcpxwmGqmZZHcuacDwEwyMFohlC6QqcTEb36B6044zDup2yn2CoEpZpYVkrhokxPJQgkwjheIFkAUG2F7zS30gIVxfXNZusdplYCABNelOd1jAuJba03i3nJA8PDw9Ph+Q219gZLwFbkyMSUFMgUIm8CF9muuvL5QcBgXysGlJgAP+dtyAnIvJ\\u002fB\\u002ffrbW7BcVzCZbIXZ86+PLWDNNw8AwuR7CaT9imaKiIE2QxV6rAfFornGzUPA9AYrrJb\\u002f5QoHsYwGcva7JbWYqq9Q3ZsxM8PXX+spGpL7oWTZdVljVoHTQOAVCgIUchGxUVNTo2bm5UiF3dR0\\u002ffK4hnZXMEdADbvz++C4L55P5pkefGMbMTiSWSo8rIJaduOXJg6KunKWSMmZMcLOCowF5lEOH100vTRSb\\u002fvOfufr7Zzo\\u002fIB4NtNx7qgCFc1aF2vR6XHPH\\u002fXAk\\u002fpkyDw7JSo7JSoRdOznnrndySW9qe\\u002fTvojuBvNtg9+2YcYo0KVrz28ND0hHLGrldKlM7MXz8j6eevJd3\\u002fc60xtYXd4ibLvkL7t3j6BJPBQlSxU5aZn\\u002fXWooCcE9z\\u002f2nkOC1pfNGvHULXNdszVcCAJPjAlJjAmZNykdAMpqWrYeOr\\u002f1UMFVs3PQpu3SojMDwOSchFfuX+QS9LNTol6574r7Xv2F2\\u002fL7zccBIFQt\\u002f\\u002fjZVUHy1uL1CqnoxXsW5hXVNGqMrpbHznWgCPc+fXXJ9H73mq327zYf41rEQvLDv1+dEO09f6iAJO5dOZUF+IqjuRdXNqkUksSYEE7DwBApjp0cctmBJr8ynlWYiz4v\\u002fZ9SoBoZNDFNkZMoS+us6m2hTfXW6hprRZmpsNR0ocnmNg\\u002fhJ9NDF4xRT0WtPQcmBHDeVymb8Uur8T2WaV0c6YQFiqUtQNfSjjwwfYvhKpHsdrH8AcA691NOU4V2888O61aGKkN9ACxrZGkjQB1NXXBYNgIATsaLZLeKZLdd+vP8hWVtulqfZywXRdifhKDjX8ChjN38i9XwGkO7BVKwrJmlyhiqzGH906p\\u002fWSi7Xax4BMOkAIBhChba4gD8pOfODYd1u6nlNudrseIhseJJAHBY\\u002fjBpHwG2dT0ZQ9czlo0OyyaJ8mmR\\u002fL5LuzJm7d\\u002ft5h8ubQJLV9ktVXbLepHsdknQP1z2dui54\\u002fIBbTN9ZTN+xtBViIOh64Gupx25VuN7iMsDQhVdhtp84LBusRreoh1oPkaWNbN0BUNXOGy7rYbXSeE4seLJLkxU9AnJUSGFtU3nKusnpA4DgDPldTiOJUWEIIL7ieLqz7YdOVNWZ6eouDDVsglZN84c41wz2qQ3zX3hky8fvubn\\u002fbm7zxZPTo9\\u002f7eYrvttz8vPtxyJVin\\u002fesCBzWITrfTadKPh53+mLNU0AMDw2\\u002fNa542ZmudUUceZV3\\u002f\\u002fq\\u002fZVN2nc27s+rqGNZNjE8+J83LkyKaP2d\\u002ffPkhae+2ezaZf+r9yskaMDQ2Yq6346cO1FcXduiZ1g2Pky1ZHzmjbPG4F3NP8PDw8PTIaUG71OVcXIVagoEdtqLuC8lBKhpKMEL7t3ixpyRVw3PXH8+f39FeV5DfbPZYqMpCUmGSmWjIiNnJSYuTk0XEF0btPH0X8TS6xiqxGr6inLkAUaKZbfIlC857Icdtn0i6bUGzT3a+qmEIJumihm6Uq56G92\\u002fhxmfFDs+Kdb52uqgCBzr0ZNw2qgklULClSO3HS547IZZpDfR0BcGs23\\u002fqWKuJTJEMS7Tr5QOD6ya\\u002ftC1M5yRpP6wdGa2zmh5\\u002f2c3Me5MYY3NTomEXbwljsmIfetvV4nb3X3s8GEv3L3w\\u002f97ewDUWVjReKG9Ij0cVQIQvfz\\u002fiFPJcSMXCt\\u002f92lS9NDQBwDLtu4ViBgHz96x2orzP0h+4drOx1P+eHRap9qe2eJEQH37Ni6j0rpvpOFd4ej1w3E3nEGzN8WGZSJBIoDQAPXD3NJQc7EQvJ+ZPSv9\\u002fSlp2jsKKR4+8X9OElA73bvQdOl+qMVq7lxkXj2zlMJ3dfOXnTvnMuWZ9h2c9\\u002fO\\u002fT2365ybxUYlkRfd9GQ12irRR0+0Du0+5q27mvaCgBBAnWoKCpYGCojlVJCJsCFBBAsAMNSNDAOxmahzVbabKT0OodG62g2UQb07TpJlnLsspibUWtPgmEKAGAZjanlLsrutvLAKyyjtRredNh2ydSf4kSbbNQOlO2A1fgBZUOnoNqHocotun\\u002fYLRtk6k9wIgp18\\u002fQwLGs1ax5xWNtUPK+wrNVm\\u002fJCy\\u002fiUL\\u002fgInEzFcDR5qbzv05rlB2\\u002fMAgHacMWkfBtYz3o2x6P+Nk\\u002fEC8RUAYNG9wlXbudhMX+BEhEh+P+rg0JvH5YSha82aByn7UdTRM7BMi1n7uMPq168VZT9ubL5GKLtRqvxHZyfqep8wpSxYLs0rr3UK7nnltWnRYYR79sXNJwqe\\u002fe7PuDDVlZOyRALyeFHVGxv2niqtefO2Ja4f3\\u002f+t3xMXphqfMmxXXvHzP\\u002fxVWNN49ZScn\\u002faf\\u002fsfP23\\u002f+2w3ONm\\u002f9vu+rnccTwtVXTc5mWdh\\u002fvvThTzc8sWzGzbPHuj7LyZ6zJf\\u002f8Zcfk9LgbZoxuMVoOnC+NULUNjKdkxH\\u002f2wEqNyfLx1sNFtc2c\\u002fdr4asfxgwXlkzPiZ2cnUwy960zxGxv2mmz2+xYOjIkQHj\\u002fg5054+h2eCV6cqIReQtG7T6VJi5oA1KIhnViblz+6i1QguCFn5A2XMsbQLEvwM9WDH1yqfF6ieJJh6nE81BlVJBBNF4imA4BC\\u002fZlJ9yRlP4zhQVLlcyLpSnTvXkQc0OQAXiEJfMGU4T9vPemy6IzWA6dLZvoOyfRkx5ELdvdQ3yumZfoZ9BHRmbqjTm64fNyvO3K5KY8dFJ1XVDMuM47Tyl+UMvF\\u002fHl7avtruZMaY5PFZccfOVXCNeYU17QvuFM38se8cYnz0+pkdamoAsHLuyBP5ldwMFZ2lz7t3EFPb2NZFADBtVJKfajsX\\u002f64SN5JjQ73GMk8fnYwowhKxYO6ENK7FyYjUaOAowrUe2cP7lr69ZHq5e4+eK0csV\\u002fqR058g8CUzsr7Y0CbvHs4ra9IYQ\\u002f2eWvMfES65LfGxdy6+aGO8D\\u002frbQefQ6Bwat4mpnmRE0PhbEh4lsB6covYEw+UsazI230A78lCfb2j7KVPzDfKwPzDMbcLGK7TjXGeVRxe0\\u002fbSp+SZ56HoM7\\u002fRvAU\\u002fXYR2mllso20HU7gOaKjI2r5SHrMXxELexVEf05rlBOfIAWLP2KW9qeysW3T8EovmU\\u002fZjN9Bnq42A1vCOUXo\\u002fhKtRxid48LgBgGY2xeRVDlaIOIDBcjWFSltWzjBZ1dhWGKjM238DQboPJDrGbvmMchbKQr7uZk6qnoWhmZGLU2Utp3PPK60YmRFFM27pJrcnyyprtY5JjPrl\\u002fBYG3jpqe+XbLphMFu88Wzx6R7LQISOI\\u002fN19hp+iZz3609dSFzc\\u002ffHhMSZLDY1h3OszkokYA8XVrz1c7j41JiP7znSpGABICH7VPv\\u002fWjdWxv3Tc6IT40KdX0iALy8ZvsHdy8fnzrMucmybqMvpVTsdK0\\u002ffNaX4P7M1XOkQqHrYeGe+ZMW\\u002ffOL9YfPDhzBnbVZNljNv1KOswyjwXAZjkeQguEi8QKheL4rgWpL\\u002fUSciA0K+Uqvedhh20eQiYqg10nhKJPhDYvpCwBWLF0lVz4P4DbctVu3WczfUfbTDKvFMbVAOE4iv0Mg9LdnrOYfDdonAcOVqvdEkqVcF+UosJhW220HGaYBwyQkmSmWXi2WXoP8AZ2Htpi+t5p\\u002fpKlCwAQCQY5Efi9JDkdbAQAAZT9ttax32A\\u002fTVBnLWnFcQZCpIslSifQmwNoeHrvQdX6+swureY3V\\u002fCPlOM+yaHSCQDhZFfor19JjXcfTq9ho7wt2bYx3e3eoMxuqjFrUCpCk9PJYNHTgL5gAw6vtQwcMExFEnFNt50KQ8cqQn4OjitQRJyTyexHvoGTJdHRJuGd+mPbZfABdD7vI4z0DCI5ji6ZmIsaqei1i8ZN7VkxBYlTbYalHnpyzRR0Efh7OK0PymUSGKv3vn7uu8nfAGigC272DFQdFm62tC+qdtF8YM4CMSPEeuDc8EY2WHZ0e63VdQnyUm3LdpDH6Wa65d+jbS6aXu\\u002fdcsZuIHxuu8lM0H5XeuhDKCctCbmEN1xJAosRxdyc\\u002fJcTRJfb9illhi27tdbUdADBMYtE956a2YyQpmi6S3SlWPi1RPiuS30MKJ3iGyNDUBav+ZcToFaHsek\\u002fpEMPkpHC8SHaTWPGoRPmCWPGkSHYzIUBv3QBAUxcs+n+iVt9gGCGUXi0QX0YKx+BkIoYH8XF\\u002fncWsf8GL2o4JBOJ5Ivn9EuULYsUjQukqnIh2ORm6wdRyG8t2blqrN88Nlmm2mb6mHWcBgBRNFSv+JlY8gSQ0Z+gah3Wr6z0JMk0sf0iifEYgmsVtxrJmu9ktSRdCbx4XAJg09yFqOymcKAv+KigqPyjylDLiQFBknjLiuCTo3zgRw23mBMOVhGCEQLJELH9IqnoddbvD0A3G5ms91XaCTBHKbhQrnpAoXxArHhJKlmE4qnFQ9iNmzUOIsb\\u002fBsmxOQpSzbirDsvkV9SMToriL+f48ddFsc9wwc4xLbQeAy8dmAMC+\\u002fLZvwZk3RkgSMcFBkSpFTEgQAESqFSwLOrMVAH47cg4A7l0wyam2A4BEKLhnwSSGYdceQqc\\u002fLxuZ6lLboUuxDsFyKTc0RyYWpkWHNeiMzix2\\u002fR+D9jG95gG7bRfLWgkiGliGpi7aLBv02odZ1shtSVMXjNrnHLb9ABjlyNdp7rSYvjQb3sSAZBmdxfiJ1byG29ygeUTXcqvdup1lrQQew7Imm3WLtmmlSf8vTjOfWExfGbT\\u002fh2GioOCvEbXdav5O0zjfav6FoetxPBxYh8N+2KB9Qtd8I3sppVWXYPQt9xp1f6ccZwAjcUzlsB\\u002fVNd9oNf+MNgRw2E9pmhZZTJ9RjrMADEFEsqzJYT9q1D2n09yONO5U13XqnQHAoH3CoH2McpwWiMaLJFcSZILTTgqyJLI7JbJbuY17rOt4ehupwPvTZZ0ZnXTpPj+XnPZ6O8tWR6KmoQQ6fOfh4eHpLKlxYWlxYRc5eQ8O5JbqTValzC8ZuqZRd6bQrT7hqPQYz2TogSUnre0x1YnO5JaWwU8kIoE\\u002fueZdjPQoY1jTbvgqAGw9VIBYrpqd43\\u002fGnqSYkOGJEedL61FHTxKo7h3ECEhCSBLchR3tZw4JIL4CvaNClYglwyPduRO1wm0dIsOyFqtd4d\\u002f13gv07SXTy93bomurxQoAsREq7mY7RHv8PeW1bqnDA0uSLOP+5Oc+L3vD4NCivr5GRipWDbt7RFBA6iV2GtpRQDtaz1gME4nk94nk92AYOmvCUOUW\\u002fQsO606u0Wb+USR\\u002fGCc6eJLBMLlQepPN+CEA4GSSUHwFKZ5HCkd71cFpR65Z+wztOMM12i1rxIqHcMJtksY3pFT1pruFZhkN5cgzNfdqup4BCmU\\u002fYTd9jxiF0uslyr97xnRTtoMW3XM0VQgANFUEUIQ0aJ9ePjcsuucBQKp6XSi91mkRye81NV9L2U+0tdG\\u002fwtDVACCS3y1RPuf8S0Ty+yy6l2ymz13NHLY9Ivldrk2E3jwuh3UnZTvAtYjk90mUf0c+CyciRLKbhJKlppbbKPsxl93\\u002flPROLNonnP3jQiCaLVY+7W3mgLGb11r1rzJM2+jCYd1mM30hknnR4\\u002foPIxOi39m4v05rMFrsJpt9RELUyZK2+eD8inoAeOzz39t2uESzwex6rZS2TvGKhaQztzsAiEgSAJxz2AVVDXBJl3eROSwcAM5VoAMAp7072BzUusNn9+eXljdq9Warxe5wjgBZJFq+X0I5zlnNv2CYUBn8tVA03XluM0yT3bqDYZpwPIzbmGE0NF0dEnmOZU2ahjkMXWvU\\u002fyMo5DuhaLbZ+L5J\\u002f6rNull86Q5gMrxptfyKYQqF+g2ReCEAAUDZLH8YtE+ajR\\u002fiRAwiBAMA98oyG1eb9K9geFBQ8LcC4VhOG3DYDhi0f8cwUh70b7HkWmfEt92216B9xG7bY9L\\u002fUx7k14y1JxbTNzbrZgBCoXpNLL0GgGBZq8X4kcnwX7QpgEA4WixdSQqyReJFrVOkrMNs+tSk\\u002f5fdusNhP8QN5O9U13Xqne22\\u002fVbzTxgmUoVuJgUZAAAspdfeb7NswokIufstqOe6jqf3iZaio30ne2qKR4WgT+vdodas\\u002f7zgKGoFAIDpUYmoaSjh7\\u002fMnDw8PAmU\\u002fYTP\\u002fYjV95vkPbToEQERnB0X\\u002fdfgC19IOm\\u002ffnI+EdS6Z3QsLuGpEeYpPePQ+yn0wfk+xPMhkXEcEKiditcoihIyX69AW3JysAmDyyc79bU3I61777BKp7BzeIMrv\\u002fdMk5jxzfPYHnt+MkOEiKWOIi1YjFiezSc6wLmz3wKxO7TN9eMr3ZvSyLTmXJpd4jWTyReyj4Hd6Lukm8LPWJtH8nyTJQR9+BATYl5LJnhr\\u002fVV2o7FwxXyUM3iBVPeKrtAICT8bLgLwWSxW5WlrKbv3Wz+EAku0MoWSYP+VkZvkesfIoUjvGqPAIAIRgpD11HCt07hKWcVSW7CoHhoTjRFh\\u002fK4xvGonsOwG1UJAl6Sap6zVNtBwBSNEUR9icpmoY6\\u002fKaXzw2BZIlLbQcADBNLgl7h+MGpJpPCsS613YlY+Tdu\\u002fnHaftz12iu9dlw202ruJima7Km2u8DwIKl6Nfcat5m+pil\\u002fp0lspm8dtt1ci1jxiCzkG29qOwDgQunV8rDfcSKOa7Xo\\u002f+VZ1rVfkTUsgsDxs+V1eeW1KpkkLlTF9RosVgC4adbYRxZPQ\\u002f4tHj\\u002fc1Ywb\\u002f8597cJktZMELhO7\\u002fWgGSSU4hhksNq4RANQyt4nwzmKw2K5744f\\u002frN1F4PgNM0e\\u002fdN38D+65Mstd6+\\u002fPMEwDAOB4uFA0w3Vu43ioWLpKKn\\u002fArSkAAEjkd2CYEMfVQvE8ABAIxglFswFAJF4AAPSlIsYMo7EYVwOAQvWaSLwIWosVkyLJcnnQvwDAZPgvEj4PAHApkZrZ8I5J\\u002fwpOhKtC1yFqOwAY9f8GYGSKp8XSG135VYSiGXLliwBgMX\\u002fDMl2M8LWYPgEAiex2sfQ659+MYWKp4jGR+HK0KQAAKFTvSGR3tS1IwgRS+f2kYBQAODhzjU787Don\\u002fr+z3fonAAjFV7Sq7QCAkVL5wwBgt+4BcBtk9lzX8fQ+mWrv95lfSnLNlB21dhWjw3bP3l+NDvTOCQDjw4ZF+RD9hwhefn54eHjah2VadE3LdE3LjNrHTLqXPP+hOwwBFk4ZLiCd46RW\\u002fM8qs+Xgee6mROQ9r3FgUXhIWg7fSRvaYUKW22OMPyikbjqX3uzlx8mF3mRtaHEb1qiV0tRhbrEkHTIiNZAz2P4QqO4d3EzKSeBu0jTzyOvr9p4s5hp7guAg7\\u002flbhR4lH0I4NcG4eNZhtjq8K8K9T59fMr3ZvSywrPt0JUX5e6HZPd7Ta36bwBIkCH4w9cWVsbeLiW6JF90Hx\\u002fDR6il\\u002fS3\\u002ft6mF3SgnvX0TvgsuCvyYE7Sc+wqVBr2F4ENfk8C9RNU6ES9Xvk6IpqMMbGCaSqt9DMsBS9sPcTZ4ewmHd7Uy64kIouVIku4NrQcGEsuDPvOYq8YdePjdEstsQCyEYQZApiFEkvwfRrDFMTnI0NZY1sUwTx4\\u002fSO8fFsiZuuDoAiOUP+FLbneBEuFC6gmOguZH77cAyBqv+31yLULpSrPgb1+IJTsTK1O+7PfKzdpvx07bN\\u002fodYSKbHhOWV1zkTuCNemVgEAJeNTLn9svHIv3kjU5HG7SCXiCiaMVrdBuE6s4VhWYUEHcR2kx\\u002f2niqua75p1th371p23fRRc0YkT0gd5v\\u002fCuz6HFORgmJSmqwzaJ2g\\u002fZmsIsjVqgSAiAcAl8mK4GgBcGrrDtotlrRgeJELmkgHEkisxTM4yOrvHbxyOywHAZHjNZPgvQcarQjeQJDqRz9A1lOM0AIgkyxCXQDgJAIB1OBwnEZc\\u002f0HQlTZUDgFi6HHGJJEsQSzs4u8hTufaz69rB6zszdB0AEKTbtDfROhVHM3TbIpie6zqePiFRERwrcxs3Oqkx6188vtVrBpjOUqRvWrHtm7OaOtQBAAC3pvd9REvf0uMPVzw8gw+z\\u002fj+U\\u002fYRANEsonuc14GgIEiQXTxuVtOt4octyrri2ok7jK4TTxZnCGiS795zxqUgMeE\\u002fgNdqlCyTHhqKmjpCI3G68Dg\\u002fZi8uFsgbEkhgT0tm1p0mxIaiphwlU9w5uVswZ+f2WEzRnKsJgsv7f2xvGZMTeunTixOx4TttA4lpnjSDwePYLVnpXRTEMcBxjmLZxWjtJxnuZPr9kerN7cQyTS8XcyHSNvm1Bffu06NCWnjH4PQEG2NTQ+aPVU3c3\\u002fLGv6U8rbUFb9DBBguAx6inTQhcECzs3DdOjiGQ3kMIxqNUDDFcKJVdz60nSjrPA2rmRvwEBJ2IE4oUOyx8uC+04x\\u002fHz9BQOyzq3bYwUK59ys3gDw2RixWNmbQfaa0DozrmBYWJSOBq1AhDCsW5R3piAFM1p27wEQaZTtkOuTYaqJISdHoP5omvHRdtPAssZxWECUtixxE+KZtpMX7s2Ketu8KKHoNgtP3PlNgxXIosDfEEIRwsli+2W310Wu\\u002fknseJxZPauXzEyIepsRZ3ebF0wOh1xZQ2L2Hgs\\u002f1hR5ajErk+NA0B2XGR+Zf25ivqJaW2hM85kMpmx3U0gg1Bc1wIA80a1zQfQDFPW0INp3AILjocoVG8atE9YzT9ZzWsEosliyZUiyRKv67EAAMcuBbRiQgDAcFd8Kw4AwLYOaSjHRQAgybRLse0cMJIQpFD207TjPLhHjmOY0mz80Gx4F8OEqpBfuaUsXFCO1qiv5vq2WToEpt0ZO1+4YswJEg0OI8gkxOKEZc02ywa7bR9NFTJMC8uYWLBeqh2NCp5+dp0T\\u002f9\\u002fZWdGBod0OmWHqnU5uxYue6zqevmJFUs47eejEFQCsLc0zUY7XJi5SCLw\\u002fuXRIo9X02fnDX188bmfaUqRyGRMac\\u002fkwdD5sqNFngjsLbL21vNJ8wUjprLSRxIRKQXCEOH6YNJ3wVlWZh6f\\u002fYLftFwgnKkO+Qx1Dm0XTs7iCOwBs3p9\\u002f78qpXIsnWw6ggfCdSone5yTGdF2Y84eymhbEEud3jmYXYWo5QeBcYZenPxAZqrxt6cTP1rfJB05OFlSdLKhKjAlZMWfkwqnDPZcLdBOJ+wLqdhALe3zqK+D0+SXTy92bEht66kJbuNmF8gaaZggPcd+TfI\\u002f8RcMiOpgfDSBSQnZF1Kq5EctOaQ4dbdlVZipkPR47A0uoKCJdkTNSNSlFnom1G3\\u002faF2Aib6vyvUKKpnIFd2AdNF3hGSDcfQTCqVzxkWEGjDY0cGFZs8P6F9ciFC\\u002f2M3RdKFlh0b3kT\\u002fxj9+nyuYGTKV6fPXHSbYKZINMxzMsPH4a7yaAMq\\u002fVQ6bpFF47LGTfqgiASAOv4xo5csAxdxbJGX9rlJVib6Rvutkh6U0e7tCGUXsMV3FnWTNn2CjoTkNvL5CRErTt81kHTT3pEuF8+NuO9zQe+233y8jEZsSFtcwbNBrNcLHRVQO2QFZOzfz145uOth0cmRDuTQ1rt1Cd\\u002fHcEwWD4pwE8i4UFyAKjXGODS4Xyx45izdutAQSRZIhBNshg\\u002ft5p\\u002fdtgOOGwHjLqXJLJbpYpHMMxjth4Vc7xfqc77la\\u002fTGMfkAMAwesTucJy0WbcAAMvaTYY3FKo3kAYAwLLOvXBXwLgnXv5sP2BZEwAAYJ67ez0Qh\\u002f2YvuUuhmkEwEnBcIFgLIYHYZjEbttFUyVoa\\u002fC366CT7ywSz7Wav7NZN8rox1xTFGbjZwAgEE3i\\u002fuU913U8fcXNqeO+unBMZ\\u002fdyw\\u002fmzsuBQfdkdGROXxmfGyf19Cqgx6w\\u002fWlW2rvrizuoh2nwTiIiWF\\u002f5m4CLUOPfz6TbLQxt+rPxoXPD9JntP9pxQbYznctOlw8yYjpUV9AEJcnB00dVrYlWEiv8aXPDy9D8vUkdIrUeuQZ0pOQkiQrJlTwW\\u002fLwfP3rJjaTmypg6K3H7nItUSHBY1Oj+VauoBGb75Y0VhU0dikNWmNFp3RqjdarHbKZqfsDspmp6wOyu7wPhPbKYQCUiLq+MmqO+g9sip7ll7sEBzD1ApJk7btq+kOvda9Q4E7lk0qq2lGrgInpdXN\\u002f\\u002ft253s\\u002f7501NmXR9KzxWXF4O9dSZxAJfA7fEUj3PFEDgj6\\u002fZHq5e8dnxXEFd6ud2nuyePb4jpfV\\u002f3W4gLspEpJjMrp77+0sIlw8KWT2pJDZBoc233Dqgv5MmblQYw9M5JQQF0VJ4mIkCXHSpDT5CHXgImEDDikc56eoCgCEKwHrJVhGh1gCAi5wP4tYO8taMMz7sgyegEDbT7Ks25oPUjyPu9keGEmKpjusW1B7D9Dlc8NXDVLcXUn3NYGERmS791X36cJxsYyWu4n+hT7gRK22wjIarN3cVrTjLEOVci1oRYd2IYQTAQiAtrEZZT\\u002fanwX3kQlRNgeF41hWXCTiUsnE\\u002f75x4f99tema\\u002f363cEx6hEreYjCX1LccL67a9Nzt0cFo3\\u002foiIzb8oUVT3\\u002flj\\u002f6r\\u002ffTdteCIL7P78svJGzf2XTx7emQh3mmHOlNUarXaT1d6gMwLA1lMXQhQyuVgYH652Su2Lx2V8v\\u002ffkv9fuLGvQiATksaLKkyXVY5JiTpZUu97H5qDOVdabrHaj1a4xWYCFTcfPy8RCuViUFBkcLO97iRPHw2TKp2XKv9lte62mH23WP83G9+22\\u002ferQDR4ysV84A6t9TRMyTjmeE3zdaqcbJLKbhKJ5es2dVvNPBBnvzEXO5ZKCjAWH726NDQ8Ql7RmlmWt2KVs8k5YFh1\\u002fsqxF33InwzQJRJOVqg9xou280rfc5SmL+09n31konicSL7BZt7Y0zBGJ52O4wmE\\u002fRTlyMTxIHvRPbsue6zqevkItkvx91Nynj25CHQAAoLNb3zyz580ze5KUIVnqiCRFSJRUKRUIpIQQxzCKZSyUw+CwtdjMdWZ9mUFTqGtstHb8fERg+BuTl6Qo++\\u002fYu9fw6+Z4RrvvrO7gWd1BtTBifPD8Meq5MtKv8YQnpaa8NRVvepXandgZ60nNjtPa3dNCl8+NuA7HAvA4ysMTWHAilqFrUOuQhyDwhVOHf7\\u002f5uMtS16Q\\u002fdaGqHRFn\\u002f+kSRB1bPD2ra6Iiw7KnC6q2H72492Rxo8b70C3gyHo+9Y3Jo3CT\\u002f\\u002fGzXBQycdfUQyd90r1DARzH\\u002fnn\\u002f4rT4o5+uO+SgvMxS2OzU1kMFWw8VRIYql88asXzWCLWyuw9d\\u002foQ\\u002fOxGQ\\u002frbsP\\u002fT5JdPL3Tt\\u002fUvpn6w8xnEzuH687OCknof25wH2nSpDSstNHJ\\u002fVCDndfKASqicGzJwbPBgAjpa+2lDXZ6hptdRp7k5HSm2iDmTI6WDvFUjRDAQCBkQRGEBhJ4qSYkMoIuZSQS0lFkEAdLAwLFoaHCMNCRBHdjxHpHUjRdNTkG89Edj0kuGOYx1CfdUC74iNPN6EvreV3IejMuUGKJveO4N7lcwMnwlCTE3fdCuOIR1ww9+BxT3mrm3TpuJAMD3bupi88\\u002f\\u002fL2ZX0AoNyLxGJ4cEclH9zAMDFBJnLz9iCp5\\u002fsbMSFBIQppqFIm9fZbNis7+Ycnrv9i+7E950q0JkuQVDwsVPXQFVNDFJ0bIN1+2fiEiOCvdx7\\u002f9dAZDLD0mLCHFk\\u002ftVCJ4ADBYbLe+u4ZreWXNDueLexdOum\\u002fhZADIiA1\\u002f767lH24++PmOowKCGJUY\\u002ffUjq\\u002fIr67mCe02L\\u002fjb393nmuz+dL55eMfu66aO4rj6FFIrmCEVzKMcZbfM1lOO01bpBLFmBtvIDkswEAIq6AEChqhRL0Y4igLYk5i5IMlUe9CoAKFTv6DX3mfT\\u002fJYgEkWQpt82lmWmacpwjBSO4rm5CXFqOQ1NFpCCb62LoSu4mADjsh53ZV5Sqd7maOEBrHdou0\\u002fl3xpTqT7TNqxz2wzbrHyzQBB4plt4gVTxMuM+D9lzX8fQh1ySPzG2p+bHoFOrgUKJvLtE3o9YuIcSJt6YsWxCLJgQbmvj1cHVKs9P5QmOv\\u002f6vu2x31PwxXTlwcfXdnZfdjLVv\\u002fqPmE8b3uwAXD0nsb11Zbim6I\\u002f7sA97KokIenDxFLbzQb\\u002fkfL7iYEw1Hf0GbJ9Cyu4A4Am\\u002ffntyO4I4VVMQwWTcvkWvyBZeGvwwUf\\u002fbK\\u002ftgldddjTdE3I6xQmC\\u002frw1r6O5guh31G3CH3YvUMEDINbFk+YMy713Z\\u002f2tlMxta5Jv\\u002frXA19sOLJ4euYtSyZGhqAhP\\u002f6DdW1Sa4DQ55dML3fvsEj13Ilp2w5fcFlKq5uffHvDvx5crJS5aVgujp6reOljN1WOJPB7V3SQ\\u002fqvXkJPKdEVOuiIHdQxeOjWWQELqAADAmbA10HQpYpGnOyBJw3E8zFkoz09w3xkAAkxXzw0vijYAAGDuD6Q47ismDpHkvMxSd4vOHxdGuP2pbGtO5A5g6Fp3A97hF03bT3A3CUE6dHJCESPCgSO4M7TbnGs\\u002fYds\\u002f7nK93vnKPa7Xz18z9\\u002flr5ro2ASA1KvTVmy7nWlyEKmW5bz\\u002fm2vzusetcr2+ZM\\u002faWOWNdmwAwZ0TynBHJXAvCddNHtS92q2QS7sf5YtrwhGnDE7iW1KjQZRPaZk0SI4L9eZ\\u002f+AynIEQjH2607GaoC9fmHUDQLwxQso7dZfhdJruK6rJb1zjxLQuEMrp2LSLJERleZ9P80aB\\u002fFiWiBcJzLRRBxpCCHcpwxGz9QqldzduouBBFPEMNoutJqWSd3F9xt1s3cTQBgGTMAAODIIh6aKnXYT3MtnaUL72zUv+ywH1ao321\\u002fdqTnuo6nb3ll3EIM4Id2NfeAECdXvz\\u002ftymw1ujhpyNJxdFWjrara0vbzDAA0Sxcbz4gJGdfYIfn6wxurP\\u002fZHbXdRbMz9ofw\\u002fndqFh6cXEEqvIoUjdU2LjNpHrabPbJbfbJYN3H\\u002foDv7xx6mCt\\u002f88cKqshhurOLBIjAnJTHK7ve48dtFmp7gWFzqj9WCu2\\u002fLYscPjIjuZ\\u002fKFJY7zj5R9f+GhzZ+XggGTnCMibtI\\u002fVo\\u002fe6FoVKEl1RD\\u002fu2e4cUwyLVrz+67MuXrp89LrWd3rM7qHU7z6z8vy\\u002fe\\u002f3mfxdYzKtsAp28vmT7hketmKty19aPnKlb+3xerfz2QW1hjsthZFhiGbdKa9pwoevrdjY\\u002f8d63R7LYO4Pblk4Z1VOCap+fAfSTQ4Blq0LRbHoDOCugE0bn2fYCfmX+9JXDvnxDuEaAMXc\\u002fQHYuPtHt0OSFIB9+Zmp1QjjzuJk7EcTf9AcPc5ulZRo+E5\\u002fPw+MJq\\u002fs5seIdy5LedMyxls25y2A4DABLo7T8YrpApHgcAg\\u002fbvNssfl1Ie0TbL70bdswAgVTzsmVKGi1R+n0R2M8va9C230XQ51yUPehGAtFk26jUPUVRrUALDaCjHaZPhdbPhLW7jTiGR3wEAFuPnVvOa1g5hHRbTl1bzOqTlpfB8xmL+ymW02\\u002fbrWm7o5tXXhXe2mn8AwAkizq3Oszd6rut4+hAcw\\u002f45\\u002fvJXJ1wh72qJ1A6RkoJHsqdvveIuXm3n0vFT6EWD24y6k6ygyZ0qbapzNK6rfK8LFbGKjKd3Nfw8N6JtapqHp8\\u002fR1I1yvrCZf7XBr24+AAAQSZahJj\\u002f48VDu6fKaT3cdVUnF09MTZwxPnJ6WoJD01D2xh1g0PYtbi89kse85UTR\\u002fMroeEAC2HS6g3GsSLp7eufD2spqWR15fW9dsQB0AAJAQHZw6LCwpNjRcLQ9SSNQKiVQilIoEYpFAKhbUNOlXPfUVuk\\u002f\\u002fw1MrdPRWevSh0L39jcykyP88vKS6Qbd2x+lN+\\u002fO1Bu8Jah0U\\u002fe2mYzuPXfz3g4szEiJQ99CmDy+ZviJMLf\\u002fXA4v+9uZvdk5WIp3R+uXvR778\\u002fQgA4BjWzjzuslkj7lg2CbXy9CIY1rmZ5gDAOmiqkHbkM1QRwzSxjIZlNCxrYlkrsFaWtQJr88x6wdPTsO7lAXGic4+sGB6MmrpAT54bGObXukAM\\u002fGrWOXrmuAgyDSfiuCK7zfSVRPkCp4knlM38A3dbILqMu+kVlnFb6W83\\u002f2w3\\u002f8y1dB6GZfSeKap4eDyh6Wqz4V2T4b8YJsLxCBZolml2Xi8iyVKhuOMT2BcS+d00XW0xfabX3INp5TgewjDNzqzuEtlNUvn96A4eyIP+SdM1dut2XfNN6tCNrjoKAuEkpfp9g\\u002fZxm2WdzbIOMBIAA7Y1VAUJqO8UEtntdtt+u3W7QfuYUf8CjgczdD3L2hSq\\u002fxp1L7KsM\\u002fYcAIAgk0WSq2yWdUbdixbjZzgRSlNVDNMoEI4VK28y6f\\u002fJedfO0YV3FkmWWM1rtE3LXRYMk+BErFA8Syp\\u002fkLuuqOe6jqfPWZU8amZ08ntn9\\u002f9acsbBBOz5KEYWdE3SyJvSxqqEHaRHG4Kgz6WelBjdZtSdjFTNRE3tsqH6I1vrypdOs79x\\u002fWj17GBh5wadPDw9hyzoJdTUbVpMltyKGudrrdm68dT5jafO3zFz3ONXTHdv2N9ZMCn9ne93c3WfzQfyvQrumw+c525KxcLZ49O4lvaxO6gn39ngKQdHBCtuuHzsrPGpEcHtxUQMFGQeWWus9q7ENXd2ryHSvf2TmPCgh6+bed\\u002fV03afKPp9z9lj58q9iqXVDbp7\\u002fvnzW3+7qp2sTUOQvrpk+paJ2fFvPnHl8x9t1ui9jLV8qe0kgd+7cuqNV4xHHTy9C4Z3bs1ol2FZo8OyxWHd7LDtBf+STfP0Koz7b66f8eCXwPCuP+j21rnR8dJqAIDAVfDqheMSyW6x6F9xbdqMX5CiGQLRrLYW7lh0LzNUmWsTw0RC2fVtbu+wLHJuBAKWtWCgQq08PB6IJdeyrMVhO0bTZTRdAxjgeLBAMEUsWSmSLIVOZjdCkAf9Qyi+zGr60mE\\u002fQTPVOKYWiqaKZTcJRbPRpt4hlOqPtE0rKMcZneYOVfCPcKnYg0iyRCAcazF9YbftpqkKAAonhuHkMJHoMlFnag57QAQFf24xfWM1\\u002f0xTRQzdIhCOk8gfEoqmWUzfIItRlOq3LMLxVvMPNFXGMM0EGS+R3yGR3UNThV0pE8ShU+9MU4XAMgAEANs65GCBZS00VWgxFtosm4LDtnNrPvdY1\\u002fH0PZESxb\\u002fGX\\u002f74iBnrys7+WVmQ29zF5Ao4hmWqI6ZFJs6JThkbNqxbd4FBTQeCO8PS5aZ8xKgg1fGyTgSiFuiPFhp8ZgsiMYFaGE6xlN7RTHtb4UKxjs01n9+Y8Czq4OHpI8SyO1FTtzlZWu15r1s2thMXWj9BIRPPGJu8\\u002fchFl+XI2fJmnSkkyE1QqKzTnCt2S2E5b1K62CM0tR0+XnuwvFaDGFctGPPgqulCMmCPan2OTIKqh2ZrV3RAo8ktg0SHDJHu7c8ISGLexPR5E9OdAe8b9pxF0oAAgNVOPfP+H9\\u002f986ZQVS8Jdv2fvrpk+pzxWXE\\u002fvnrL578d+mXbadTnAUHgc8al3rJkQmqcjxqGPL0I1gsJNFi7zfS11fgey6A3dp7+gzOu00WHhTQ9IAEjO0wXgNKb54bvhGmBp7eOSyi72Wb+hqFc6SxoU\\u002fNtYvm9IvkdmHsyetqRbzX8z2HdxjWK5Hfj7kULPWEZg680ETw8vQBBxsuVL6FWbwRHHOFuSuUPSuUPujZxPDgs2kvxAKFoutCPAtFi6TVi6TWoFQDDpOowt8o0LnAiWqZ8TgbPoY7uQkpkt0tktyNWddifiAWAlMhulshuRq2CLKQrOt91\\u002fr4zRRVoG5cARgYFfyUUzeAUq2ApxwVdy80MXW21\\u002fCqR3cHdq8e6jqdfECKW3ZUx8a6MiVq75XRzzdmWulJ9c4VJ22w1a20WC+1wxr+TGE7ihIwUBAklKpEkQiKPlQXFydUZqvAMdbiE6EqVrKFGB\\u002fJWrbXUxqBL2pMVozC\\u002fZzJZYLfXf49aAQBAQsjnRd44UjVTiIsBwMHYzukO\\u002flX\\u002fncHRgrS8YDjeaKsKE7kNR1547tcDBwoB4OZbpt1yq8979Ndf7fvm6\\u002f0AMGlSyr9evRp1AwDAhYLanTvzz+fXVFe3mEw2lgWJRBgaKo+OUQ8fHj1mTEJ6RhS6Dw9PQDlV3hre7iItKjQ5IgQxDggWT8\\u002fmCu4Mw249VHD9wrGcJvDXkdaUcC4WTc9CLO1gp+j1u84gxlXzRz9+wyzE6AuaGhiPLgopKsTUt3Qlyslo6YR6OHS6d0DgDHi\\u002fY\\u002fnkn7ae\\u002fHbzMYu7fKzRmz9Zd\\u002fCZ2+dxjUOZPrlk+gkmiy2vsG0iE8exqFBls9Zkd9AiIamQiVVycVpCeE5K9JSRiWFqOWdXnsEMQzeYWm6l3cPuUDABjqkwPAgwCYZJMUzKgo2yHUSb8fQq\\u002fsWDc8BAyEInBPfBem705nFhmFimet\\u002fYfA3Luh6ZKavxfavxA4JMxolhgElY1sBQRR61UkEgXiBWPIEYPelCrhseHl9YHRfqdK9a7GcYxiwSJIbI71DLVqKNeAYRFuNnLGuWyZ8Uiue4ezBSkCESz7OYvqLpKncXz1BBJZTMikqeFZWMOngCRAeCe4O1EjUBpMhHoSbfnNMdrLd6qR6jEATfnvhyqCjGZRHgolHq2amKMV+WvuC5S652z2URNyDG7tPcbHzzjS2HDxUhdoPBYjBYSksbD+y\\u002fCADJyeEff3pHb4Zl9DLXrvqgsUH\\u002fx+YnJB6xgTydgqHrrOYfpIrHUUdHlDag80zT0\\u002ft98SsfTBwRH6aWN2raArX+PHAeEdx3HG1T5AFgWKR6ZGo019I+B3NLTRa3pcHBQdL7r\\u002fE58eaJ1ohOJfZP4qPRlKwVdZ2O1TKYbUh3tc\\u002fQ6d4BhEwivGP5pMUzsv7+3h\\u002fI6pBN+849tGo6UjZzyNInl0x\\u002foKCs\\u002fqHX1upNrbpMdFjQK\\u002fddkZ3ChwsMdVimydi0lKHRoEIMDxWIZ5PCyYQgDSeSPAvTUfaTRtsyxMjTs2AyaBNtgSPg+kun1PbBem70\\u002fnERwlGy4K9NmntZhjuYZ2mqiKbQZ0wXQum1kqB\\u002fdlguFbwtghGIF5CiaYixs3AzSPAMERx0fXH9UoZtTTpisedVtTwKQKll17q14xlEOKf6fBVsoKlSAMDxCNTBw8MTCDoQ3JvtaNQtAKTIR6Im3+xrROs1AwCO4dfFPcVV213IyKBr4576sOgxB+P2rHtGuy\\u002fggrtWa37koW9ra7XOTQyDsDClQiF2ULRWY9br24a56RlRg1htr6xsbmzQo1aeLkFTJRbDm10Q3CuatYhlUvIwxDJQwDHsiqmZX\\u002f9x1GW5UN5QXquJj1I7NyvqNMWVTS4vACye1rnkOfnFbXVZncwZn9apjDTNWs8Ed\\u002f2RtLhwxFJWg87NdEhJlVtvd8jQ6d4BR0Sw4r0nV9zy4veVHBGZopkTBVWzxqZwGg5d+uSS6XNadObH3\\u002fjNpbaHBytWP3sNX2iBBwDM2qcQ5RHHw8TKp4XSqzp8CuDpZTBcwTKcmw+n+J5fsPZOpSkfrOdGnxwXKZqsCPvLanjdbl4D4JEj0h1CMEKseEwg9ndpGobLAQiAtvJIBJkmkt3a1oKHxz9ajN+41HYXjYbVvOA+iCEFw+223RbTF0LRDIJsi+dj6Dqz8X27bQ+GiUSSRZw9eHh4AkYHw45mGyq4BwlCZaS\\u002f8+FlpnM1lhLUCjA+eMEwaRpqvUSoKHq0eu7RZrdUXBp7vWdWmW6y+qMdTrVdIhHeedesuZdlKRRtEYJarflMbsXRIyV79xZcsWiUyz74OHG8DDXxdBWW7UruAgDQmNA4prSoUMQygFg8I4sruAPAtsMFd1452fl617FCrgvHsCs6Kbg3advC551kJKAqW\\u002fvkXkRDn\\u002fonwUHS4CBpi67tqbuhxVBW25IQhYbxtkNhRSNqapeh070DEZlEeNeVk1\\u002f4aDPXWF7baU15sNInl0yf8\\u002fG6g826tkfoR66byavtPABA2Y84rH9xLTgxTB76C054CXnxoAPRkCfgYJjbZcswnbsRsWwnomcG67nRh8eFYWJgLa43wTAZYBKW1QLLYrgCw4MIIokQjhWIZhDC0e67dgiBE2EM3RYMwTDNHC8Pj7\\u002fYqGLUBGCnyligMT8WW\\u002fAMRCTy+23WLTRV1NIwjSATcTwEgKHpeufEJIZJFKq3CWKgxvnx8PRzOhDcW+z1iCVSHI9Y2uFIs5si4ESAi2aHr0Kt7kwMuRwR3AGg1lIaQMHdbLbv3nXe+fqFF5dPmIjmLVKppDNmZsyYmfHIYwsEgsH8C3TieClq4vGAoRsAw3A8DABYxucjDUN37unIhcnmlpdZLhaGKmRcy8AiLlI9IiUqr6gt8cX2oxddgvuOY275ZMZnxYV3UhtyBXK6UCmkiKUdWBaOnEPzVvVbRqbG7DruNkVxMLe0U+rh3pNehtftMKS6dyDimX\\u002fJMNAqfPYovX\\u002fJ9C0UzWw92DqeAQCCwGeN45c78AAA2M3oMlOp+i3\\u002flEdgGR1q4ulhcDKedpxxbTKUl6CldvBMEd4Og\\u002fXc6KvjYugGY\\u002fNVrtKppGimVPUGTgQsSwNOproJ7r4z1fDwdBaWpYBl\\u002fa7QxzPAwPFgddifFtOXdus2iiqiqQoMIzBcJRBNFgqni6XX4UTnwqp4eHj8pwPB3Uqjixkj\\u002fBbcTZTuvN6t2rKT8cELOoyRDxcNUwpC9A632fs6a1kOdCKJcPtUVbU4HDQAhITIPdV2LoNbbbdaHadPDy1p7KX\\u002f+\\u002fngngsAcNNdM2+6aybqvsS3n+759tM9ADBxauorb12nbbwMw6TqiMMA0FLXuXDsLqCUDPh0zItnZHMF99LqZmdWmbom\\u002fYWyBk5DWDIjm7vpDzIJms7S6j5j0T57ThZxM3L0c+ZPTkfUw\\u002fU7z1w7fwyO+zU01hosJ857qcbRDkOqewcidqptXbkTJZ\\u002fAnUPvXzJ9S02jzsK5QqViIUngHD\\u002fP0IWyH+JuEoIMUjiRa2kHlg+h7XVIQbbDstG1ydB1LGvEMH\\u002frG9MOtBx9OwzWc6OPjos2tdzkUtsFolmy4C8B6+Apu1OQgpGUbZ9rk3bkAusATMBpwsPTMSLSi+ghFqRhAT1defobGKaQyh+Wyh9GHTw8PD1MB\\u002fdWO2NBLEqBv2kuTml30SwqCuAYPjl0MWL0SrxseJ52P9dSZw1kILbN2vp0KpOj0lLPwbLskkVvWiz29IyoDz+6les6d67q4Qe\\u002fdb7+16tXT5rkFp729JM\\u002fHztWIhKRf2z+G1cyqK3VHth\\u002fMfd0RUlpo1ZjcjhoqVQYHq5MS4+aMzdzzJiEtrfgcPFi3YnjpUVF9SXFDZWVLSzbuvhx8RVvuDcEALj2ukl33T0btV7Cbqf++uvsoQOFxSUNWo1JKCTDw5WjxyRcsWhkYmIY2prDd98e+PKLvQDwy9qHg4NlAFBa0rhx46nTp8obGw0sywYHy5KSwsdPSLr8ipF+qiQ9ikA0hbvUlCDTcSKS42+FoetoqhMPPC7kYiE3q4xg4Msl8yamv\\u002fndLpu9rX7X3pNFNy0av+eEW1SOQiqaOdbL4K991Eo04Lq0xt\\u002fHJIvV8dEat3tLP2fa6GSFVGQwt4UwV9Rp\\u002fjx43s88PF9sOEzRDGptlyHVvQOR\\u002fBI0yX5YsL+KzFCg9y+ZvoXA3X4vDCbr\\u002ftMl00YlcY08QxOWdluoSgjGcjfbh7KfQE08PQwhyHI3sJRtv0C80N3oE9pxDjX5ZrCeG31yXHbzOtqR79qUBL0SWLUdAEjxTDC+79pkWZvDfkAgmtXWgofHD0LktzQZPmdYt9SR4crHuZs8PH2C3rKt2fi587VKukwtu87dz8MzIOlgNGBn0MQCYsLfNBenNLtQE0CGYoJK0J4O6yJEGIVYDI5AhkwGh7TKEzXVWrPZJpX2huyOYVhySvjZvKqy0iaWZTFOJdZzZ9tSHp\\u002fPr0EE9+LiegBITolwSc91dbo339jimQ3GYLAaDNbi4oYtm3MnT0l9\\u002foXlIhH6La9fd\\u002fyvrXmIsQucOlX++mub6uvbFmA6HHRpaWNpaeP6dcdXrBx\\u002fz71zOtTKm5sNwcGyb77e\\u002f83X+13SPwBUV9urqzWFhXWLFo9qa913KNSruZtSxWNCiZepI5tlg1HzAGr1g2C5lCu4a83opTfgkEmEs8el\\u002fslJdHDwTNlNi8bvcc\\u002fVMG9SulCAnqId4plSfNexwruunNJhcWOGYV9YvblsQCW8FpLEwqnDf9l2mmt887td2SlRcZFqrtGT0xeq1+7IRa0dMaS6t68oKKuPDFGqFBLU0RF2iv5203GuBcNgdHrA8q0NAnr\\u002fkulbIkIUQgFpd7TNbj797saVl42aNTYlITpYIRURA38Gl6crsA7WveomjvufWImmbLtRG08PQwjGACbkFj51WLf5L7g7rFtRky8G67nRR8flsLU982J4EE4mtPkCBCkcjxPhDN22QtRu+pIX3Hk6C0mEJ0dsqNP922I\\u002fw7BWiSArIuhxmWgK2o6Hp9fRmTcYra0xWxJhjruTh2eg0oHORXkUu5f4J7jXWEoarF4SlYwPno+afKASooqPzSPcvjvExKjDwhSNjQaKor\\u002f8Yt8DD16GtugZ0tKizuZV2WyOmmpNTGzbKPDcuSrX6\\u002fPna1yvAUCrNbe0mAAgPb1tEkKtlhZebA1yFIkEKSkR0TEqkZBsbjHlni43m+0AcOhg4bvvbP2\\u002fJxe59nIy97KslJQI52ubjfr8s93O13ffM5sk0fw56RnozIeTAwcKX35pPUXRACCVCkeMGBYaprBaHQXna6qrNSzL\\u002fvrL0eZm43PPL0P3dKe52XjwwL5vvt4PAGKxID4hVCwWNDUZ6+u0FMVMdJ946D9gPkbwSMEr\\u002f0mJCCmubwsiNlhsDMviHQqc\\u002fZvF07O4gnteYU2LzoyU01zc+XwyADAhKx7DgDNBAyXVzT9uPXH9wvZCmVp05mc\\u002f3HRyQCWLcHLXlVO2HizgplY3mG2P\\u002fW\\u002f9qw8vSYvzOYV58Ezps+9v6kKs7lDr3j5h84Hz63bkTh+dvGh65vjMOJGwg59jJzqj9cXVm4sqG7nGESnRkSFdvPMMVnr5kulbSAKfPzn9j71twa0Oiv7xzxM\\u002f\\u002fukzWlMoIBVSkUohSYsLy0yOnDMuNVTNL5IYdGACDBOzbNtVwLpHNbaD3bzOWU6NpzfBcIVANNdh3eKyOCwbGMWT\\u002fqQCp+xHGdrv39\\u002fBem700XGxnOzqLGNgqBKcDPgaI0Iovc5qeMe17bDuomz7SNF0Thseno4RC9ITQr9GrTw8fY3RdgA18fAMfDp4whdgYpt7pABHgWmPk5odqAlAJQhLVoxCrT4Q4WhOA5tHQvlusvLqCR99uAMA1q09ptOa7753dmhoj2sW6emteUiKSxrdBPez1QAwbXra\\u002fn0XC87XsCy4tNbiotbVkWmX9gUAkUiw8uoJRUX1Cy\\u002fPGTMmgZto3mSy\\u002feffGw8eLASArX\\u002fm3XrbjLAwt+MaNy5x3LhE52uj0eoS3JcuGyORCF3N2qGuTveff290qu1Llo6++57Z3CUC27edfeN\\u002fW+x2atfO\\u002fFGj4hYvGd22pwdbt5zZt+9CaKji3vvmTJ+RQZKtgXhms+3ggcL4hFD35v0CufoDEl322wqOd1GzyI6N2HrmomuTYdmyRk1SuHdZf6AwLjMuMkRR12xwbjoo+rstx7liVkJ0cFaSl8w8HRIcJJ05JmW3e3aad3\\u002fc06Q13bpkgmc+67om\\u002fbpdZ9buyDVeyjIhFQvFIrJFF+C7Sg8RJBffs3Lq61+73VerGrS3v\\u002fTDirkjF03P4mqIJov9WH7Fxj1n959uq7cWGaqsa9K7Ntunz7vXZqeMFpvRbDdabCazzfW6oQV9ci6rafny9yMyiVAuEcmlIucLmVQol4hkEqHAYwaxHSiaMZptRovNZLYbLTaj2Wa02J2WFr3bgTgo+rP1hzgf1\\u002fpCLhXKJCKxf9I5ADgoeuexizuPXRQKyFHpMROy4lLjwpJjQ8M8pE+TxZ5fUrfvdMnGPWfNVreJcAyDe1dO5Vp4oNcvmT7ngaunH8+v9P8PtjuoZh3VrDMVVzVtOXj+7R\\u002f2zBiT\\u002fOh1MyNDlWhTnoEMhoezdFv4C+3wa\\u002fUGQ9dY9a+iVp5eQSi9iiu4s6zNavivVPUGp4lXWKv+X6itXQbrudEnx4UTMQDHLm0xxuarRfL7SdF0nIjFMAkEqBilSHaXzfQly7ju86xZ84g8bKOfJWF5eHh4+i1Wx3mKdgsn4uEZHHSgC4gIiY1xFxqYtqSovqBYxxntXtQKMEo9G\\u002fN7zCHA0RwvgY1wB4AVK8efPFF25EgxAOzYcW7v3oI5c7OuWDQyKyu25wKL09JaA8ZLSxpmzEh3vq6p0Wg0JgBYtGjU\\u002fn0XTSZbZUVzXHyI01t0SXDnRrgDwA03el\\u002f\\u002fJZOJnn1+2XWrPtDrLSz7\\u002f+zdd3QbVdYA8Dsz6s2y3Hu3U53eeyEJJLRAILQAC2HpbT9YYIHdBZaFpe3SIUDooYbQ0hPSe7XjuPdeZKvXKd8fcuTRsyxLji3LyfudnBzpvpFkSTOamTvv3cedOll9yaK+dCL24eOPdlksdgCYM3fYw4+gY10XXjJKr7e++852APj8s31LLs3t3nHebc+e4shI5bvv3xZxrsiPi0wmXnhJP\\u002f\\u002fZ\\u002fUUs7bHbvkA0XhObh0b9MG9Exmsb9\\u002fIjh8pqhnrCnSBg6cyRH\\u002f98yB35futJXntfpkt1u2v59L2nKhhe+p7j4KuNx77fdmpkemxKvEYuEVpsTp3JWlrdWtei63okgIAiX37w8kNnqr\\u002fa6FGaI5Qtn5+790T5ofwqftBJM99sOfHNlhNSsTAyXCGgSJ3RqjdaWX7vdICr5uXmZsU\\u002f9+FmftC3Qfl4HTRz+UMfmix2\\u002f7sY1zR1vP9Djx0iREJBVlLkJ\\u002f+4EW3w9Pj\\u002ffjmUX8Wfb8A3mmHX\\u002fHQQjZ5DUaRCKvryhVuiNf5ewXU46SNnqo+cqXbdlYgESrlEIRMLKdLmoI1mW4exx93fNQvGThiehEaxoG8yg0sTJvvw6etf\\u002fHgb8n79xDDsH0dLD+dXP33nogWTs9FmbMgSiKc4LF3JR9pxgnGepoRjeIugWKbe3H4by+Lz3sEhlCykBJkM3XXB22H5jhLmiuW38pZCWfXP0o4TaNSnC3XdGJT3JZRe6bBucN9lmRar\\u002fh\\u002fuuyhCSBBhJBlOCUdQogki6eUE6VfvIoIMk6qesuiecEdYttXUerlMs0Yg8jUAkY9xFjgs3wvEs4SSBWgbhmHYIDHZvCQPMewC0FvCnUTLy9oYtINhd2f0+63dFiOAGB8+Hwn6QAKJRNhuU7C6uMp\\u002fo1E\\u002fEATx3AvXfvDejp9+OsZx4HQyWzbnbdmcFxunXrJk9JIluVHR\\u002fd\\u002fVKylZI5WKrFZHRUVXGb4zZ+oAQKORj5+QJhRSTidz9my9O+FeXt4CABKJMDm5M9IriUQ4eUrG9m1nAKC11d8ub35qbzfv3lXkun3n6rkebecsu3zsJx\\u002fvttmcWq3p+PGqKVMy0CV47rt\\u002fIZJtH8oEPVWb8S0tKnxsStyp6kZ3ZE9R5Y3Tx3YtMTQtnTXyk18OuXNZDrprKyZJ4tIZw913A5WRFPn4qvn\\u002fXrsdiTuc9MniupPFdUjcTSQUvHjf0smjUgAg0IzwICIJ4sX7lz3wnx8LyrtWEjer3Vnb1IFGAQBg5tj0x1bNrw6wrvrgfLwcp+s5s9wHDietN3UNLe+J0WzzP9veK4Zh9SYbw\\u002fo5HswLm4O2OUytHehutLur5o7+yy3z0CgGAEHfZAZXQXnj+j\\u002fyiqo6L8\\u002f3jcXmeOa9jVKxcPqYzjFw2FAnlFzisHzPj5jb75ZHfE4JsvjBTpzTYf3RavgXx+rQpvPCcZyZY00s3XlN0Y1laghSQ5BKgpD3Uy9ghmPNHGfg2K5jbAAAYFm6HAglQSoIAh1BG2IE0rAXTNqV\\u002fJBV\\u002fzRDF0mVTxBkGD8OAAxdatM\\u002f5wy8+HhorBv9b1Del1CyUCRb6bB8gzZ4xTk5ro1h2xi6FKw\\u002fWw3PiaTXSFXPdP9yuxPJbnLadvPHQLBsq6ntaqFksVh+GyWaSBBofzUAjmXqGWc+bd\\u002fvtO9j6XIAoHB9ZAzDQonJ5tHvEMMuGL0k3OWCsFa7R2Kl3dFVpa4nh7Ub0RBAuiI3XBSDRnuG9KwHAJJAU\\u002fDnTyAg73vgkksWj\\u002f78s32HDpa5Ju1satR9unbvF5\\u002fvX7Bg5K23zYyNU6MPOw8EQWRmxeTn1VZWdPWkcNWTycyKFQjI9Izo4qLGwrP1Sy7tPBiqKG8BgMysGP4kq72KPne1wFXPvR+dPFnl6vealh4VHx+ONgPAubLyrgsJeadrfCTclUrJrHM9\\u002fS9yf54\\u002f5Z61G9x395VUFTe25sT1WG54SEiIDhubneg1PzstNy0iTI5GA3HVvFy7g37zmz3+d4iOjVC+cN+y0ZlxADA2JwGZaTDEyaWid\\u002f567XNrtuw82lV9yLer5uX+3y3zBBSZFh8hEQlsgaSVL7aPN8ikYiEaCoRSJr772hnXLhyLNmA8Qd5kBkVDq\\u002f6ltdsPnxsecZ4Yhv3nh5vXv3qH3L\\u002f6cliIE0oWU4Ichi52R1imzti6RCRZJhDPIgUpBCHmWD3LNNGOo7R9B39KRrHiHsZxgnYcdkd6xdJVNuOrHGfkWCPvfxOA952Iuf2uczcJgpATpJIgFASpAkJBkEqR9GqhpMeZn5y2bQ7reo41AmfkWAPHmTjWyHFmdDkAAOA4m6Fl7rl7FEEqCEJ57uWUBKEUKx+mBJm8RwwmgXiGSHqNw\\u002fojP+gwf+mwfCsUzyUFWSQZyXFmjm2l7Yf5X65APJMgw5zW33mP61GQ142gGaz3JVP\\u002fhySjbKa30IZecbTD8i1t3yWP+MaflVAW\\u002fqZZeyPtcFewAQDOadvstG0GQkQJsgkynCBVwNk41sxxOpau4he1xzAMCzUc5zTb+\\u002fLDi2Ghr5eEe7QkucpcwI8023o5qau1FNdZStEowES\\u002fp0t1sXWr2E4R3tMTEREKH\\u002f2jtVqTVttLP8Hs7NgX\\u002fnVtS4thy+a8rVvONDR0AADDsFu35u\\u002feXXjPvQsvv8JXFfJAZWfH5ufVNjTobDanRCKEczOmZmXFAEBOTlxxUaN73lSnk6mp0briXU\\u002fhB3cxdNdVhH5UVNjZWzA9LdqzxYPm3JfS0uKri31WdmxAFxJCB+04ztAVHKdHGwAk8jvRkB9mD0tbnJvtruTOcfC\\u002fLQfeve1Kz6WGnmWzR3pNuC+bNQINBe76xeNHZca99c1ery\\u002fBJxEJrlkw5vYrpyrPzTcgEgrGZscfKegaehz6pBLhvx9Ytv1wyXs\\u002f7Ktr1qHNPOkJEQ\\u002fcMHt6bmdPVZIkspKj8su8dPX14WL7eIPpnmtnTBie9OP2U4fPVFvtTrS5Z1HhisXTht28dFK4Eh2ChnUX5E0myE4W1z362gZ+WX+ZRLRk+rApo1MzEiKUcolYiB7m0QxjdzImq7213VRS03rgdOWxsx4bqc5o\\u002fXrz8dVXT+MHsSGLlKlfMWlXcJy9K8Y5HNb1Duv6rkg3ItkNUtWTNtO7ASUfWbbVYf0ZjfqF4zgT5zk6lhJk+Ui4M85Cp\\u002fU3NOoXhmP1HOiBN25WJLse\\u002fMh1Bo1U\\u002fRLLVNGO4x5Rzum0bQPY5hE8hxRkyMPfdlg3+JlwD\\u002fK6EURBf18c7bCut5s\\u002fZZz5aJPfWKbZrL1RGbWJIHsZzUwQEnnEV5aOe502dAwicA7GeQYNYhiGhTaL4xjL9ecQZwwLHeiZGCJWkoJEqsxnGY6hiJ5Lcrd6OZpRCSNGqKagUZ9sLNpLRUSiM\\u002fW5LF029tbbZqHRcz77dK+fBWeio1W3rJp5y6qZp05Wr19\\u002f7MD+Eo4Du53+7xubAaAfc+7Z2bEAwHFcVVXrsGHxFou9qrINADKzYuFcYr2ystWVjq+uanN1J\\u002feacOc4KCpqOHG8qqKipaXZYDBYLRa7w8E4HLRzwDqWtp+bt3DHjoIdOzyux3hlNPjqWKFWh\\u002fjYXi84tt3Qfjt6IsTTt4Q7APz96gWF9S01Wp3r7u7Cio92Hb1z7iSPhYaaZbNGLps1Eo32n5EZce\\u002f\\u002f7bqiquZ9JytOldTXNusMJpvN7hQJKYVcEhuhzEyKGpeTMHt8hkyC9tl866\\u002fXIhHftr57LxoK3Hcv346GArRwSvaCydlHC6r3nao4U95U36IzWewEQSikosQY9ajM+Nnj0sd3q+v90bM3IBF\\u002fBPPjFQkFhz9\\u002fFI0OvPeeug4NBcXkkcmTRyYzDHu2sjmvtKGuuaOh1dCkNZisDrvdaXfQNMvKJCKZRKSSS9ISNNnJ0aMy48bmJJCBXKRUK6V+fqoURfq5JAAc\\u002fPQRNBTCBm6TGcSPt6ap45FXf+JfrZk4Ivm5ey7tbeSQUAkQqZanxmkmjUy+6dIJu46XPfnmr\\u002fwq9juPlOCE+wWDEo2Thb9r6XiA49COLD0gJMoHJMrHAEAonm2Dl9B2bOCdS6re57TtQNu8oYRj5JqPCTKCEgZQKuRCXTeC+b4YusTS8QDjPMsPklQsKcgkCBWg58sccHaOM7NMM0tXA3icqbFMo834mjTsRX7QK4KQyjWf2E0f2Yyv+v0e+QiC6LGnGoZhWJDhAu7YBazXhHsqEnGwtjP6fWPUc5C4S52lpMhwBI0CTNYsIdFjjl7oHOjcNRLK9zlkvxk7LmXsuJSSkqZ\\u002fv\\u002fhLTbUWAD54f+fcecOVSu8Z\\u002f0Bln0udV1a0DhsWX1BQ7+qEPnx4PAAMGx4PACzLFRc1jhmbXF7efO5RsZ2PP2fv3uI1H+6qrwt2nVmzmddnxA\\u002f8eRe78zGfasiyGF6iHceF4rkiySUEqUabz0OYTLLmzuW3vPddi6Hzqsb\\u002fNu8Pl0uvmTTKc0EMNSw1ZlhqAEWrQty1n6073dBVv2t6avJnN1zDaweCgMmjUlyl0oOgHz\\u002feDqt18n\\u002ffB4Atd92aHtGXCQ\\u002f6y\\u002fq8gr\\u002f+vjUrMmLj6lVoWxBRFDk6M85Vh6e7\\u002fccrHn\\u002fpp3YwCBzcC\\u002fcuRZv98+mPh9Z8sx+NAnz4rxtHnpvH+3zc+tjnZVWtAPCfJ66eMSEdbQ4NQd5kguDfn2zjZ9szkiJfe\\u002fQqiaiX47ru5k7IXDx9+Kb9XQmjinpth8ESrhp6l8Mxr4SSRcqojRb9U7T9ANrmiRKOlKr+LhB3Xm6hhKMIMpJj2zyXwoKBIORyzVqH5Wur4VUfXwFByMWKuyWKe4EQAQAlHAVAAb\\u002f3vk8X6roRnPfFOM+atNdxbNdQV5HsOrHiHn8qw3Cclbb\\u002fYdX\\u002fi2W6xhjZLeskqqf8y4YTYsVqofQKu\\u002fkDh+UbjjWi7d6QglSR9HKRbCVJJaNtGIZhgwQXcMcuYL2cmCVIs+QClZn2KAnyR8u3w1VTuvc3d7KOX+rfR4IAICIlkyOWoNHedK9dIxf0PplMP8rOjn39jZtuW\\u002fWhyWSzWh2HDpZdsqh\\u002fkp5JSRqZTGSxOKqrtQBQXNQIAFHRqqgoJQCkpETIZGKLxV5U1DBmbHJFRSsAyGSixESPMYZff3Xw4492uW6rVNJJk9MzM2NiYsKUSolEKpRIhOt\\u002fOLZp02n+Q\\u002fqLVNJZ2ycnJ851ecA3\\u002f+d6DbIv1uz+Ys1uNOoHh32fUDRFFfEl2tAfEjVhX957\\u002fT1rN5Q3awGA5bhnf9h2vLL+b1fOk4vRHsTYherq0SOGR0e1W62Fza21Oi9lizAMu5gVV7ecKKrjR+5bMbMP2XaX2ePT+Ql3AGhpN+GE+wARShar42vRaAAEfXg4KchQRHzLOPOdtk204xhLV3GsjuMcBCEhSA0pSBUIxwokCwSiCZ6PI8JiT3pGfBGIJvXhb+sbifJBifJBNHqhIUSym0TSFU7bFqdtB+PMZ5kGjrMQhJigYijBcKFkjlByBUEqux5ASNXxVV1P4IcBXTeEkoX+rBJC6VK1tPfFRLIVItkKNNqDAX1fAACcw9xxLz\\u002fbLgv7t0h+M28JXwhCKpRcRgnHGFvmce5aChxN2\\u002fcLJYs9Fu0ZScVIVc9KlU847btpx0HGeYalazlWy3F2ghABISdIFUWlkIJ0SjhCIJ5BUonoUwwIzuI4abYdsDhOOegKJ9PCchbgWJKUUmSEWJAiFeXKxFMV4hkE0cd9Vq8cdI3Rut1kP2iny2mmiWUtBCEkSaVYkC4VjVJK5ssl04ne0iDYAOE4h8m212jbZXMW2OkqljVynJMkZQIqVizIkIunqKQLRYJU9GHnzeYsajN+bLbvczJNBCERUclKyWyNYpVIgA5wdLE68rSmT832Q06miSRkQipaJp6kll0pF09HF+2jwd9SeDiL45TJtsvqyLM7y2lWy3JmAoQUqaZItVCQIBdNlosnS8Vj+3fD4YCxOQqtjny0AcMuFL1sMCRBjgybfkS7mR\\u002fU2hu\\u002frXn1uuRHxWTXyZiVMf1Q+0ajrZK3YKdJmsVSyp9r9R5abGjlX5Ug2H0hw8PlU6dlbt92BgBqavzq7OAPgiAyM2Py8mprqtsAoLysGQBGjkhwtw4bFnfiRFVJcRMAVFW2AkBWViy\\u002fhEBFRcsnH3dmipcvn3jnXfPEYvSrlCu6z1PfP9ThnUMNhg2Le\\u002fChRZ6NFwWObRLIrkaj\\u002fSchXPXVvdf\\u002f7bstOwrKXZGfj5\\u002fdWVB+7eRR10\\u002fNTYpQeyyNXYhuGj\\u002fGdePNvQff2nfIsxHDAnP90gnzpmbrjVadwaozWN76fLfF2lX1GxuKDpz2ONySSoRTR6fyIwGJieicZd1NZ8LFNC9AlHA0JRyNRrEQR4iE0suF0svReL+6UNeNgXtfDut6lu48SgcAoWSB\\u002f9l2N5JKEEgWOq2\\u002fuiP85\\u002fQXIRJKLhFKLkHjQcewRq3pk3bzOiftcT3Y3cqwRgddZbTtBniLItXh8hWRyj8LKXQM9\\u002fmwOE616F8x2vYAeMxhxnE0y1hppsVsP9Rm\\u002fEhIxUcqV0cobuenMk22PZWtN\\u002fIeBBkxv8hE4\\u002fkRhIOuKm6ciQSjVPfGhj2FBP3UrP9Pi+FNJJgTt8\\u002f\\u002fBDTH2c\\u002fUZaBRn2LCHotWPYRGA2dxnChvvoIfSYtap5DMct9lOVub8X2t6VOaQfMqDGtgWIPdWWKwbmrU\\u002fVMhmRGj+otMHFhV1br2RzrM37vvDos7JBR0XmRqNbzdrH+FOzf6h+OcNrbA5ixoM30cp34mQuFR7ZMDurHjn1rTWneE4RwMq7M5S9pNX6mkSxI0Lwt6m27Bh2BuKd1X0aSId9SyK913OWA6TF+3GdfY6QreUq4mmmWsTqbR5iw0WrcDgJCKj1T+WaO4kSSkyMJ+YliDzVloc561Os7anAU2Z7HHfBvntBrebTW8i0Z7FqG4LT78BTSKYSEAzdJ2lxs2G0m4A0CJ8fh\\u002fi+8bFz4vWpIMAE3WqlO6XWbaSzdMESmZFbUcjfbGSHdYGHRwXLgoGokEQVhY568Jw3jsts9Tdk5cXl5tdY0WAMrKWgBg5KjOhDsADBsef+JEVUlJEwC4Zkx1V6Fx2bnjrKsKTVZW7L33X+K1nK\\u002fJZEND\\u002fWTYsLhffgY495cPXZpIRURkV7cghLbN2N5mQqMAAEBSSSzTgEb7lVIifnPVFT8ePfP8TzudDAMARpt97Z7ja\\u002fccjwlTjE9NSI\\u002fWqKRipUQsFXmfTNh\\u002fi0ZnoSEMwy4gUokwJaHrivWab\\u002fbjhPtQV1rjUXYvPT6Cokh+JCDdK79RpLcDCwzDMAwAAJDJaUWy3uf88IoSZHSVBgNgWS3v3hDCtZu+bNL\\u002fm2E9hsX7wLC6NuOadtMXUaoHopT3nX8fXo6zN+r+qTV9gaTavXIyDY26f3aY1yVFvCcR5riCVjzlbH+zOs+4E+4Wx\\u002fFa7f0OuvexLACcybbPZNsXLl8ZH\\u002f4cSfRxvJ3VecaVcG8xvNGsfw1tBgAAjnM0dDzDcc5I5V3nYmxN2z0G6yb+YnwG62ZHS21GzE99+sMGf0uxOQrgXMLd7iyp0d5rcxZ5LtIjJ9PQqPt7m\\u002fG95Mg1MtE4tLlnrYZ3LI7jVudZr9cYMOwC1vsWmyIfnq7IrTDlIXETrdvb+hMS7G5W1HK5AO051atKk5cdXqS4KyUdNK4a7gAQGdVjZrYPXPOmtjTrjUZbY2MHAIwc2TXKb8SIeABoaOjo6DC3thoAIMezgHtjo851Y\\u002fToRK\\u002fZdgDIz\\u002ffr50wopNy3LRaHVNp70ZIJE9NIkmBZrrCwvqGhIz4+HF1iiFh69YRbVs9Bo+f4KDgjkd9iMfyHkd9FCYejbeehrl3fqDM26Y2NHcYmvbFJZ2zUG1kOzYM0602bThcjwfNR8PIjaAjDMAwLYXrPa+oK2XmNaWtqQ0\\u002f8wpV9OI3EMAy7WNCeydnz6EePnMj1cF4XwhjWUKO9x2TzftLkG8vZmvWvGG27UiI+EFB971rHsB2VrTdZHWi+wjebs6S8+fLkyPeVkvngSkRi\\u002fcr9keotv9S2P8Rx\\u002fKtLveswf2N15KVFfSWgotA2P1gdBSrpErP9QLP+dbTNU5PuRbl4ulQ0CgCa9W\\u002f4yLa72JwFDR1PJWr+izb4FApbCvAuLJlsu6vb7mI5s2d775xMU0XLNYmaV9Uyf7vVtpk+ohl0gkYMuxj0nnAHgEvjbn+39FHOj8vFCI0obmbUVWjUD6UmL+XzYqVpaOg8WK29J5fz82qPH+8ctT1pUn++umsGVJblDh8q4zgQiwUZmV0TEg4\\u002fV17m0KEyjnMt79HDXSzu7NTcobPw425bt+b7OZmqWCyUSIQ2mxMAKipaIiJ6L\\u002f4TGamcN3\\u002fEju0FLMu98drmf798vUDQ9451Q4Xd+rP7NknGUsIcfdtSkfQKgXAUQUYix8diaddALf8tfvkTNIRhFy6KvPB\\u002fNzBsgIg9y7Wbzm\\u002fIwp4THkUMKIpMilXzIxiG9cHsrf+wMl3b5t9zr12W4Ks8BTaEcGwH\\u002fy5BBty9zIVlPDpIkWQk\\u002f27oczJNlS3X2\\u002ftQCYfHYj9a3nJ1etS37gIgAWFZY0XLdTZnIdrgB5azVLfdmRr5uUIy0+rw0uEPOx+u3K7Rur1W+yAHNNrsB5vzbEXriozonykyDG3rjc15hgOmvuPpXgc9cEA3619OjfrC5ixuNbyFNnvTYf4xSnmvWJiNNvQgFLYUF5vjDABYHMer2+5k3RNIBIjjHHXtj4oEqb5rLmEY5lfCPVaSOj3y8v1tv6ANPpEEdU3SgwIi4HoXHHBlRjThLiTFMeL+nFH9g\\u002fd3lhQ3zZ4zbNy4lIzMGCRl3NFh3rI5\\u002f4vP97EsBwCzZuUkJfW9UFd3iYkRrnlT9+8rAYDsnDj+H6BWy2Lj1E2NuoMHygBAoZAgvchzcmK3bM4DgIMHSquq2lJTuw7OOI7b+Pvpt97c2rV0b0aNSjx2rBIAPlqzKysrVq3uvV\\u002fb6tVzjx+r1OksJ05UPfLwl3++e\\u002f6oUR6\\u002f+3a7s+BM\\u002fcGDZRaL\\u002fbHHl\\u002fKbhihTx31oCMBu+cEOP6DRvibcsQve+ryC708XFLW2muxodmxycuJXN\\u002fk7FRgir6Hpl7NFR2vqqzt0NppWikWZERGXjci+YVyuoFtSmwP4\\u002fWzxT\\u002flnzza3dFhtCpEoSiEfFh25ICtjQVaGVOixUwho4UCJKOqPsoqPDx8\\u002f29xKs0x6hGbFmFErx+VS3YbtXPvZutMNTUcevjtMIvnqxOn1eWcr2zs44BJUqiXDsh6cNQ1ZfmdpxTen8vMamvQ2m1oqGZ8Qf+ukcZOT\\u002fT02\\u002ff70mac3bScJ4tUrliwd3jnQ2KW4te2Tw8cPVde2mi1SoSAnKnL56JHLc0eQvL\\u002fZ4nCOee3trMiIjatXHampW3Po2OmGJqPdHimXTU1JunfGlDRNX0YFEUOw1xs2cCLVnZOpuFTUtdkcdN8mTa2o1+4+XsaPjMqIE53f1o1hGHZhIwgJx+sZyrE6IvAZyzjO7rRt40fIc+VNhgSa1Va2ruwph0gSEqlonJCKpahw4DiGbXfQdVbHaa+JVwddXdl6Y0bMb1TAly64Gu19PrLtEmGOSJAmpOIAGJppczINVke+u5Y3AHCco0Z7d3r0ejvd2c1uaCME0aqHaLadYdtppp1h22m2nWE7OM7Lxz7Q7M4Km7Ootv0B5EsnCLFMNFZAxQjICADOyTTZ6Qq7s4S\\u002fjJvdWVaj\\u002fXNa1NcA6EmNb1bHGYNlk\\u002ftpZaJxEtEIADDZ9jroGo9FAYy2Pxx0dZP+3+4\\u002flSJVCsksIRXrpBsNth3dSo1z7eZv4tTPega9C40tpRPNttucRTVt93TPtlNkmFSUKyAjBFQkxzlott3qyHfQ1chiLhxH17TdkxW7neJN3I1hGMLfE6pFsata7XUlxhNoQ8+Wxt2RLBuGRv3QaK000TokmCIbThJdxU\\u002fOH8dBcXFjcXEjAFAUGRenVqtlYonQ4aBbW43NTTpX13IASEuLevjRJfzHnj+CANe8qQcPlgHAyJFotZzhw+ObGnVHDpcDQFZWDJKAWrBw5Kdr9xoMVpvNefddn8yeMyw5OYKiyKYm\\u002fbGjFU1NeplMdNPNMz5du8fjYT24+pqJroR7WWnzrbd8kJubpIlQOOy00WRrazVeefX4Sy8dgzwkKlr1j38u\\u002f9tT35vN9rMF9Q898EVYmDQhUSORCO02Wttuam7Su6rMT52aiTx2iJKH\\u002fQMNYVggnty49YfTBSKKmp6arJSI8xqaqjt0ADA8JmpycuKExHj0Af453dB07WfrXLelQmGsUtFqMh+rqz9WV7+nomrNiqs8lgZ44rct6\\u002fPPAoBCLIpTKXVWa1mbtqxNu7modO\\u002f9q5EcekALB+q702fe3ndIIhBEKeQdVmtBU0tB084DVTVvXb2Mn792q9cbnvx9247ScgCQCAQ2mi5t047QeQyrZDjuid+2bDhTCABykShOpWwzW7aWlG0tKVs9deLj8zrrSPrw5fHTz23dKREK3ll+xaz0FH7TN6fy\\u002f7F5B8NxFEnGKOQdVuvR2vqjtfW\\u002fFxZ\\u002fsOJKEeWxh6rq0K3PP\\u002fvEb1sIgohVKljgmoymDWcKt5WU\\u002f3DryszIgK\\u002fgkiFcUxtfDAi+7GSP4dVWu3P9ztM3LpnAD\\u002fqjTWd+4s1fHHRX6gEAlkzvz2ppGIZhFx6CiuV46TPavkcku5HX7heb8XWO1bnvEoRYIJrc1RzaOGBqtffanR7Xa10UkhlRynvl4mkEgY4mZzmTwbq1Rf9G9+y2na6o1d6fGvU5EvdNa\\u002frMaNuJRgEAyAjFrRGKVWIhOlUVw+oM1m0thjfcWVeG1ZU3XwGAlvEcigigYsIeQ6MALGuk2Q6L40St9n60bQCx5c1XsZzJfV8mnhitekgunkYSEt5iAAA2Z0m76XOt6bPuHdJNtn1txjWRyj8jcd+cTGN9x+MAICAjkiM\\u002flIunuOIc0I0dz2pN6JrWpPuX0brDdTtCcVus+kmS6Ozc4KBrKlqvQ+qPG6xb\\u002fUm4h8iWwlfRcjXDekyXqJZdqVHcLBNPIrqlBx10TavxvQ7zuu7XbJxMfbvpiyjVvUi8u5TItRyH9jZzoZnWGi36zaplV2sUtyBBH\\u002fo2oyyGBQG6RfWEJKgbUp5YX\\u002fdmvm4f2taNkBQvjb9zQvhCtME\\u002fp3R\\u002foCGAHNVENHR+JJKurvcMw9bVtdd1q8FCELB4Se7d9yxQKtFdwvlzzZvqdDIAMNKzezgAjBgR\\u002f8fOs65WpJ4MACgUkueev+aZp38wGm1OJ7Nju0fJuago5TN\\u002fvyotLfqzT\\u002fe6st6+TZ2aecuqmV98vg8ATCbbgQOl\\u002fFaL2fuP4+jcpHfeu\\u002fU\\u002fL\\u002f1+9mw9AOj1Vr2+Hl0IIDIy4O4eoUkivxMNYZjfDlTV\\u002fHC6QCwQ\\u002fHjbDTlRkQBAs+wjP2\\u002fcXFQao1A8vXAu+gC\\u002fjYmPvXr0iBExUYtzsuJUSgBwMsynR0\\u002f+54+9u8oqj9TU8Xt2Fza3rs8\\u002fK6KoD1dcOT0txZUl1Vosu8oqtRZLpNxjdEtAC\\u002ffBO\\u002fsOPblg9i0TxgopiuG4n\\u002fLOPr15+9bisnUn824aj17kA4CnN21vNBhfuXzJwqwMhVhkcTiP1NRpZJ3zWru8tffghjOFCrHopaWLFmZnUgTBsOymotK\\u002fbdq25tCxeJXq5gnoM\\u002fNz+x8dPv7yzj1hEsma664al+Dxw3uwuvbvm3cISPIfi+ZdmzvSNXRgf2X1Y79t2VdZ\\u002ffLOvc9cMpe\\u002fvJNhnvhty+Ujhz25YI7rszpe13D\\u002f+l\\u002fbzJY39hx4Z\\u002fnl\\u002fIX9IRRQAGB30D9vz9t5oLi2scNsdYSrZKNz4pfOGzVlbCr6gP52NK\\u002f6t51n8osb2vVmuVSUkhCxYHrOFQtGC4UUfy6QnhzNq960u+BsaVNbh8lq67GI59v\\u002fuG7cyCQ0inUzb1LWa1\\u002f8wfJ28e99vz85Nnzm2HTeUr5wHOw+Ufbyp9vb9R616aI1ystmjuBHMAzDMIRAPM3BS7jbjP8VSi4lSP9HsLE249t207v8kFB6JXEuwRf6Wg1vm2z7kSBJKJIi\\u002fquS9thTjSQUatnyMNkVjbrntEa0lqbRtrPD\\u002fG24\\u002fHok3hOabW\\u002fSvYRGAQRUZHLEu3LxdLQBAAAoUh0uXxEmu7xZ\\u002f1Kb8SNXkJ8UviCRpFJEKlnOI80aBO4PlgBBgualcPlKz\\u002fYuEmF2fPgLYbKlNdp7u9f7bta\\u002fppYtD7SYO8MaAMjUqC+kolx3kABBfPgLFscppOi\\u002f3rrRdSNcfkN8+Av8JpEgOSH831WtHilgB11FM629\\u002fkmhsKUg+Nl2IZWQFPGm+2pEd673rpZdXt22mmH1SKvWtDZSdVf3ND1CJhqLhs7xOo+uUBAnFw+Zq48Y5kMv2wafgBBel\\u002fSXbOWE7U1f6Z1taDMAABBADFNNXhJ3q0aE5oj9xHD06Y7dSJAkqNFhM5Hgebrn3gWzZuccP1ZZUtzU2KhrbzfZbE6aZqVSoVIpTU2NHDU6cf78EbFxavSR\\u002fcRVxt1lxLmi7W7Dhnd1d83plnAHgNG5SR+vXb3+x6NHDlc0NHTQNKtSSRISNdOmZV5+xXiZTAQAaWlRFRUt6CO9ue32WRMnpf36y8mCM3VarYllOYVCHBGhSM+IHtGt971bUlLEW++sOnG8au\\u002fe4vy82rY2k9lsE4kE4eHypOSI0aMTp03LSkvvZSeEYReD7SXlALAoJ9OVbQcAAUneM33y5qLSvRVVDMueT0Hz\\u002fyxbzL8rpKjVUyduLirJa2w+Wd\\u002fIT7i3ms0AEKWQz0jr6rsdIZNdkzvSfdctoIX7YNnIYX+a3NkhlyKIa8eMLG3TfnLk+MeHj3tNuBe3tG34003uD1AmEs7NTOMvoLPaPj58HACeX7JwcU5nhyaKJJeNyHEwzF9\\u002f2\\u002fLfPQeuHj1cLvLoRSIRdO4H391\\u002f+I09B6IU8k9XXpMdhfZAf\\u002fWPvSzHPTpnxsqxo93BGWkpTy2Y88jPG9edzHt49jSlWMx7BIyKi3nl8iXu3voTEuPvnTHlua1\\u002fHKzycmTZK6lEWNvY8deXN1TXd10bbtEadxwo3nGgeNHM4X+7f4mA6vta5APNsP9+d8vmPWfdEZ3TqjPUnS6s+2nrqZcev0ql8HVN2mZ3\\u002fuN\\u002fG\\u002fce9dK1B0EQQA3MW7jwRITJl8wYvnFf15ficNKPvfHz4mnDViwaNyIt1tsoEQAAjoPS2tbD+VW\\u002f7S2oavDSz+DxWxf0rTQNhmHYxUMkXe4wf+m+yzKNxrYrZepXBKIe81adOKfTttlmeo9x5vPDBCGTKB\\u002flR0KZg65uMbyJBElSmRG9XiLsfYwUAYJ49XMkSFqNHpccAKBJ\\u002f1KY7AqS8OhO0ZNWw9vdE+UEIUqJ\\u002fKTX0tIkIYlT\\u002fwOAaDOuQduw\\u002fkckRbwZJrsCDXcjF09LiVxb2XIty3lMDs9ylhbD\\u002f5A8uD\\u002fC5dfxs+3nkFHKe2u0d6NhAIoMj1M\\u002fg0YBlJK5QirOyTTyg1bnWSU1hx9BhMiW0hMhlZAe\\u002fb1IkIw2dCMXT0+KeKeq9RZk\\u002fIGTaTTZ9rimHcYwrLuAz6nGqufmhs0qMR4vNZ101X5hOUZKKcJF0SnyEcNVUzSi8xrQUWg4YmHQS68jVdPkgjAk+NwL1yIRr269bdatt81CowAAMGpUIlJ5PJgWLBi5YEGPeasRIxJ2\\u002fPEkGvUUEaFYfde81XfNQxvOWfPxHWioZ33+NMZPSB0\\u002fIRWN9ubmW2bcfMsMNHpxe+PmZWgIu1A0G00AkBTm8TuWGBYGAAzHtZotscp+HguSognPa2w22j0KDo6KjZEKhfV6w5Mbt94\\u002fY2pCmK\\u002fyfwEt3AcLszKQyOUjcz45crxWp6\\u002fXG7q\\u002f3PysdHe23as9FVU2mg6TSC4dno00XTFy2HNb\\u002f9DbbPurahZlZ\\u002fKbFGIxALyx+8C7Bw4nh6s\\u002fW7k8UY3ubhoNxrzGZgBYNiIHaZqUlAAAToY53dA0k3dlAgBWjBmF1MYZFRsDAEa73U7T4nOJfj\\u002fZHfT\\u002fvbi+rkknl4rGDE+M1CjMFvupwjpthxkAtu4rZFj2uUcG5DfkX29v3rqv0HU7MlwxdkSiVCJs0RpPF9ZX1moffu77mCj0y+L755ud2XaZVHTdZeOzUqNFIqqmoeOHTScbW\\u002fQAQBLE0\\u002fdfmp0WnRAbhkuH++\\u002fhG+cezKvqMHT1T2c5btOBwk0HCsOV0vTEyPioMJlUJKRIu5O22WmD2Vbfoq9v0VntPY4wuO+6WbPG+dtHHsMw7KIlEE0SSa90WH92R1i60tR2LSUcJRTPIYU5JBVHEDIA4Dgrx1k4poGhaxjnGcZxlOtWOhmAkKlfJakeOzmFmmb9K91KWkOS5nV\\u002fcohuMWGPm+x7kKlKaaa13fR1pLL3c1iWM7eb16FRgDj1M71m293i1M9aHQVm+wG0AetXEYpb\\u002fcm2u8hEY+PU\\u002f6zv+CsS7zB\\u002fFxP210ArhofLV6AhAABQSS8hCFH3Oifh8mt6KI9OyMWTdZauTR4AHHQFgK+EeyhsKT0hQJAa9bk\\u002f2XYXpWSuWnYF8gkAgMV+FCfcMawnfTmzJQlqmGryMNWAjPKgWfuUiEuR4JSIy5AIhg0ih\\u002fV32nlaqnzYdSQNwJoNL9gt6wBIqfwOaV\\u002f7pywa3dknF7vwuCqftJnN\\u002fKCrCzkBoBSjlfsCYnU6fztbfKCqpqytvcNqNTscdppxMgwAIDWlNDLpS0sXuarJr887OyU58fKRwy4bno10+u7Dwn2QHI7mtTMiOvuVV3fouifcR8ZGIxFEaasWADIjNd2nXRWQZEakJq+hqbilrVvCXbTm0LF3DxwWUdSXN17rKsuDKGrpHNk68+0eu0FpzV15T5fsbpcH3B8dw3p+MX7IK6oHgMsXjH7w1rkyaefzsCz3yfcH1v5wCAB2HCheMD1nzpR+\\u002fhnZe7TMnW2\\u002f5eopd62c4a4mrzdaX\\u002f5g2+7DpY2thq4HeDp5tm7PkTIAkIiFn7+6Ki6680ufPh6uXjTmz39bV1rVwnJcY6t+8ewAzj0wAAhTSN7567UP\\u002fOdHrd7jhwUAOozW44W1xwsDGEshEgoev3X+5bNHoQ0YhmGYN9KwFxi6AumozjjPME6PvFivCEIsVb8ilAZca26wOOgaveVXJKiSLlJJ0VN43whCEBP2eFXrKiTebv7CnzSi3vIr61mHGgCEgkSN3KPuR2+IOPUzZc2B\\u002feVYQEhCEq16GI36FK5Y2WZ8HylfznIWvWVDQKW9CUIkE3WOpkUQhFgqHGlxnETiSslCJOImFg4D8Eg3O5km\\u002fl1EiGwpPYlQ3iEJcJbmCMXt3RPuZvsxJIJhmFvIjd0eGz5vWfxdyL8ocV96XmPYALFbf7RZ1rmnN7Ga3reZPiQINUmqLcbXHVZ0z4phruInm4pKGw1d5wafHjkBAJOTE88nhX28rmH+e588tXHbxsISgoCx8XGXDcu+YVxumiYcXRQAAC4bnr3j7j\\u002fdPW1yhEx2sLr2qY3bZry15tVd+6xOL51eA1o4UO5aLm5SocCVyrV4e36ZUIiGPJkdDuAltREKkQgADDa0m8nphqZX\\u002ftgLAA6GeXPfQaTVxWh3AABJEGkR4T39k4rQP09xftdRuhs7IvGvf17kzrYDAEkSd14\\u002fY9GszlT1FxuOuJv6y+frD7tuzJ+WffeNM\\u002flzt4Yppc8\\u002fsiwzxVfdsN2HS103li8e6862u4hFgjuv76yv6l4MC0hGUuRHz66cPNLf3kleEQTMGp+x7sVVONuOYRjmP4JUKyK\\u002fE0o8yvoFSiCaooj6XSS9Gm0IYR3mbzhgkGCU6kEk4g+lZK6Q6iqj6mJ3ltmcxUiwO71lIxoCiFLeSxDo4aVvUtFomXgiGsX6j0p6qYBC+6D4RgDldSpOd5l1P0mE2T7WB7EQHWsLQMjE49DYOUIK7fpDM+1IhC9EthSv+nAVBABk4gkUifaXsjk7++VgGNZdjz9AGIb1hHaeFYlnuzYfjrNZTe9SgqywqE0EULrWxTbzZ6Kh00sFC475WRkLszK2l5Zf9tHnC7IylGLR6Yam\\u002fMbmMInk2UV9H4VnddL3\\u002ffir1mKZkpz4xpWXRSnk7qb71v9a2d7BW7ZLpFz2l7kzHp49bV9lzXen87eXlH9w8OjBqppvV610zQXa54UDYnXSSMTicLo6fveaW\\u002ffKVULdlXbvzuRwgLfBBK0m8w3jx8zLTLt\\u002f\\u002fW8\\u002fnC5IVqvvmT4ZWUYuEgIAAbB59a1IlRgfCPB3ST9dt3S81xe\\u002f\\u002fdqpW\\u002fcWAkBhWVNtY0dSnPdrLX1Q36w7W9bZeefWa6Z6NgIAUBR5wxUTn39rE9pwTs25KuFZqV7y8u5kfU2D93UV61V8VNhbf712z4ny77adPF5YywYyeCJGo5w\\u002fOfuKOaPSEzoHl2AYhmH+IwiFXPMRbd9rM\\u002f6XdhxFqhv7QBByoeQSkfwmgcjLvjXE6Sw\\u002fIRGJMNvHpIg+kUrJ3Hbz10jUaNvpu+8txzlM9n1oFIgwaV+GxYdJL7fgXroDxsfUoD6opJcS8ASSsDbbD7KcjSR8TR3EJxJ4FHtEdE9hiwRJZM8TFwtI9LKB70loQ2FL6YlSujDQ4jwAAEBIRWNNtt38EMMaADjo77MeDLsw4IQ7hgWMY1vJc\\u002ftvh+13jtVJVX8nCAkAiCTz7Jb1HktjGAAB8NbyZau+\\u002fuFobf3molKGZWOUiuvHjr5n+uTutVP8d7S2TmuxAMArly\\u002fhZ9sBoK1bhRMERZJzMlLnZKSeaWpe9fWPeY3Nv58tvnKU97IeAS3sp6r2jtFxMfxImbYzOZsSrubH\\u002fZQTHQkApW3a7pPQ0ixb3tbuXoYvI0Lzz8XzAeA\\u002fyxY\\u002fvOH3N3bvTw4PWzrc4+DVVTue4bjC5tZeK9sMnAk9dGROjtckxKjrm3UAUFDa2I8J9zPFja4b0RHKnnqyTx6TioZ4nHTnmZJY7OV4Q3xucs6A0sRYd7PHZ8wen9FhsBzIqyqtaS2vbW1oM1hsDovVYXfSFEmKhAKFTKxWSGIiVYnR6uzkqFGZccmx\\u002fbaqYBiGXbQE4lkK8SyWaaZt2xj6LOMsYplGjjNxnAk4FggBQcgJUk2S0aQgmRIMp0RjBcJx0HPH21Bmd5Y5aLRemeI8KjhLRWOgWxrRaj8JPpOBVsfp7tW3paLRgfakdlFIZqIhrP\\u002fIJdPQkB8oUi0TTzDbPcZuchxtdZySi\\u002f29TCWk4tAQD0miE2iJBElIhM91ss\\u002fXfSV0C5EtpSdq2VVoyD\\u002fePlKWYU19St9j2IVvSO7pMex8\\u002fOOV69GQN7esnnPL6jlo1IWQu2tj2y3fEqTS3aWdIBQs62twGXbR+veOPUdr61+9fMl55qn5LA4nAJAEgWTbq9p1pxt8VRXkGxUbMyExfld5Za1Oj7Z1E9DCvm0qKrl85DB+5JeCQgBIDlf37SLErPQUhVhksNl\\u002fLyy5An3mIrPDIReJpqd6z1kDwGXDsxv0hpf\\u002f2PvX37bGKZXjE7u6vSSqw0bFxpxpav7w0NH\\u002fXbWU96DgUaukCrkYjZ6TlhThSrhX1WrRtvNQWdvmupGe3OMZrCZMppRLjGYb2gAAAPHRYSegFgBq6r38MFafC8ZF9+UbxxDhKtnSmSPQKIZhGDbwSCpGJL8ZjV5wTPb9aAhAIe5LRtVFIsxGQwBW51k05MniyENDAApxH\\u002fPmEmEWSUhZL5PZYudLSCUISA0a9Y9cPBVJuAOA1ZHnf8K9e590PpKQIhEB6atXDUGgA3A5zksNTJcQ2VJ60lNp+155nVGWZfU44Y5hXp1XQQAMuzhRggynfTcATTuOOO0HxNIV7iveLNvc\\u002feo3hgHA96fzSYJIVIfRLIu29ZWr5zXLcV8dP+0OHqiqueO7nzhkvlQAAPjmVP67+w8XtbSy51pplt1SXHq4pg4ARsR4HGUGtHAfbCspf2f\\u002fYRtNAwDLcT+cLvjy+GkA+NPk8eii\\u002flGKxQ\\u002fMnAoAz27esamohOE4AGA47vfC4n9u3QkA986Y7Co705M7p068cfwYO03f\\u002fcMvyBWFpxbOoUhyY2HJX37ZVNrWmdTWWW15jc3\\u002f3XPg7X2H+AsPBKXc1w+LWiVz3egp8d03BlPns2nCOp\\u002ffqzBlj3+bexLX7zedNJnRAvqfnSsQP3Ni9zKaGIZhGIaFFqvjDBoCEAnT0JDfupeEBgAn0+C7Po+dLkND3kty+4kUC\\u002ft5znnMRSxMR0N+Ewu8fKF2p5evviekt+ywG9Gt7ylFqpEIn7dy8D2e04XIluKVgIwQUN7HrfbKaz0fDtAyoRiGuXT\\u002f1cAwrBcS+S2mjgfbm8ZznIEgFFLFfe4m2pFP+awWh120Lh2esz6vYOUX37ojUqEgXqWanZH652mTImRdCc0NZwrzG5uNdrvRZi9qbQOAgqaW1d9vUIrFSrFYKRY9Mnu6q2RKWkT4FSOH\\u002fVJQ9ML2XWuPnoiUy+r1hjazZVxC3A1jR7\\u002f8x173c7o06o3vHjj8xp4DYoEgSiFnWbbdYnWlvJcOz5mX5XFMHNDCgYpRKm6bNP7lnXve3X84RqnQ22yu6UwX52TdMC4XXdpvf5o8ocFg\\u002fOzoyQd\\u002f+l0uEmlk0naL1VXV\\u002fYZxuaunTkIf0M2zi+Y1Gox\\u002flFXc8d1P369aGSbpPKyclJTw+hWXPvH71l8Kin4pKBKQJEEQTqazXgrSoX4giIQUGuJx12ax2Hrsa9MHVnvns7mf3yuJGO3y4zZtXHrusIS8onpth3nV\\u002f32+6urJGSlRErGgrkn33e8n8orqASAyXHHTlb1\\u002fNRiGYRiGDS57twkSCaBEVI\\u002fDB3vlNSXKcQ6GNXjNMLo46Co0BCAS9D2bKaQSrOCl1zx2nrrXSfefyFuy3sHUoKGeUT0XZPeKJANb3ocQ2VK8Evep7DuGYX3g6xQawzCvxNLlLNNkt\\u002f5EkqNkyv8jqc461CxTSztPSxX3eC6OYVCubWdZliIIDkAuEgEAB5zVSZdr28u17ZuLSn+942Z3bvf3wuJdZZX8h+ttNn7kgZlT3TXK\\u002f7Ns8YTE+O9On6nu0LVbrCnhYbdOGvenyRPK29qhW8L9mtwRVtp5vLa+WqdvNBgJgHCZdGpM0pWjhi8dkYNMdhPQwoGakBh\\u002f55QJmZGajw8fP9PUTDPsyNjoFWNGrRyX6\\u002f+spF49vXDu\\u002fMz0L46dOlnf2GgwqqWSaSlJN4zPnZ2eii7qDUUQ\\u002f7vqshu\\u002f\\u002fP5MU\\u002fN9P\\u002f66duVyIdWZ6b5sePa4hLjPj5\\u002faV1FVo9MzLJcQpkpSh83NSLt0uJdhnv3L4fSYNgphd3RmxmWSHnPffeDOpPt+dZrpsYMPQcBLj1\\u002f5lxfXF5Y1NbcZXlmzHVkgKS78pcevDFOio3qxIYHl2ENtZSc7qvI7appsOqPTaqEd4SJ5lEQ1JTJzacL4FLmv0dwBqTS1nGivLDE0lhob2+wmo9NqYRxSSigTiFVCaZoiOlsZN06TOjY8FX1kP3G92b0thUWGhnpLu5m2c8DJKHGMNCxFHpWrTp4YkZ6pjEUf1ptgvq9gfl8uhfr6vS1F+bqaanObzmG2s04xKdSIFcnyyDHhKXOjR2QoPWbyCH00x+xpLtzbUlRkaGi0dlgZp5gURIqVGcrYKZGZi+JyVcKuXzMhSVl9\\u002fXb2zuC07mkpPK6tKDM2Ndn0ZtrGcSAXiONl4cNUCVMiM2dFDxOR\\u002fXM2F8zX6m6Ati+s3zmYeiTCAXOmLhUJnj+Ws1DQYxqRZlrQEIBYkIqG\\u002fCY8dzaH9S8B2fdZ2UVUIhoCoJlWNNQzgvA1sLU7khChob4KkS3FKwHV9y8Fw0JKXasuMUqNRkPJQB02YdiFTaq4V6q4FwmSVGJEfBWAr46o2EWopFV77WfrhBT5\\u002foorZ6alCM7lyjmAkta2u77b0GAw\\u002fnymcNXEca74mhVXuR\\u002fbK4okbxw\\u002f5sbxY5D48Jio0icfQYLJ4eqnFsxBgj0JaGH\\u002fhUul7j9sbkba3IzeuyP9cOsNaMin6anJPmq1uy3PHbk8dyQaBZAKhT\\u002fdfiMaBQCAOJXyr\\u002fNm\\u002fXXeLLSBRyYSdv\\u002fkXbKjInpq6pXO4Ku0aLuuc45cpaI\\u002fM9fKc1XjdYbO5\\u002ffKeK7yjFdhSulbf7\\u002fujie+rK5vFwkFDMuSBKFSSDJTo+ZOyVo8e4Tv7vNYaGI59rvqQ19X7W+0diBNrXZDq91wVl+3tnwX0oQgCfLwkhfQqKdKU8svdcd3N5+ttXiZn8BM2820vdVmKDc2b2\\u002fMB4BEmea6lGnXp0wjicCqJh7Rlt135BP33Q1z\\u002fi9B1lV2dkfTmbeLN9dZOmcdcNOzFr3TUmJo3NaYBwA5qvgvZ9yPLONV0N6XS9C+L7c\\u002fmgvWlO4oNTYhcSvjqLe011vaD7aWvF+yLTc8+d7sRRM0XroxhqDf6k+8W7y11W7gB62Mo9airbVodzUX\\u002fLdo48qU6XdkzpNSIgCQCyQGp6+fbh8arR0fle3c1HDKyaI5e73TotdbCvX1P9UeUQol16dMvyVtlkwQWF6JLwivFeTtCxsgHNA04+UnayD4mI4SAGhvM2Z1nwPTf14rU2PnjyR91ST0zWt\\u002fc69ffU+6V13vTf+cxYfOluIVSfR9S8Gw0OGkmZue\\u002fzpKLb9q1qhl00ao\\u002fT4LfvaTzUcLawEgNU7z3qPXoM39Cp\\u002flYlg\\u002fIvA2hXX36dETVqfz7mnTkeQyAZATFTk\\u002fK\\u002f3L46fr9UZ+E4YhjGZbh94S3kMt9Yqu2U37s9NKWmLns1X0PBerxepo15vRKI\\u002fdQT\\u002f0\\u002fPfV9e3ZadGvPrk8ItzL6RM2tLTY9E+f+vZkRxXa0K+Oass\\u002fLd99RBtAtVYAqLO0v174+5aG0\\u002f8Zf1O0JLA+X3xNNp0rIchw7EsFP2+oPYou4U28NBwNdRP89xWc78tN5zD\\u002fM++Hfa3FaIM3eR01dx\\u002f+aHnS5L+MWDZwvafPn51xPpv3\\u002fc4mLzV5+eyM87OK3XtaCl8bf0uSPEItknW\\u002fwuGP76sPvVm8ycb0XiLM6LR9VLbzt\\u002foT\\u002f8xdMV7T+wXs7oL5Wm4Dt31hA4plTT6KVvc3Dg3wsKyXS1nd58D0Hxlg7RHMT15LfvuJJKQABLImsJyvTh7d9OUS9fkLnS3FK8rblYwLzI1vr8ur6brkPzUz+aO7es+r7i+p\\u002fsuXv03LSnnjlmVoW4AsDufkp98GgJ8eXZUV259nZ+ejH99gKDhRUmey2k1W+xvf7alsbH9m1SXoEj0Yl5Xw+8FCAGjRmcrq2zIT+nl8J9\\u002fg\\u002fAZhGIZdPJqMRgAIk3o\\u002f4qxq1wFAlOLCP\\u002fTBztOR09VoCAAAKmu1Ta2dPS5HZfW9VmZ3I7PjXDcaW\\u002fRV9d67FJ0oqPU2QW+Xb387XlDSCAD\\u002feGgpzrZfAPROy91HPuqevSUJUiNWJMg0\\u002fJIa56PY0BhoVtqtQF\\u002f34LFPTXRAp+Uemqx6AGA59q8nv\\u002fYzGwgAM6N7n84hyO8raN+XS61Fe9vB9\\u002fzMtrutrz1y\\u002f9G1FibgbnrB4WSZh49\\u002f1mu23a3S1HLX4Q9rLVq1KOBfPJbjXjyz4T9nf\\u002fEnA+7WZNXdd+ST3+pPoA0+BfO1EAO3fWEDiuXsaGiQcID+XJCEBIBAgv7zNh8mNuiI7hdRuEBWQuI8VonzETpbildE\\u002f1XOCVlXTBixYsrohaMyEzUB9FE4WFJtsjn+OFuONlwoLrA3eKqswX170cQA6rtOG5nqvn2owPv5dX\\u002fBuxYMOx9sD5ev8ZaFdcmJjtpbUf35sVMzUlNSNWp3vNlo+uDg0X2V1WKB4NJhWV0PwDBvvvnt2CUzh5EkevLw6Y+HXDdGZcfHxwRwWNmr5HhNTnpMcUUzAHz6w8F\\u002fPLQUXQLgm9+OoyFPR\\u002fKqAUBAkUlxuHPiheDJk+tqzR4jHsZp0lalz56gSXOV0QCAVrthd3PhZxW7m6w6\\u002fpIAoBRKEmURCTJNkiwi2WfF8OVJkz4u24kkl2UCcbYyLlMZoxbJlUKpjXG02oynO6pLjY38xQCg3Nj8v6JNfxt1NRL3U7NNBwCvFv62u\\u002fksP64RKxKk4WqRnCJIncNSaWrROzsLLhFAzIjq\\u002fXA\\u002fyO8raN8XALTZjfce+bj7k6QposeFp0ZJVDKBWO+w1Fvaj2jLOhweI2NOtlc+feqb1yes4gdDxKtnfz2mrUCCQpKaFpmdpogOF8lNtK3Fpj\\u002fcVtZs07ta2+zGvxz\\u002fog8J91fO\\u002fvpT7REkKCCoiRHpaYroSLGS4Vit3VhibDrdUc1yXYegNMc8n\\u002f+jgCCXxI\\u002fteqRPwXwtxMBtX9gAQ4sODZ7ul\\u002frPsxjIeT4c847zfqbsL87LKoceBoek7n82FlQrp3VWW31328F3t3WeKPVqWnbKD0fyF4zMRBuCqEVvWvPHkbvmT4lSBXwI0atQeIP9qLC6czIPsVAwLtvLlA89idUoYzTK5nYjAOSVo0fa\\u002fQunBTGsD1iL8b92yzqWafJ2wAcR8XVoyA8nqromVxmRECMR4s3zAnHX1InbissqtO2XfLA2VaPWyGQsx7UYTQ0GIwBIhYKXly1OCMO1I7FelFS2\\u002fP2\\u002fvz121yUqRedoCZblPv7uwPb9Ra67q5ZP6Vq6n9x81eRnXv8VALbtK0qMVf9pxXR3xt9qc779+a6TBbUeD+jGVaKdZti3Ptt13bIJ0RoFReHRdUPV\\u002ftbio1qPfjGr0mffn7MY6UQWJVZdmzxlUVzuo8c\\u002fP93R1XNkZcr0v4zwdxCrTCC+JnnKZxW7ASBZHjk\\u002fdtTs6OGj1IleO6yd1de9VPBzod5jjrLf6k78KWNuXJ\\u002fKUDRZ9dsb87+v7jxJUwgkK1OnL4rLTVNEey4I9Zb2A60lWxpP0ywTIVYird0F830F8\\u002fsCgOfyf0Sy7dOjsu\\u002fPWZyl7Bwr48Zy3MaGk28Xb9Hau8qp7W0p+qb6wMqU6bwFB1++ruanbl2wr0qa9EDOku6DA45pK\\u002f5z9pdKUwsAuP4PyM6mMz\\u002fUeOQFhCR1a\\u002fqcm9JmKgToILkOh3lt+a5vqw+6U+Esxz2fvz5FHjU8LMFzWS+C+VrdDdz2hQ0or1NQCqjIcPl1aPS8UaSvDgQEiDjwGJnBcl6KzATCywld8HFAo6EhjuPO5x2x3fuz9+O8pgMndLYULCAzslMOPXcfGg2u3UWV6w6cvm7qmIFIuIfCG+xH9W2dvRyGp8SIBIFdNM2Ij3Al3Gta+lL6z384o4dhAbOa1liNr5NkhFA8w2nfJxDmAiFk6HKO1QlFU8Sy69EH+OeW975z3\\u002f7s7usmpvXlHCZQLMcV1DUfLK1p0hvbzVaaYeViYaImLDs2cmpmcpgMPenC+iBcKt1w+01fHD+1s6yiQtte26GnSDJMKpmSnDg9NXnFmFG4ngzWqzlTsorKm3ceLDlwonLs8IToSJXV5jh1tq613eRa4NI5I2ZM8DLxYLveYjBaTRa7yeIwW+wmi93m6Dz52XW4tK5JJ5eJ5DKxXCpSyMTRkUqBZzZ8\\u002frTsnVOz\\u002fzhUAgBrfzj0644zY0ckyqQibYfpdGG9yWJPT45Mig3ffaSU\\u002fyi+y+ePPnSyEgC+23jiu40eVQjEIoFGLR+eGbts3qgpY1P5TVho+qJiL\\u002f\\u002fuBE169+ytm0oofWncjdfsed1Cd54wf1dzaHny5O45tZ6sTJ3eZNVdlTRpYoSXdZtvRFjiR1P\\u002ffN+RT07xaqfQHLOtMX9V+uyu5fx2sr1yR1O+6\\u002fas6GH\\u002fyF3RPbvqkiDTrEiZuiJlqtXviihBe1\\u002fB\\u002fL5+qDl8sLWEH7kzc\\u002f6fsxbyI24kQSxLGD9Bk3bPkY\\u002freVNlvlW0eU70cH+uJQQHy3EvF\\u002fzCeWbi\\u002fjJ82cpU71cFJkakfznj\\u002foePfYZc5\\u002fBHm9344pkN\\u002fIhSKHlv8p05Ku+FwsJF8keHL50bM+KhY5+6a8I4WPrved9\\u002fOeN+3wXxg\\u002flaXg3o9oUNnO71PVzB2LCn0OgAIwkJyyHzx7AsZ+tzxXCW67r4N4jOLz0diliuc5BKH3h9LOFtJQw1obOlYEPOwZKufg+Yb226zr1AauCjqOMjO69Uteg6T6UHSMBHSBiG2a0\\u002fUILUsKhNBKFsb0yXqf4mFM8AoK3Gd62m9+TCXPQBgavV6gY64c6w3PdH8t7ffrjV6H3CQ4ok5g5Pv3Pu5NzkWLQNC5BCLLpn+uR7pk9GGzDMP4tmDr\\u002f7xplP\\u002fOfn6vr2Q6eqkNZL54x48p7FSNDlry9vOFvqfazc17+g3Tbfe35l7jD0l+fvD11GkMTOA8UA0NZhcneoB4DstOiXHr9yx4FiHwn3aePTLpk5bNu+rke52R10Y4u+sUW\\u002f80Dx0nmjnrhnEUl4TwViocDCOPjdnwHg1ow5PWVvXSLFyqUJ49z9WFmO\\u002fabqwJOjrvJYqGeRYuULY\\u002f29hi0iBc+Pue7q3a\\u002fRXNdQ7hPtlf4kprurMre6bixLGP\\u002fM6Gv8WTPdFVp6FZz3Fczvy0Tb3irezI8sTRjXU7bdLU4a\\u002fsKY6+849D57biIIB0t\\u002fXbk\\u002foG71A+pgW0mxoYEfWRI\\u002ftqdsu4uIFLw64Zbr9\\u002f63e2kd3z4s3eEunwIAJEG8PmFVTxlwt\\u002fGatH+Pu\\u002fGRY5+5I5Wmlm+rD96SNou3FCqYr+XVgG5f2MAhCRlJKFjOIz1BMx5Fq4KDoiJoFn1dljWRVB8T7gzbORfO4AqRP6MfsefxjhjWSyJMQIbK\\u002fJM+hM6Wgg0tToY5Ut7L0GHMzero7AGgVni5xOWbQtp5UGG1dT7JAMEJdwwLGEtXSuS3EYQSAICQcZ19IgRS5YO085jF+G+lputspG9qtZ0DZAZIu8ly9ycbCuqb0QYehuV2FJTvPFu+YnLuE1fMEQvwzwWGDZrROfER4fJPX7nl5215Ow+WVNe3W2yOcJVsdE785QtGT8pNQR\\u002fQf4QC6vlHli2dN+q3HfkFpY0deotcJkpJiLhk5rBl80cJBVRKQo8nP+u3nFrzzX6DyUZRZEKMOkqjEAg6e9DTNGuy2GsbOyxWBwD8\\u002fseZ7LToay8d5\\u002fF4LJTkd9Twc75Ckpqo6aWDNgBMjcxyJ3AB4GCbRyfo\\u002fhUrVc+NGbH9XM9ZACgxeL\\u002fa5KdMZeyTo67yJxs4oPr2voL5ff1ad9zdLx4AlELJYyOu4LX3aJQ6aWHs6K2Nee7Iz3XHVmct6Km\\u002fc5Btqj\\u002fFvysgqPuyF\\u002fEjXsko0erMBc\\u002fn\\u002f4g29MzgtG5qOMmPXJE4cWx4Kj\\u002fSk5lROfNjR\\u002fHndP2iYs\\u002f1KdN66ngezNfyLUS2LywgQkG83enxs8ByFpazkISMHxxoAjLSDuivk5OpF1CRSNBPIZLpZtiuET8XBifTgob85qC9dPUVUH4Nuhp0IbKlYP545IvftuV39RzKjInY8JdVvHbUhmMF648UlDS1mmzo0KuJ6Ymf3r2CHxGQxLGKuk92HcuvbTLa7BEK2eSMpD8vmJIa1dUpu7pN99HOI0WNrWVNWifDAMDVr3\\u002fe9RQAd8yd9MhlM123LQ7n5Kffdv2RvT6zi59vsA\\u002fPDAF+Gv2LOHf8wLABzxXhPvZg2IGtJ9aXwyMMu+iR7m2HJMNYpuu8VyCabjW96b7bZ7VaHRrqP21G863vf1\\u002fV5le9Ko6D7w7nFTe2fnjHcoUEdy\\u002fCsKCaMSF9\\u002f\\u002fd\\u002fcd8VCQUrLhu\\u002f4rLxvEV6sebFG9FQn0wdmzq1h6ovyB\\u002fp9tXPR9\\u002f9cg8ATBuf9tjqhTGRXiYqYBj2\\u002fa\\u002f3ufra\\u002f7ozHyfcQ1mr3SMfkSiLEJK9F0xMk3ucGzdadWbaLhd4KW\\u002faLyZFZvAT0\\u002fxuvH3w0LBL+5ZJ7Hd9eF9B+7444L73rAZ+TdIU3w\\u002fhW5Y4gZ9wtzKOI21lC+NG8xYZHFbGsbvlLD+yMG5UrFTNj\\u002fTksoSxrxX+xr8I4dvPdcfcpVoAgCSIuzIX8Np7cXfWQn4SvMNh3tlUsCS+c8o4RDBfy7fQ2b4w\\u002f0mE2UgaEQBsjgKZeBISHFAiQYrZfgAJ2ukKqagvqyIAOBmPsSyDxUFfaJ1bnUwdGvKbg65AQwBiQSoaCkkhsqVg\\u002flg4KjNcLtWZreUt7eXNvQxEeOb7rT8dLRAJqKmZyUqpOL+mqUarA4Bh8VET0xPHpaJjxf44W\\u002fHGpr0EEDFhCpbjmvWmX08U7iwo\\u002f\\u002fr+lRkxnT2WdGZrbbteLhaNTop1zeo3MjFGKhK6nyQxAq3UX92m+\\u002fn42ae\\u002f2+L7mV0CeoMBPXOgn0b\\u002fCpNLWnUmADCYbWhbb9wPUcr8PV7tG3yUg2EBI6lE5twRAEmlO+17JPLbO9s4G3D9MCzF9VPl1mIwFTa06sxWi8OpkUsjlPL0aI1G3sf+X8\\u002f+uN3PbLvb6ZrG+z\\u002f7+ePV11BkZ+9UDMMwH2x25yffHwQApVzy3MPLZOcG7iEoivzTimnrfj3KcVDbENjvEhZkeodHklcp9GvkvqLbYnqnxf9sbKDS5FH8uw6WtjFOCdV10uK\\u002fNEX01MgsNDpI+vC+gvZ9Fekbas0e528BpcvHh6eSBOmejRMATnZUBfQMA+SMrpafmAaAWdHD+Xd9EBDUlIjMP5oL0IYe7G72yOyPUadESbxcoexJmiI6SxlbamxyR7Y35feUBA\\u002fma\\u002fkQUtsX5j+pMFcPvyFBs+NokNOIYqGX8Tp2Zzka8pvN6aXwnR+8XMXsPs+nnzhgHHQVGh3i7M4yDhjC2wfVK7vTa8I9Aw2FpBDZUjB\\u002fLB03bOm4YQDw45Ezf\\u002f9hG9rMc6is5qejBWKB4JsHb8iKjQQAmmEf\\u002f3rj1vzSaJXiiSvmog8AeGPT3qVjhz12+ZwIhQwATlY1PPz5r1qT5a0tB\\u002f676nLXMmNS4lw9wV19zAHghesWZ8V6pLYRToZ5+rstvT6zi\\u002f9vEAJ55j58Gv3LnXAvqW1D23pT1dx51hmmQI97+xfOnWFYwITiKU77Ho6zAoBIMt9h22Y2PEc7jjusv9rMaylBP5w\\u002f1Gj1AGC02j\\u002fefeyyV9bO+9eae9dueOq7LS9s2PnoV7\\u002ff+v53c57\\u002f4MZ3vlm757jNGdjsOptOF+8u9HL40qujFXXvbPPowoZhGNaTylqtze4EgMzUqJ6y7S5ikYAkSdcNtA0LJcjUkU62q1yJD3bPlCUASEhfaeLzpOxWisTPv7O7GVHZaGjw9OF9Be37ytPV8O+qRfJsVRw\\u002f4puYEqbII\\u002fkR\\u002fgyxg6i4W92eKZGZSMSHCb3NiOvmZJlCfT0\\u002fMjtmBP+uP+Z4PuSotpx\\u002fDcMtmK\\u002flW0htX5j\\u002f5JLpaAjAbNuPhgaY1NuMWWb7QTTkH5rV0kzA+RoAoEi02ykAMKwRDfnH7ixmOSsaHeJYzmZ3FqNR\\u002f5jsXtYrqTjgy3uDIkS2FKx\\u002f7TxTDgALR2e68ssAIKDI1fMnA8C+kiqvhU1GJMS8uHKJK3MNAONS4\\u002f+8cAoAHCo73+Esg\\u002f7Mffg0+ldOcmdnlOLaFp0pgB9Pm4POL+88xkuL1Xg29jOccMewgIml1wslczjODABi2Q2UINtm+lDfdqWx4x6W1clUj6EPCJzBattwrOCyVz99fePe6jYd2gzActzpmsZXf99z2StrN54K4Djmi30n0RAAQcDw+OjLxuZcO3nU7GFpCeHe+zp9svtYoF3jMQy7OJFkZ2k8V4l2H7YfKGIYFgDSkjySblio0YgU\\u002fLttNr9yCi02j8ImJEGoRZ1H8ANB4EfVFD\\u002flhqegocHTh\\u002fcVtO8rv8Mj4Z6hiPE9NWt3EWKPPzXQ6UYHSInndKkRYmWYsJePgi9Z5qtvGl+RocHBenSeyFbF8u\\u002f6A7nIYaHt7olJ+YL5Wr6F1PaF+U8mGiOgOnMcbkbbXgft8Tsw0GTi8US3kfoW+3GG7cskWCbbXjTkH4pUdk+nOHm1RgNitO1GQxcEU5+yzDTTanXkI0GCEHu91hKCQmRLCX0E4eXwhgv8Im5wNBtMAJCg8bjSlqgJAwCW5dqMXsr9XTN5FDJVyciEGAAw2ex2OrB+k4hBf+Y+fBr9a3xWousGy3LrtnvJcfXkx9159nOdVsdndz7JAEH3EBiG9UogGqsM\\u002f4AkIwGAICRhkRtkyr+IpFdIFHeFRf0uFM9DH9Anf\\u002ft+a7up99+pZr3psXUb\\u002f7fFr0OZ0ibt6Rr0KHBsStxv\\u002f3fbDw\\u002fd9MoNl\\u002f3zmkveu\\u002f2qLX+945O7rh2ZGIMs6WSY97cfRoIYhmHdpSRopBIhABRXNK\\u002ffcgptBgAAh5P+aevpl9\\u002fvHNt4+YLBLyKB+TAsLJ5\\u002ft9VuqLe08yNeIb2VMxQxJDE0Dj4TZQPb52WgBe37KjR49JhOkHmZU8s3hcBjPK+JtrGcR\\u002ff8QVFj9ujx6n8C3SXJs9u+D+W88iwu6Qr0AKxX3R9SYWxBIhDc1\\u002fJtqG9fFzFCLVuOxoDVmj5BYwOJJKQy8WQkyAFtsG5Ggv7QW35HQ\\u002f4iKFKJhKyO0+A5wMhPesvPaOiCoLeglVX8obdu7P4xKiSzCKKXQVchIyS2lNBHEegWBABsaExi3J2roq\\u002fWaOYHW41mACAI8Drdnbv3t5tM3LnYeU7XOejP3IdPo3\\u002fNzE0TCjov2Hy+5Vh+BZrm8qqqqf3DXzvLNhAEzByd5tnez9ArwxiGBYogVVLlI2g0uD7cecTmoP96+Ry0wdOhMvSieqRS\\u002fv6frlZKxPwgQcCUjKQv77n+b99vQbrPbzxd\\u002fH9LZ0Uq5fwghmEYQiIW3nzV5DXf7AeA1z7a8eOmkxNGJ0dHKAUCyuGgO\\u002fSWmsaO\\u002fKJ687n+7\\u002fOnZV86J+DKBlgwpStiEmQaftL2+5pDDw+7jLcIiuHYDbVH+ZGZ0cP4dwPiZJkqc0uJoanK1NLuMOmdVr3DYqbtdtZpZ5x2lnYwTptn793zoRSgVVwGyAC9r6B9XzqHx7nWL3XHf6k7zo8EiuU4E21TdauiE2RG2sa\\u002fG1ChcwDodWSAGzIFLkkQkWIvCQjforv9eU02L119g\\u002flavgVt+8L6XYTiljbjGgCPLqha06dq2XKpKHi9j8Nkl3afN7XV8I5afm1AFcNppsVk+wON+k0iHG62e5TcZFiDxX4s0FLdZvtBq+MMGr0gWBzHrY68gNYNjqNbje+hUYAw6aVoKISFyJYS4khSSRACjvM4yLGH6mQGs4enfX84f0te6T0Lp8aqO\\u002feeX+w9AQAT0xPl57LSfAOXdx70Z+7Dp9G\\u002fIsPkS6cO37DvDAA4aObB\\u002f234+22L5o7LQJfjOVFS\\u002f7c1G03Wzpk2ZuWmp8QG3E0kIDjhjmH+6mgaQwqSSCqZopJJgfv\\u002f+BDZjj7fd2JyRuK8Eb5+YgrqmpHItZNHIdl2N5GA+teKxRUt7UUNXSOFGZbdeLp41czxvAUxDMO8uHX5VKeT+XLDEZphq+rbq+q9965VyMQ3XTnp5qvRrmpYCLo2eer\\u002fija6766rOjAlInNaz7WY3yj8vdaidd8VkYKrkwL+os20\\u002fY\\u002fmgp1NZw63lSHVMAaUzOdMoecvCO8rCN8XB5zJMzHdL2yMY9AT7ma683zMRUoFduro\\u002f\\u002fJGp8cH6P8D+aSUiCQI\\u002fsgAJLfuEszX8m2gty9s4IgEqeHy5R3mH\\u002fhBjqNrtfdlxGzs3uN7gKhly5t0\\u002f2I5j1XaTld0mL\\u002fVyG\\u002fkB31r0r+MPElAZOKJSMIdALSmTwNKuHOcs6HjH2j0AtKoeyE9+lvwu9pYu\\u002fkLJ12HBClSGSa7HAmGshDZUkKfgIxxMh5D5ayOUyxnIQl\\u002fr1sHzdzhGfNHZuwsKL\\u002fq9c\\u002fnjshQSkR5NU0Fdc0qqeSpK+ejS3fyd7UP3CA\\u002fc58+jX52x7Ip24+XuhLoBovtL+\\u002f+MiYj\\u002foqZI8dlJiTFqF2FcTgOGtsNeeWNGw+ePVBQ5T54EQqou6+YxnuyARESiUIMGxIo4UiWaXQ6dzo4Ez9MUvGUIImkUigqyZWIF4gGJx\\u002f97A\\u002fbfn8sQSX1GJrNV9LkMT4aACZnJCERPpGAemHFomv\\u002f9xU\\u002fuLOgHCfcMQzrFUHA6pUzls4btWXv2ZMFdTUN7QaTzelkRCKBXCqKjValJUZMHJ0yfUK63OesqljoWJE85ceaQ3XnOk2zHPvI8c9vSZ99Q8p0jWcZ7lJj43sl2\\u002fa2FPGDN6fNipOq+RHfHCz9ffWhteW7+pDOO3+BFiL3X9DeVxC+L5PTHgrlXwaC2fNCgoQKrI4BRZACgqI5Bm3oxui08u\\u002fKqD5moqWUiH+RoPv8txDc1\\u002fJt4LYvLAhiwh7XWzazHidEYKcry1uuTI38VCRI5sf9RzNtButmjeJmtMEbigxTy1e0m75A4o0dz0qFo\\u002fzsQay3\\u002fN5h\\u002fh6NBkIuntx9BgOd5Re1\\u002fDqlpJeRxy4c0LXtD9mcBWjDBcRsP9Coez5O\\u002fSza4I3FcaJR9zwaBQiX3xSCGVjfQmFLCX1y8SSdxSPhznKWdtNXkcrV\\u002fGAoIAh4\\u002feZld6754VhF\\u002fba8UoZjo1WKa6eMvmv+5Pge5sC7gIXCpxEfoXrujsV\\u002feecX96Ho6fKG0+UNAEAQIJOIKII02eystxo7f7l+Tk5yNBrtbzjh3g8qjG3LdryNRoPiutSJ\\u002fxi7DI1iA0MV8bXrBseZWKaBZRpZpoll6hmmgWUaacdxB\\u002fsbxxoBICIevSbfZyIBtWh01pSM5Jz4yDCpRCwUGK32hg7DiaqGX08WNnR4FDhrN1t\\u002fOHLmT3Mm8oN8eivagyMnLgqJIIbHR0\\u002fNTObXojlV3Whz0hLhBfjr8cqZrWvLDqDRnsVIVH8seRSNYhjGEx8Tdvu1026\\u002fFo1jQ5GYEr4wduXdh9fYzuXXGI79tHzXZ+W7UxVRcVK1hBKZaVulqbWlW4mJuTEj\\u002fpy1AAn60GY3PnLssyLPiSsRQpJSCWVKgUQqEEkokZQSOlj6mLYCXS6UBPN9BeH7srMBZ1qHKMJzAjF\\u002fCEmKZnpPuDOes8Nx3coW+4nzvPIh9DbRbjBfK6Tcf\\u002fibnY0e15N8mxSZ+tnM29Aodo6Qio8P\\u002f3td+2NI3O4sKW9eFh32iEZ+E0H4eymdZtpM9v16y89G606SlPqfRoxWPaQzf4\\u002f0T2c5W1XbrckR78vFU\\u002fjx7jrMP9S3\\u002fx9S8SNQCslsIRXXbaJUrlZ7b0rkJ73+DTTTWtfxmNG6HW244LQZPwRgY8KeIIkee4YBgNl+sEZ7D8d11ht0I0lltOo+JBj6QmRLCXFy8VSdZQMSbNb\\u002fRy6e4ueVs2B65fc9xyrqX1q5ZNn44WjbeROfq0jeYbYABDZzzKAY0E\\u002fDT3PGZLxw52XPfbrVPQ+qC8eBu3IpgiSJB5bPXDF3DNowAC7AlBmGdVdrbt9Yd+ZIW1W1uV3nsDhZRi2SRUuUEyKS58XlTIkMbKoEglBQgmxKkO26yzINTvtep32\\u002f02HjwEQJAns2H+aNyPjnNQsjFB4X86OU8vRozcyc1PsumfrV\\u002flOvbdpLM11Hit8cPH3b7AnIvNJuxnP1qlxEAkot83XQ47Jiymh+wt3JMAV1zRPSEniLYBiGYd717w5o0I0MS\\u002fzvxFufOLmOX7mbA67S1FJp6nHixCsTJz4+8opep990a7ebbj\\u002f4XpNVh8Q1IsX0qOwJEenpiuhkeSQy2SYA5Otq\\u002f3TQS+HXEBH89zXQ35eIRE8l5saMmBThq7qdP5SDXU8GAGQCsftCBQDwb\\u002fsJyW73BOk7b2G8nx\\u002f6xgFn9fwLvX6GwXwt7MIWLr\\u002fBbD\\u002fRYV6HxGm2vaHjmVbDO2GyKxXi6TLx5O6lM2imzU6X2+kKm7PQbDtgcxZDn679CKnYaNWjTfoXkTjNtFa0rIhQ3BqhWCUWZiGtHGc32Q+2Gt4024+4gxSpZFgjbyl\\u002fESCIUNze\\u002fW9gWH1l60qN\\u002fOYo1d1CystJk81Zorf83GZcw3JdQ51IQoH0hu4XLGdlWSPDGW3OErQNwMk02pzFFKkgCRVFygF6\\u002f+XvmzbjRwbr1mjVwyrpIopUI612Z5nW\\u002fLnW+KnXSyCxYU9S5MCWWh4gobClhLgw2dJG3XP8DQEAWM5a0XJNTNgTGsWNJNHjLobjaCfTQJHKoK0ePx7OJwkiQRNGM6yA6ueNhSLJtGhNZUv7J7uOjUiIcddSdzKMkArFC9sD+mn4b8nknLQ4zWvf7jpe3Hu316zEyEevmzN5eB\\u002fHlwQKPUrGsAuMhXa8mL\\u002fp55rTyGlPq83YajMW6Bo+Lz80VpP0\\u002fLgrMpS99PXm4ziz037Qad\\u002fjtO9h6DKCkArF06WKu4SS+RTVl62XnzR3uXLCiBevW4wE+SiSXDVrfKxa+ciXv7mD9R2GvJrGsSnxvAW7WBweZ1bh8h73XnxTMpIIAvidmYoaWnHCHcMwzLcB2gENugma9K9nPvB+ybZf60702kN2eFjCnZnzZ0cH1vPlxYINSFY6Qqy8L3vRZQnjKD+ywCFrUN7XgH5fCoGYJEiWt4anKaKvSxnwmphBIBeI2+1dmS9bgLlpB0v7WZpfLZLz79oYBwdcoBVXrIwT+XK9FsEP5mthF7wEzb8ZVmuwbkUbAJxMU5vxgzbjBwBAEGIBGU6SCpa1spyJ5czIBInnI1L1Z6NtZ\\u002fcq6gCs1rRWa1orEWaLBOlCKhaAYNgOJ9NsdZxEOsWTpDIt6tuy5sv4Qf9pFLdozZ93rznOcU7X3yAWZkqFIykynCCEDNtBM+02Z4GTaUaWF1KxqVFflTb1PrTIq3bTFzZnCcsZGdbIsAaWMzKsgWGNLGvkwNcH3m76st305bl7BEnIKVJJkkqKUFKkgiRVFKEgSVW4fLlEOJL\\u002fQH8khL9c3\\u002fGkO4fuoGvq2h8lgJKKcoWCeAEZCUDSTLOdrrQ5Cz0f2kUpXRChWIVGh45Q2FJCGUWGaxQ3txk\\u002fROIsZ23U\\u002fb1Z\\u002f7JMPF4kSKEIFQcMx9kZVs+wepptczLNNNMGwCZFvKmWLXc\\u002f8NcThWfqmo1Wu8lmL25sA4Cz9S33rt2glIgVErFSInpg8XSK7Dzo+uNs+eGyWpPNYbTZq1o7AKC+w3DvJxsUEpFCIlZKxHfOm6SUdpVfWzImZ8Oxglve\\u002fdYdkQgF8eGqmTmpd86bpPHsKNkHf14w5Yl1m\\u002fYVV81+7v1olcLJMB1m64OLZ9w2ZwK6qH8CfYMBGehPw385SVEf\\u002ft+KvPLGLUeLT5TUldW1IQUPE6LCJuYkzh+XNWN0Wg99UwcETrhjF7J2u\\u002fmO\\u002fZ8XG9ADGsSp9tobdn\\u002f07rQbJ0akoG09aG8aSYBQIBorkl4mFE0ViKb6PxzMq+NVHpXL4sNVf1\\u002fu1\\u002fHWotFZV04Y8fPxs+7IqeoeE+5InVU\\u002fr5SGy6XZsVHFjV0lCitb23ntGIZhGGrgdkChQEwKbbykm4wSSQQig8PKAqcQiFVCabI8crQ6eWpk1ii1r5lCvDrZXrm7uWunBgDx0vAPpqyO7a2eOACEcuevQXxfA\\u002fd9kQQZIVa02roK3HXwutIPacg4Ay0v+e4Pk+f0pD7ESML4d1mOa7EZkGCvmqwdSCRM6OVEN5ivhV3wCBAkR35Y3\\u002f6Y7zLoHGd3Mk3Qe3WlviCASolcU958hZ2uRNsAAMDmLPHarduNAEGy5i2yW+di\\u002f1GkMjni\\u002fYqWqznO+zgYu7PM7ixDo55IQpIc8Z5EmCOgomim65zLfx3m7yyOk2g0YBzLmVjGBGiRHJAIc\\u002fqQcFdIZsWp\\u002f4bUZOeAsThOgn9\\u002frUQ4PDniHTQ6pITClhLiolUPGqxbHHQ12gDAchaTbR\\u002fAPrShZ5tOF+8p9PhBMFht\\u002fMg9l0x1J9z3FlV9dyjP3QQAVodzT1HXwtdNy3Xnoyta2hmWJUkCOJCJXckfzuqgK1raK1rat+aXrn\\u002fkZh\\u002fT6flj2bhhMpHwi30nCutbGnVGhUSUHReZHq1Bl\\u002fNbQG8wIEH4NAKVmxGXmxEHAA6a0ZmsBrONYVilXKJWSGVijxF+QYMT7tiF7G8nf+412eFiou2PHv3+5\\u002fn3hov8OmcgQMSBg2NNHGvkOCtwdji\\u002fhPuuwgr+3VUzx4sF\\u002fm6et82e4JFwr0GPkHoi9rsOe0aMhp9wr\\u002fesHY9hGIYhBm4HNOja7MbVhz5wT8U5LTLrmdxrosQqz6X6bmPDKSTyj9xr\\u002fctKg8FzTsiQMljva6C\\u002frzR5ND\\u002fhXm3qS6ooBCXKIgr1XZ0hqs1tvMbeNXcri9+TdGUMEqkwtQSaBK\\u002foViAoWxWHRCC4r4VdDAgQJGrekInGN+r+iXQbDxqKDE+P\\u002frGy9Qabsxht6w1BiJMi3lJKF1rsx9C2QMhEYxM1\\u002f61rf5Tj7GibH0hCnhz5oUw8CQCkwpFGZhe6xJBFs22Ryj8DcI26f\\u002fXhyrFUlJsW9RVJKNCGoSYUtpRQRpHq1Mi15S1X9q2yE+Ld269CQz17dvmCZ\\u002f3r5ljapL3x7XUCinz7tiunZ6W4K6hwHJQ1t937yYZGnfHX44U3zRwHADKR8Mx\\u002fHvF4\\u002fDlZsRE9NbnMH5kxf2SPpfkCfWb\\u002f32BAzxzQpxF8IgEVrVZEqwf\\u002fp2Oghq9i2KD7o6l4d5OvHg2INpvp3aLdaLQHmtgCVcQ3Qsk82nnS2H5Xe9MofdvlFuN\\u002fnPYD3ad58UeeZ5Z8zvA0\\u002fl3fsmMjY8K6fk2q29CeRz3xfzBNkkbNv9tiCKyfF4ZhIejJd35b9MB7bboLpDtqSBnQHdDgYjn2waOfdmVvo7Jfn7iqH7O3AHC83eMKdKYydpzG331iKHevHpT3FYTva4Q6kX+3QF\\u002fnZC+EHno5nknkVpvBQgeQSuuele5JljIWKYVf7HNOXa9KDB6HkRqRIknmZb61YL4WdvHQKG7Jiv0jTHoZ2hAgqWhUbNjf0KgfBFR0Rswv4fIVaINPIkFqRvRPrj+bZs938K5admVG9E9CyvsgYx8kwmEZMb8qJXM674pGebYPbTSjBYBI5d2pUZ8LqMCq52kUN6dHrw9abe4gGPQtJZSJhdkZMRulIbz+f7HvhNXhvG32hNnD0vj1ygkCsmIj545IB4AGXT9cMBgS8KfhJ5xwxy5Y6yqOoqHe\\u002fFxzyupnjU5CKBRNlSkfD4v8TRObr9R8JBTNcNp2GbTXdzSNQBf2Q5uxa54QpVScHKHuavPDsPho922D58yo\\u002fSJB43FyrrPgK\\u002fMY1v+2HS5+5v2NaDRw\\u002fj+PVCQkSf8vvWH+Gtgd0KDa2HCq1NiVbntsxOUCwq\\u002fqZP5rs3kcoI9WJ\\u002fPv+pbXUYOGQsagvK8gfF\\u002fTIj3mJHSw9DFtOT8yRGWrPBJnHHBHtL0UheDzP5EtIgXjPS+97PEsPeSPXZ4PGR3ufe0K5mthFxWRICk58sPs2J0axc0CMqDqB4RUNCpKeW9mzObMmM0axc1ou39IQp6oeSM9+ke5eDra1o2Aio4NezI7dqdUlOuKMKzOY4k+kYpys+P2xIc\\u002fLxL4VZtLLEiLD\\u002f9XZuwWiTDbHZQKQzfh2AcM29kPTCmZlxO7J1r1oB8JdEIhmZ0R82tC+EskEdR6FEEw6FtKKBML0jKif4lXPycSpKJtPSNAQBIe05MMkGadEQDCZN7Xyeo2HQBEKYPxl4QC\\u002fGn4yd+CEhg2tNgY58FWQs64zwABAABJREFUj65k\\u002fjDR9uPampnRmWgDD8fZWaaGoatZppqha1i2iWWaWaaJY1tcfdu5Pg0T6zB3jRYPl0l5LX5R837sDAOQDVdKPAp7mWz9n9PHMGzPyfJWXT8MH\\u002fHzef593zI0hPWHgdsBhYL9rV1j9lVCab\\u002f3bHWyDHLhQe13pR2WYw+2BTCwIJgG630N9PcFAGPDUyLFyjZ71+WEb6sPTovqyh8NUaPVSSJSwJ\\u002f4dE9L0dwYf0sYI\\u002fX6fVsYN\\u002fpQW6n77hldXaO1I07aa1qqU7mxudKzQ\\u002f28nv\\u002fOYL4WdrERC7MTwl+KD3\\u002fBYj9usR+1OvMddI2TaWJZE8vZSUJMknKSkAvISLEwXSzIEAuz5OLJfmRg\\u002fSUXT0mP\\u002fs5OVxqt28z2I3ZnqZNt4VgLEAKKVIkFaRLhSKV0nkI8iyA86vmyXB9HESFIQhKhuF2jWGV1nDbbD1vsRx10jWuOR46zE6RMQGrEggypaIxSMlsmnth9vHGYbNloWR0S9EdGzK9oKASwbNfhKEkqY8Iej1Y9ZLT9YbLttToLHHQ1wxqAowlSJqRixIJMuXiKSrpIJEjhPYe\\u002fEjVvJGreQKM90yhu0ShuQaM9k4nGj07qy1fTXdC2FJEgtb\\u002f+ZpeYsMdjwh5Ho\\u002f2HIEQRyj9FKG8324+Y7UesjpN2uophdSxrYDkHSYhIQi6gooRUjEiQJhZmSoQjpKLc4FyYyY6L2l9S\\u002fdX+U9OyUlIi1e54i9700R9HD5RUiwWCS3I9uiBcwPCn4SeccMcuTGd1jQzHolE\\u002f5HfU95TvMLRdyzDVLNPEr0BHkOGUIFUomkQJ0khBGkWlUgJ\\u002fR4jzOZmu8dcSUcAbpkjQ1WHN5uw6OewvEs9q7w76QhgtjmEhhWW5o2drUuMD6u3iRX89D9ZnA7EDCh38btom2lZjbkuWR\\u002fLaz5eQpMSU0M443RH\\u002f63hsbDjVZNWh0dAwWO9roL8vACAJ8qqkSR+V7XRHDrSWHG4rmxIZ6iuzbwqBZEZUzh\\u002fNBe7IlobT92Rf4k9BnpMdVQ3dZhb14ZLY0f8t3GiiOztMcMC9X7r9n7n+1sd4v3Qb\\u002f65GrLgkbjQ\\u002fwhfM18IuTgQI5OIpcvEUtCFYxII0sfKuSOVdaEPPOK4\\u002fT20IoGSi8TLReFDeg7ZdZDjo2uu5EIRYJV2iki5B4hehQd9SQhgRgp\\u002fMn+ZO3HGmrLKlfel\\u002f1qZEqsPlMo7jWgymRp0RACRCwb+uX5wQ3vsRwoUBfxp+Cjivh2FDQp0lgPMcvlpzjw9k6FJSkCoUTacEaaQglRKkUVQaQfbP74hSItaaOqvK9KEmjMnW1W9OSHkfLc5yAc9U44Yk3GmmL7kkDLtobTpQuP6P09VNHRabM0Ily0qOunT68AWTOnuArv8j79e9Z8rq2uwOut1gmXzb6+4HHvz4YepcXbyCiqZf9545WVzfpDWwHJcUE750xogbFo8nic7uUX4+z9bDxU+\\u002f97u7ace79yllHkNYXDYfLPx+x6my2jYAyEmJvuWySbPGprtb12098cbXu7a8efemA4Xrd+U1tRkiwxWTRyTfe+1MtTLgMToXmIHYAYWOWGkYnPszWY778+E1t6bPmRyZEScNl1BColtnvT6IFCvrz9UcB4Czer86ZzXb9G8Xb0GjoWRQ3lcQvi8AuCF1xrfVB4zOrhzu3\\u002fO+\\u002f3TaPX5OCRuyLksYy0+4O1j6vZJtz46+hreIFxxwbxVtQqM+yQTiG1JnrCnb4Y5sqj+1LGH8pIgM3lLe7WkpRGq8LE+ajBRq5wvma2EYdtHr++knhoWUcLn0u4duWnfg1K6zFRWt7bXtegFJhskkkzISp2YmL580Kkp1EVVQwZ+Gn\\u002fAREnZhcp\\u002f1BcrQ8wPDY0+jof6jkkrcCfd2k8VBM\\u002fxO671yXUt0UUm95M7g\\u002fHq+E+cyei4Uiad\\u002fwDB\\u002ffbHx6Fvf7R2WGnP13FwAqG\\u002fRHS6oTo3XuBPuKXHhV80ZzbDcS59tT44Nv+XSie7Hkrxt7ctNxw7mV00dlTJnfAbNsLtPlP3vm90Wm2P1VdNcC\\u002fj5PNNGpbz31xU6k\\u002fWjnw+V17W543xvfbvni03HUmLDr5wzmuO4A3lVf\\u002fnvhodWzrlpyQT+Yk+9+\\u002fvZyqb5k7IWTs4+WVy\\u002fYXd+eX3bx0\\u002ffwF\\u002fmIjQQO6DQsShuzOaGrr1hm934WuFvvHYPQpJSCqRhIlm2Mm50ePIlcaM1oq4pvnsyLjyVn5jO19We1deNCPOYmRPRZNU9cvxzLa+qSQgalPcVhO8LAFRC6QM5S148s8Ed0dqNtx1875XxN\\u002flfqr7E0Phr\\u002ffEpkVkzo3LQtkEyK3p4miKaX0Hl17rjw1UJK1Km8pZCvXr2t3xdLRrtzS3ps36rP9F4rl88B9z\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r4AYUT7tiF7IVxV9xh0RfqPWYM82pebM4L43qfDWyghculb6664t5PN3SYu0aC++mvl8+ZM7yz0uujl86MUEjf2LTfyaBFhCemJbx241K5WBQmk+gtNgBgOe7fv+z66VjBNZNGTUhLiFIphBTZajDvL6n+Yt+J+g4D8gyXjfH4pcOwoDnQio7\\u002fCH0kSd5y6cRDZ6o3Hyy0OWi1Qpoar1l91bKFk7uKw7jde+1MkVCw\\u002fUjxp78dkYiFmYldtZtyUqJff\\u002fiqD3468NlvR4QCKjcrYc3fVhZVNXtNuPt4HqPFfteL3\\u002fKWhZc+2+66sfqqae7LALcunZwap\\u002fly07GfduURBJGdHHXPNTMWTPLyN2M9GXI7IB+K9R6ThfRtKkgA6NaFv9tlXqw\\u002f4O8LwzAMwzAMG9I++v0ww7IA4HDS\\u002f\\u002fthL5Jwb9H1OKdO6MAJd+xCphbJvpx9+4t5mzfUnGJ6mLRKKZTcnTP7tsxp3U4sB0ducuzX9628+5Ofqtt0aFsPZCLhM1cvuGL8cH7w1lkT5o3I+HzviQOl1c16k0oqSY0Kv2rCiKVjh7kmWlw5dcwHOw+7ly9qaP3Xz390Pb4HCeGqKyf4W8IVw\\u002fqXj+lSQxZBwAPXz37gejTulVgkuG\\u002fFzPtWzEQbAABgem7a9Nw0fiQzMXLZzJH8iIuP51ErpUc+fRSNejNnfOac8Zlo9JwbFo2\\u002fYRFaimrRlJxFU\\u002fAFuU5DcQfUE53Twr+r5FWpDkij1WPWAY1Ywb+L9Rf8fWEYhmEYhmFDmpPu6jxqCcocp\\u002f0OJ9yxC5yUEj0\\u002f7oq7smf+XnfmaFtVlUmrc1gZjg0Xy7JVMbNjMq9IGqMU9vFcdIAkR6h\\u002feuSWrw+c\\u002fnjXUd9d3QkCluTmPLxkRqImDG0DSI5QP33VfDR6zs0zx607eNr3lKrd\\u002feWyWX2rCo1h54lmmaNt1WgUw0LYUNwBeSUhBRam6zBX77TKAp+d2MHSe1oK+ZEMRYhOEjvU4e8LwzAMwzAMG9IWTx72w67TrttLJg\\u002fJCYFwwh27KCTJNXfnzL47ZzbaEKrEAsHtsydcPzV3f3HVgdKaoxV1LQaT2d55\\u002fiwVCYfFR83ISlk6blhyhNrjkX7TyKVPXTn3iW82ow09W5ybvTgX15TABsfJ9lorL4WEYUPFkNsBdRclUfEn4TzcVnpV4GXBPyzdYXB2XUIWkYKxmtSuZqz\\u002f4O8LwzAMwzAMG9KeuHH+\\u002fHEZda36xGj1lOHJaPM5X\\u002fztxogwORr1qU1vXvWvr9HoAMAJdwzrI4apcVo303Qpx5kJQi4QZAmlSyiqxx+CPpCJhJeMzrpkdJbrrpNhDFa7WCBQSESeC\\u002fbR5eOGV7V2vL+jq7CMD9OzUl68bjEaxbBgOdg69OrJYNiFYYImnZ\\u002fAXVO2c17syDChv7Nnsxy3tmLXZxW7+cHF8WNkVP\\u002fsyzAE\\u002fr4wDMMwDMOwIY0gYMqIlCloGJWZECESBpbZ7q98Wq8C+7MwDAMAAM5ieNFq+gCgqyyvHQAML0gVd8tUTw7QxGJCiopQ+HvC7KcHFk2PCVO8\\u002fOtum5NG286hSPLWWeMfWjzDVfwdwwbFgSFYwB3DLgyXJYxbX3vEfbfFpr\\u002f94HvPjFo+TuMxqUB3TpbZ3Xz288o9hXqP2X2llOiuzAX8CNaP8PeFYRiGYRiGXfAkIkGg2XYAkElEBAEch8b7XcB\\u002fGYZhVtOHVtN7lCBVLLtZIBxGkBqONTP0WZv5C6vpXZKMlCjuQh8Twq6bkjtnWPpXB05tzS+t1er4TdEqxYKRGTfNGJcWFc6PY1iQGZ22Al0DGsUwLCjGhKcsjhuzpbGziiIA1Jq1dx1ek6OKnxaZlaGMiZaESSkRANgYh5VxNtt09ZaOYkPDqY4qG+PseiIAACCAeGb08lipGolj\\u002fQV\\u002fXxiGYQGJVN4ZqbwTjWKBkInGj06qQ6MYhmEDKUwhRUN+IAiQSURm64CXq8UJdwwLmN3yJSVICYvcSJAqd1AoniaW3ahvvcxm+XxoJdwBICZM8eilMx+9dGa7ydJsMJlsDpGAig1TxoQp0EUxbDAcbqtkuK4BJRiGBdnjI6+osbQhHZ+LDQ3FhsCuhIlIwTOjl18Sl4s2YP0Kf18YhmEYhmHYhU3dp4Q7ACik4iAk3HGBCAwLGMPUCSWL+dl2F4KQiqSXMYzH+e3QolHIhsdHT0pPHJMch7PtWOjA9WQwbHCphNL3J985N2YE2hCIcZq0z6fftyR+LNqA9Tf8fWEYhmEYhmEXtvNIuAejjDvu4Y5hAaPIOOC8Xw3j2A6SjEWjGIadnwMt5WgIw7DgkgnEr4y\\u002f+XBb2cdlO091VHPgb+FDGSWaHTP86qTJ43urIY71I\\u002fx9YRiGYRiGYRekIx88BAAE0cfZExVSMRoaADjhjmEBE8tvtpnXypT\\u002fR5BqfpxlWx3WTRL5bfwghmHnqcGiqzG3o1EMwwbDlMjMKZGZrXbD3paiEkNjubG52aYz03YL7WCBFRCUlBKpRNJIsSpBGp6pjB2pThylThIQFPpEWFDg7wvDMAzDMAy7wFDkedVrmTEqNVIlR6P9DSfcMSxgEtkNTtsOXeulYtlySpBBgIjjzAxdareuJ6loSjjcYdvIn\\u002fNYJF3KezSGYYE50IrryWBYaIkSq5YnTUajWKjC3xeGYRiGYRiGudyxdAoaGgA44Y5hAWtvGu26YTX+z7MFWKbF2P4nJBghxTO2Y1jf4XoyGIZhGIZhGIZhGIYNFTjhjmEBk6meRkMYhg0MDrjDrZVoFMMwDMMwDMMwDMMwLCThhDuGBUyquBsNYRg2MAp1TR0OCxrFMAzDMAzDMAzDMAwLSedVZh7DMAzDBtSBVlxPBsMwDMMwDMMwDMOwIQP3cMewPmKcRTbLV7TzNMcaCFIlEOaKZdcLhJ3l3TEM6xcHW\\u002fCMqRiGYRiGYRiGYRiGDRk44Y5hfWE1fWAxvAjAAAAACcDSjhM282cy5aNS5SPIwhiG9Y2doU+016BRDMMwDMMwDMMwDMMuUPVt+roW3bjsRJGAQtuGCJxwx7CAOWzbLIbnBaJJMuXDAuFoggzn2A7amWcx\\u002fs9ifI0SZIukS9HHYBgWuOPaajtDo1EMwzAMwzAMwzAMwy5Qu0+Vv\\u002fbtbqlYeMnE7L\\u002fftghtHgpwwh3DAmYzvU8J0lQRXxGEzBUhSI1QPFclmqJvXWw1f4QT7hjWLw624noyGIZhGIZhGIZhGHYRKahsAgCr3ak1WNA2gDkPvuu68fWzNyVEhnk2hgo8aSqGBYx25gvF89zZdjeCkArF8xhnARLHMKxvDuAC7hiGYRiGnR8CDWAYhmEYFtLK6rWuG8NToj1bAABMVrvrH8tyaFvIwD3cMaxvejp0JwBCd4P3n9FpKzE0lxhaGi36FpuxxWZstRnNtMPB0jbG6aryIaYEEkooJgURYnm0VBUjUSbKw4eHxQ4Li1WL0KsRWHd2hq4ya6tN2kpjW6VJW2\\u002fRmWi7hXa4\\u002ftlZp4gUiCmBhBSKKUGkRBEtUcZKVTFSVZYyerg6LvxC\\u002f5A7HJYifRMaxQYeXjOx0ITXzD7Ae3MsBLXajCWGlipTW6VJW2lsa7ebLYzDTDsstMPJMjKBSC4QywUihVCcKAtPU0RmKCPTlVHpykiK6GNfsT4\\u002fMPQ5WabG3F5hbKs2a1tsxjabqc1marObrLTTztJ2hnawNAAISUpECoQkJSIplVASLpaHi2QasSxCrEiWa1IUmhRFhEIgRp\\u002f9wmVjnMfaqg+1VZ5qr9Xaze12s4V2iEhBmEgaJ1VlKKNGhyfMismMlfZzr8kCXcO+5rK8jvpqs7bVZrLQDgFJyiiRQihOkIWnKSJywmKnR6cnysLRRw41eM3sX3hvPhA44GpM7TXm9hpze7W5vdbc0WYzWRmHmXZYaaeVcXAAUkoopYRSgUhGiWKkygRZeKJcnSgLz1RGpSgi0GfE+pXBbHPdiAqTe7YMGTjhjmEBo4TDnfZdHGcjCAk\\u002fznFWp\\u002f0PSjiKHxxC6i26\\u002fS3lB1rK8zvqG616tLkbV44DABqtetA18JsSZOoZ0RlzYrOnRqVJKRG\\u002f6SLHAVekb9rfUr6\\u002fpfykttZ1rNkTG+O0MU49WAGgxtyOtMZIVbnhCTOjM2fHZsVIVEjr0MVyXK25vdjQvKuphLsgLl8NCXjNxEITXjP7AO\\u002fNsRBkY5xH26oOtFTsbykvM7agzTxGp83o7DzHPt1e544rhZKpUWkzozP7kAaVUEI0NGQ5WaZQ33i6ve5Ue12RvqnG3M5wLLpQNwzD2hin63ZPPwsRYsXo8PixmqQxmsTR6gSZ4MLc5Fttxi\\u002fKD39TedRE25EmK+OwWh1NVv3J9tofqk8AwBhN4sq0ScsSR5\\u002fnNRsb4\\u002fy26ti6iqPd900Mw9oZusNhqTV3HDpXSjFNEXlNyrjlKeOGUJ4Ur5n9Du\\u002fNBwLLccWGpqNt1cfaqo9pq3UOL7VK+Iws494lFeob+U0asXxMeOK4iKQpkWmjwxP4TVi\\u002fMFg6P3ml3CPtNoQQHIfTGSFkyu8vubdnf1yXOvEfY5eh0SHuq4oj\\u002f8rbiEaD4q0pKxfEDUOj3ditP5k6HhCKpkqVjwpEowlCybFG2nnaanzD6Tis1HwkkixBHxPCas0dP9ee+r3uTLWpc8zOkDMzOvPD6Tej0cC9cmbr2rIDaLRnibLwrYseQqM9qLfovq089lPNKa3dhLadt5yw2CuTxlyVPGYIHZe7WWhHiaG5WN9cZGgq1jcX65utjANdKITNjxv29pSVaLRf+blmZqmif55\\u002fLxrtDV4z+UJ\\u002fBxRS8JoZUvDeHAtNZcaWbyuP\\u002fVKbF9A5jm\\u002fjI5JvSJu0OH6EgKTQNm9eyNv4dcURNNqzSZGpn828DY0OHg64s7rGfc1l+1rK8zvqfV967BcCkpoSmbogbtj8uGHREiXaHHT3H\\u002f5mZ2MRGuVJkKm3LXoYjXpiOe6j0n3vFu0O9ANMkmueHL1kbmw22uCfX2pPv1qwrc0W8L5MTAluz5x+V\\u002faskL1ihNfMgYD35gOkWN\\u002f0S23eb3X5rTYj2nbe4mXqxfEjLk0YOQpn3vvP1HvedNIMALx412WLJ+UgrRNWv+G6seFftydFqz3aQkbvPdwrTHk7mtflqCbmKCfGSFLQZgy7+IilVzPOs1bTe07tdQAAhAA41+EFKVM9OVSy7RxwOxqLvig\\u002ffKytGvcj7hu50K8Rjvtbyr+qOLynuZQdsAucxfqm\\u002f+ib\\u002fnt2x6KEEauzZmapotElQkmjVV+kbyrSNxXrm4v0TbXmDrwG9osKY5uDpUVk73t2F7xmYsGB18wBhffmWMg60FL+bvHuE9oatOG8ndDWnNDWvCzZcl3qhFszpimFvfR963WB0ORkmX0tZVvrz+5tKWu3m9HmgUSzjGto0fOnN06NSrs5Y8qcmGyS6Kmi5uCrt+iMTpuPL7rRqn\\u002fs2I99Wxtrze33Hvr66uRxz4y5LKDct8FpffL4hj+aitEG\\u002f9gZ+v3iPT\\u002fXnH590ooxmkS0efDgNXMg4L35ALExzu+rjv9YfbLE0Iy29Z8Gi25t2YG1ZQcyldF\\u002fypq+LHG0n9eDMR\\u002fkEpHOZAUArT6ovzP9qPeTnzP6AzWWohpL0bamL8OEUTmqCdnKieny0ULyoh6Kgl3kZKq\\u002fiSSLbZZvGecZjjMShEogGiuW3SgYCvVkOOC2NxS9W7y7GBfIPj+9lhSsNLW9mLdpf0s52jAwHCz9W23exrr8K5PGPDB8XqADrgeInaHLjC3uDuxF+qZ+7OOG8TEcW2ZoHaGOQxu6wWsmFkx4zRwgeG+OhawyY8srZ7btbS5FG\\u002fpVm830btHubyqPPTR8\\u002fjUp430k3cKEUjQUwhiOPdhasamuYHtj4aAfMnHAHWytONhakSBT35E1Y0XqhPMsrjJwig3NEyO89w6sMbffuu\\u002fTZqsBbQjETzUny4wtH067OUzk1+pUa25ffeDL7jVkAtVo1a\\u002fat\\u002fYfY5ddnTwObQsuvGYOELw3HyA2xrmu8ugnpQcGYqxkT8qMLU+d2PC\\u002fwp2rMqbekDYpoEt0GCIyTO5KuB8vqbtx4Xi0eSjoJeHOAVdoOOy+q3e2HtFuPqLdPEmz+IqEu3kLYthFRyCaqBBNRKMhr9LU9uzJX49rq9EGLHCKnnu4W2jHu8W7Py8\\u002fRLMM2jbAWI77qebUxvozj45YeHPGFKLHCX4HitFpO91RV6xvLtY3Feqbqkxaf2o4Yv2i2NDkO615Ma+Z2CDCa2a\\u002fw3tzLDQ5WPq1gu1fVxwJ2q6\\u002f3W7++6lfv6k89vexS3PDvfcCVvuXIR10TVb9j9Unf6g60Ww7r9TwQKi36J47\\u002ffuXFUf+OmrRrJgstDkEFOubvCbcK01tt+\\u002f7rKU\\u002fikjkd9Tfvv+zz2fd3mufmyqTdtW+tX0oI+OVk2WePvELAAxWzh2vmQMH780HAstx6yqPvl+8J5ipdr5mq+GVM1vXVRx9MnfJvFi0Fgrmp2HJ0WX1bQCw53TFntMVs8eko0uEvF4S7m32ehOtQ6MAY8PnoiEMw0Kbq3Dhe8W7XfOYY+evp6PtCmPb\\u002fYfXVQ1q3T07Q\\u002f87f\\u002fOe5tIXx18VFdwShz\\u002fVnHopfzMaxYKiSO9rsORFvmZigwivmf0I782xkFVhbPvLsR8GpZNmob7x5r1r\\u002f2\\u002fkJasypqJtACE+WwMH3L7msnWVRwe0lFa\\u002fqDC2\\u002fvngV5cn5f59zLJQm7vS647G6LTdffDrfsm2uxTpmx458t0H0272MaKi2Wa4ff9n\\u002fZVtd+GAe\\u002fbkrxqRfE5fS8n3AV4zBxTemw+QCmPbMyd\\u002fPtleizYEXZ2l475D6+bGZv8t97IEmRptxnozY3TqbwfPAgDLco++8\\u002fPkYcnjshLCVTIB6TGcZfuxknBl3\\u002ffyV80awBoVvSTcq82FaAggQhyXLBuGRjHsIkIDUDBEOsG5GJ22vxz7YV9zGdqAnQe5t4T7H03Ffz223kTb0YbBsL+l\\u002fLpda9bMuDlTOYQrFGP+K+o5zYHXTGwQ4TWzv+C9ORayfqk9\\u002fc9Tvw\\u002fitOc0y7yUv\\u002fmEtuaF8VciXSLCQzXhTrPMb3X5a8sOlBpa0LYQ9mtt3pmOhtcnr8hRxaBtg8frjuZvJ36uPe+iLoj9LeUflOy5J2cO2gAAAHaGvv\\u002fQN+dZvsYrhmOfOrFhw\\u002fx7gnBVGK+ZAw3vzQcCw7Efl+7vw8TIA2pXU8lxbc2L469aEIeTqIGZNy4zVqNsajcCAMfB4cKaw4Ve5uF4+6f9aCgQA5pw76XQVYPVSxnNHOUkNIRhFxNtQ6rd8g0aBQAAs\\u002f4JQ9s1aHSwVZm01+9eg\\u002ffo\\u002fa57SZmPSvfdf+ibEMkcuTTbDDfvWdu3SaKwIcfr2SbgNRMbbHjN7Bd4b46FrE9K9z9x\\u002fKdBzLa7bW04u3L3R62ePZo1Yjn\\u002fboj4tTbvkm3\\u002fe+rEhqGV03SpNLXdsueTUyHQh9StzNiCFDL6quLw9kYv3QfP33vFe8qM3r+1l85sLtA1oNF+0uGwPHvqVzTa3\\u002fCaOdDw3nwgGJzW1Qe+\\u002fO\\u002fZHSGVbXcxOm0PHP7mlTNbg1Zs7cIgFFDP33GpWNhLN\\u002fFQ1kvCvd3h5QQpWzkky9VjWBAQZARNF6HRQeWqITi4Q\\u002fUvVEj\\u002fqc\\u002fLD71esD0Ep5V3HX8M3NE\\u002fFjqMTlujVY8E8ZqJDTq8Zp4\\u002fvDfHQtZ\\u002fz+54tWAbGh08FcbWW\\u002fd9yi82HRGSCXcHSw9EP+igMdH21Qe+DJ3Lk3aG5v9Cau2m\\u002f53dyWvvTzTLvHB6IxoF2NNc+m3lMTTar3Y3lRxurUSj\\u002fQqvmQMK780HQoWx7frdHx1qrUAbQsnasgN37v\\u002fCHEq9TELf+OyEtU9cPyYzHm0YInq5VtA94U4AkSTDVf8xzDsCKI6zoNHBU2vuuH1fP9cQxNyUvB7uP9Wcejl\\u002fC68xtFgZx72H1n07585YaRjahl1YivRNcbxvGa+ZWIjAa+b5wHtzLGS9nL\\u002fls\\u002fKDaHSwVZm0q\\u002fZ++unMW10\\u002fOzKBSEIJbYwTXW5QXZE05s3CP5DO+EOLmbbfc+jrH+b+OUkejrYNhkJ9U4YyynX79YIdAzp86khb1f6W8hnRGe6IhXb889RvvEUGymtnt383ZzUa7T94zRw4eG8+EPa3lD969Huj04Y2hJ7DbZV\\u002f2v\\u002f5muk3q4RDYyrvUJCTHP3JX6+vbGw\\u002fVVZf16o3WR00zVjtzi1Hi10LLJyYrZCE6MwNvSTcbYwZiahF0SJSggQxDAMAjnM47LsoMg5tGCRGp+3OA1\\u002f042zyYkqgEkollEBICpwsbWNovcMagoO2gsZdw\\u002f2PpuJnT\\u002f4Sgv00+VptxnsOrft2zp0ispdffmxIK9I3zYvtvC6O10wsdOA1s8\\u002fw3hwLWV+WHw7BbLtLrbn9jv2ffztntVIoAQCNWN5g0aELDSohSd2cPuWNs9vRhiHF6LQ9dOTbdbPvFFOD\\u002fFMJAMX6pmWJowHgrK5xQ80ptLm\\u002ffVSyj59w\\u002f7Bkb\\u002fexXAPhTEf9yfbacZoktKGf4DVzgOC9+UDY11x23+F1TpZBG0JVfkf9rXs\\u002f\\u002fXjGqtCsdRay0uI0aXEa911+wv3+q2ckRavdTSGll18fB4teJooUD9XO\\u002fBh2nkwd97NM55gPq+k9u+VHz3aOoStYtlWquM8zPmieOrHhPKcJipIoZ0Zn5oYnjFDHJcjU3fcKHHAddkutueOsvjG\\u002fo35vc5nWHowr9kqhJEkeniTXpMg1SfLwEerBucjhquHebDM8eXzDkKjIVqxverdo98MjFqAN2AXEXSwbr5lYSMFrZp\\u002fhvTkWmvY0l758JnTHqQBAlUn7f8d+fG\\u002fqjSRBRIRewh0AVqZN\\u002fLBk71CvMFCkb3qlYOvTuZehDUHn3tF8WLI3CNd0D7dVFhuaXfNzNlsNn5cfQpcYMN9WHhu4hDvgNXNg4L15vzvcVvngkW+HULbdpdjQ\\u002fKf9n381+0\\u002fu3ntYoKRioUIqNllD\\u002fTeql4Q7AQQSkVDoVo1h\\u002fev61AkL44fp7JYOh6XDYemwW3QOa4fDojt313U7+MNCRZJFDttWp30\\u002fADB0BQNojTCSjJLI75QqH0Hig+LTsoM7GvtYTV5ECpYkjFyZNnGMJrH7jwAfAYRGLNeI5WM0iTekTeKAO6mt\\u002fa7q+Ob6gv66uh4pUSTLNclyjWsvniwPT5Zr1CIZutxgUAjEHHBPHt9gcFrRNr+RBBElUcZJw6SUUCoQSSmhk2VsjNNE2xst+la7ie7XY4iPS\\u002fdfEj98pBpfOr1gFembAQCvmX0TsjugCwBeM\\u002fsG782DpvR0zfGdBaWnq2uK\\u002f5+9uw6P4toCAH5m1i3u7o6E4C7FpdAChbrLq3tfvX11d6cO1JACxSnukBDi7p5sNus+748NYXNJsjvrSe7v69dvc+5sNllu9uyeuXNuk1yiVEhVBEEIvHhCX350cljiqKixV2XEpUegdxuuymWtj5\\u002f9y4FnzggggnleoTwvAZPDZ7JJglAZdGq9TqpT1ys7be4VcLSl7MPC\\u002fY+nzw3gCNExDyBicVfHZP1QfgIdsI8XixfEFflx+BwGi0MyOQymzmjQGPUKnaZNI29Ty5R6B29v+1vV2VXRY5K9Q9AB1zIlmnpl58Avm0FcUawowIvFJYBo18iLJM027\\u002fe7pSbnmRELAODr0qMW3xsQQEQJ\\u002fcL5PiIW12A0tqplpdIWi\\u002ffq057GgpdGL+YxnNVFAc9Mh8PZ3OGyO2r\\u002fc3KjbX9BfWKSjCCuKJgrEjA5HAaTy2DpjAaVQac26FrVsmZVl8bgmOcQAEqlLY+d\\u002feuLiWsZhIVtNbH+BPkKB33Bnc3g6fW9ZjCLwCdhMOdikoxgrlcw1wsd6E1t0PWUPzq1yi6zakinVinRKDu0Csf2R2PzlrF5ywAM4qZkvtdLHP5q81ECGEBY+INymSZV1ydFNm4TtCRy5KNpc8w77VqPAGKMf9QY\\u002f6iH02Z\\u002fVHhge91F9AgrLI0cmeIdEinwNeVyLoOFHuExhEzOj+UnbdieJU4UONY\\u002feqx\\u002fVLpvWATfl0Uy0CMuMVDGcllboaTxZGvlkZZye6pUJgbK+EruP07t\\u002fMhlMB3yrktnNNBdWePF4pHEQG9DnQHZO9ft6hWdCr3mj+rzeGbawGMT0BCAZ6YNcDZ3jWPbsze+v7OyoB4dANC26TrbpHWlzce2Z\\u002f\\u002fw2tbEUVFrH188aeEo9LhhRm80PHVuM90cfSUmycj0i5wenDghICZOFMhn9ls9lOnUVfL27I66cx0159praP1prys73qVV1dq3sNR5bo6f+EvlaZvPFBJAxIkCMnzC4kSBCV6BccKAUL63xUZYLSppibQlV1x\\u002foq0iv7PR\\u002fhMnRop6\\u002feKun6fdhg64VodG3qGR\\u002f1R+ss\\u002ffKILvuzZu3KLwjGBeryyvMxoONpd8UXy4VNpiHrfGP\\u002fX5T2XMb9fIN9Vko2NmxgXEXBudOSsk2dTgqIfGoP+3ufiL4sMVsjbzuEUag\\u002f5YS8XcsFR0wHHwzHQgnM0drlYhvu\\u002fUBptPlZkQQKT7hk0KjE33CUvzCQ3j+Qz8QbJNLcuXNBZIGk+1VV0Q1xkpuy6jOdpS9lbe7uc85gqMQSfQR1jZ6OmbD1t4yeMxBEp9ryZTKgP+AIl5BC6DFcrzHiD3NCglc\\u002fd+hEYdgMFgpRAEk\\u002fDgk09v5+2x4WSvL5v\\u002f2pire3rs2iOU5\\u002f121jULwzOey97aqaW3iyybZN6WMBmNeqQnzm2i1asxhOe9LHLkssiRcZc2dLKIQZDJXsHJXsErojINlPFwc+kvladPt1Whx9GR39lwsLnEIf\\u002fQfVodM3Z1zFg0St\\u002fexsJHzvyBRgf09+z7kA9RwxAF1MpD39C6cH6YzEwHcl8CGsTwzLQBzubOppKr3773+9N7rK1BlOXWvnrzl1OXjnn8s1u4fM99H+hsX5Qc7undYRt\\u002fjnBN7Ni1seOubInQJxGLO9I3YqRvxK0Jk3RGw6Hm0m11uYeaS62sx\\u002f01YDHUvYJ5XovDM\\u002f6uy0UH+scgyBG+4VOC4kf7RY70DUdquNYI5nkF87ymByc+mDqrQyPfVnfx96pzdp6TONdRg2wi6hbXH\\u002fm+RYX2yGYQ5AMpM29LnNxnwZdFMuaFpc0JTfmw8MD3ZcfR4QF1aOS5nfWHm0v7a2oRwvN+ZfSSacGJ6AAAAHAYzIXhGXNDU9\\u002fI2\\u002f1b1Vl0eEAHm0ucWnDHM9OBcDZ3LLVB99Dp322+8gkAkr2Cr4sde1VoagCXxsVPgVzRrJDkWSHJD6TMEmsUexsL11eeoXu2zNz6yjNpPqErojLRAcwKQT40\\u002fu3cpY+UYy6QE9GhaTKPKA2DeLtqDHMIDu86khGDRj3G2fbqvY2FaNSScL7P91NujhRc3onCfjNDkn6bcecdx3+pV3aiY\\u002f3bVJM9KyRpdmgKOuB5rH\\u002f\\u002fF8b3uTd5+oqo0fZcNcYgyNmhKbNDU862V7+Vt6eoq9eLMy2fFR2aGZI08DWJ2OBVI7f2bD+emZgr4ZlJC87mzqaSq59f\\u002fWnh2Qp0wJJj27Ml7bL\\u002f\\u002ffbA8Ky553c2fFt6DI1ajU0y70yaclfiNJt3MmSRjLlhqXPDUitl7Z8VH9zTUOiCbt1OdXviFGvKml4s3pzQ5BkhSZMC42woZfbHnyO8LWHyzfETt9dd\\u002fLjwX3t2dPyl4pR7y5oAcGWPbC6D9cXEtRMD45A4gkGQT6TP5TFYnxcfQscG9EHB\\u002fjJpKxoFAIAs\\u002f+hPJlzna+m6TybJeHHUYgNl\\u002fLP6PDrWv2Ot5WjI0fDMdAiczR3uxQvbbbgexWSsf\\u002fTDabOz\\u002fKPRAZr8OII1sePWxI472lL2bv6+clnfLwIWvXFx94SA2DC+DzqAWRLkO\\u002fgL7sHc6GJpr3Otcj2NP04MG5K4gpvQkCf5jubSDAAI5Ip+mXZbSP+rNW0WKfD7Zdpt1x3+tlVN41zdG3m7JwfFe8gFa3Zikoz7kqfflTiV2X8PBLrGBcT8MfOuT4sOflt6zLYPmUVdTSdbKye7760n5nZ4ZmKeCc\\u002fMHjibO9u6VzabV9tJBjliUuLo6SmRiSG+wV58IddopNRKTUeTpLakKedwceHZCsrYPX\\u002fyT5Z9\\u002f+qW\\u002f7y1pufuw4SRol7I2WbluvIrpfuEvT9uZZSDSkhxooAPxq3Kjqv9b\\u002faWOsUg\\u002foia6BU0LTjxaEsZOgAAAF4s3lVhKQvC0icGxjrwhRHBIMjlUaPnhKa8lbd7S+0FdNg6R1vKa+Qd0UJ\\u002fdMB9GAT5yYTrLFbbe9yfMrNQ0nSwuQQd6N\\u002f5jho0BAAAI3zDv550wwCNkhDPjlh4vqO20urVsu1qeYNSEu7MOh2emQ6Bs7ljbag8s8Om3jjBXK8XRi1y+JmDacGJk4Pi11ee+aBgvw2N8hV6zXPZf38\\u002f9WYXr+oYAiakRsmUagAQefDqBwvrhuKFI5FIh6ZJrpcgQQzDPESlrP1YC731DkyS8dmENc7I6CbBPK\\u002fPJq6l9T6sUSn5sfwkGh2E4kSBv8+4877kGbR+fWswCPKRtDkfjl9l83e2+T0rNgTgmYl5Jjwze+Bs7mzVRQ07fz7a8+WUJZnfn\\u002f3fW1seXfPowilLMtPGxcekhselR6SNi5+2LOuGJ5e8t+OJ7069OnHB5U9GO344XFPc2PPlMPF3XW6JresKl0SM+GXabY6qtvcY4x+1eda9SyJGoAODyh2JU5AISRCTg+LfHXvt4QWPv5Z59dTgBFp\\u002ffbYRsbivj1n+7IiFtl1dRAG11YoF0a50V9LUqUEJaHRAL49eYv9mpD5s\\u002ficTrrO+2g4AHAbz8fSr0OiAcsR1aMjR8My0E87mjtWglLxXsA+NWmFeWNrW2fc5vNpuwiDIm+Mnrp9+ewTfFx2zwun2qg2VZ9AoZklWcsTT189++vrZPkIeOuYxLLxgRfPT+QwREqyU23JCCcOGGIOhVi3\\u002fRi55Utb5H7nkSbX8G4OhFj3I5X6tPE13+d7dSVNH+IajUYfK8Am7J2kaGh3QTxUn7dwFxe3GBcRsnH5HqncoOuA488LSPh6\\u002f2ra3nvubiuzpfIcNXnhmYp4Jz0xzOJs726HNZ3uWqy++bcbzP9wTHGlh8WNYbOCLP9+36JbuZ4AyUgc3Da9PyFqj\\u002frOig2jUOtfFjn1n7LVOWiApYHLeHnvNzfET0YHBY3xATMalv18\\u002fjuC+5Bn75z3y3eSbFkeMsLn3js1ujJ\\u002fw8uglaNQ6B5torA13tkiB33+SZ6BRSwK5opsT7J1Lz49cZHED9ivNDEmi1ULkYmcfWz07Fp6ZdsLZ3LH+l\\u002fuPDd3w70me\\u002ftH41d5s55Zl033C1k+\\u002fPU4UgA5Y4dOiQ7Q2A8cGCwsfOUiCHOs3FwkWSYfXm0sMuwKllL4uaZmqkL6qUW7UqrZplBsV0lclLVOV0jeAZk51IANlpNshLozvc0\\u002fSdDTqBHcnTaPVm6xLq\\u002fqr2nN3uLJodmjKt5NvdGAfw\\u002f7MCkl+NG0OGrWCxqCnO1uwIQDPTMwz4ZlpDmdzF8g+2P0MB0X43f2\\u002flb0H+0UQxD2vrw6K6K6Inf+X3j\\u002fTYLe+8gytveJ7LI4Y8cLIxWjUoQggnhmx4PYrVuMOInckTE73CXtjzPJ\\u002f5z\\u002f6YOos561vtca10WPuo1+qBoBSaQutzbGd6oGUmbSW8fa4PnacbXc0GekbsSgiA41agQBiWSTaYGAAFVJr+8\\u002fYA89Mm+Fs7lh7GgqO9NPgaADPjlj4cOpsNOocgVzRT1NvteFCLqlOZc\\u002fmKJjHslBwB4ApAVezyV6ffwq6TrRqnH75EoZ5LJX8G5X8SwYziu\\u002f1vJf\\u002fr96BO738\\u002fxR4v8JgxqnkX6jl36J3cJXzHbVijQKNDujupKksO95QWo9FMu6meSL9T3cndZuN9ot8f+xKNumidR+3J06xbSOgozSvcMQGOzwzMc+EZyYCZ3MXaG+UmG5MuzqLzaGx7JrNYc1YMdZ0u61hEPcNp0tr1K+j34kYANJ8Qt8Ys5wkXNGd9rG0q2aFJKPRQWJ+ePqfM+9eHjXaZS+GA\\u002ftPyozRfpFo1Aoue6kcmB9HsDA8HY1aJ5ArmkazEY25\\u002f6TYUhE2mRGciIb6VylrR0NOgGemzXA2dyCFXvNm3m40ask9ydNvjJ+ARp3JnyP8ZMJ1NrSlWl95xp6dgTHPZLngzmd6zQ+52TxCAfVvy0bzCIYNKxrlrwxmtHfATp7wXhZnJpM1ksWZxBXc4R24i8FMVCt\\u002fRu\\u002fgKvsai9DQgEQs7tWRo9Go0yyLHElr9WK5rLWoqwmNerwIvu\\u002fnE9e6+CrLF0ctseERT7VVGSm3XZCBuRiemZhnwjPzSjibu4CsS2m6EZVMu4tRVFL3XRTS7m8yHOxuKKBbOQIAHoP93tiVrqkfAQBJEO+MvSZSYEsXXQzBIMiXRi224UxJnqQBDbnD0oiR9qxSnxeehoasE8H3pds13lyaT5j1nd9b1FKl3v1dPlxsEM1MnM0daH3lGVobvQLAvLA0l61tN5fkFfzcyIVo1BK1QfdNyVE0ig1ylgvuADDef2GK1zjzSEHXyTMdu8wjGDZ8GAz1LO58gkQb8xEEj81bZDC4Opf3ONtejYYGtCA83YaKg824DNYCmitN6L5NcTuSIN7MWu7L5qMDThYp8F0ZnYVGLZHqVAWSYbfh2\\u002fCEZybmmfDM7BPO5i7gE9C9SRWXZ21tqweb270i3stP2HtkKFtv05Zuj6XPiRH6o1FnEjA5zm5fM3wke4cspr8bbYHEbSU5c7NC7brWYUqgLRdCAcDVUaNsqAX3IAki3ScMjfavTiFGQ8PAYJmZOJs7isqg\\u002fYnmrq3BPK9XM5eiUVdZET06yz8ajVryd12uQq9Bo8NYQ3vX6cIard6ADgweVhXcAWB15ONR\\u002fF5b+v7T9F2p7Lx5BMOGCQYZClTfqwkoYydJhqBRl1DqtRUyeo38rnLOPt0DmBmShIYGdKK1Eg15tlviJ9mQXB3ijsTJNuwEmCuuR0PYUIRnJuaZ8My8Es7mrtHTh72rQ957xLLOtu6LvoOjXFpKdqMCSWNeJ+0FJXGiwDWxvdZsucbU4IT5NKtIWH9s2Iq2XNqqMejRqGtxGaxMm7qO9AjgCm3b\\u002fHC2fYV+AEj0CkJD\\u002faO75nfI8PyZibO5A\\u002f1Wda5TS++SsldHL\\u002fViOXeX1AEQQDxPf5G7Uq\\u002fdVncRjQ5jhy9U\\u002fOfDzbMf+fKVH\\u002feiY4OEtZ80WCTnppjnzWvuRsq4vubNI22b6W67jGGDHUdwo1a9hzJKkLjR2KZV7eLy1yBx18iXNBooIxrtH5NkjA1wdZljfEAMrQJHgaRxEF0pGcgVPZg6C426SgjP24auxCXSFjSEDTl4ZmKeCc\\u002fMPuFs7hqTFo4y3SjJqe41YIWCUxWmGxPm0djecFD7o9qWVVZPZcyjNU8c6MGUmQTYvsoY65HuE5bmQ6\\u002ftkoEy1indvOw60y\\u002fS\\u002fkZGtFaamwRzvVK96T1dV4oT0ij0twzXgrvnz0yczR1FbdB9X3YCjQ5oanDCNDrbIThDsnfIdPo\\u002fw29VZ9HQMFZQ1QwAKo2uo68OfjMe+sL0X0O7Ldu5uwaNvy4uQ3Bb3Ksjfab3RIyUYV\\u002fzLz9WvVSrLDY7EMOGOC5\\u002fLYMRJWlbqJS9q1Ft1qp2aJS\\u002fK6WvdbXNJxlBDFaqVr1Tq\\u002fqn5z\\u002f0\\u002fs5R3NWMhgaU4hVsw24edhIwOQmiQDTaPwNldEF1w1HuS57OZdDYeM3hlkV2lw+sN4ieXsxmeGZingnPzD7hbO4as1ZO4PDYAHBqV66sk0Zr8tZ68ek9FwGAzWHNWjkeHR6KDJTxQBPtz3rJXsE21BocJU4USHflJtYfG\\u002fahbVZ1XwXiLnRLsX2yoXTukIJpBJ1NCFpVw7TgDh4\\u002fM3E2d5RdDQUdGhrXohFAPJUxD426wx2JU9CQJWXS1nz615MNVeUNHaYbqdF9XPcjV2lM\\u002fxmNnrsEnEbBHQCYBGtV5KNLw+5mkZyeYKU879uK\\u002f35V\\u002fuRZ8Z5WdS1e8I4NeeLmETrtaaOhTiX7WN75kKzzXrnkcZX8K6OhVa8rkIlvl4nvlnXe0\\u002fMfen\\u002fnaFBK0NCAaF2u6ECJ3sFoaEAlNN+suEs432dl9Bg06lrTghPodo0sl7biF+2hDc9MzDPhmdkfnM1dwz\\u002fE+65XVwKAQqr6+LFfjQar1iGqFJq371mn1egA4ManlvT0pRnasjtqbdgu9eaESWjItW5PnIyGMJvYcOKkxYVlzT7ZUCu\\u002fUiz97QdG29fHxiSY273DhDXEWtp\\u002fm0OGh89MnM0dZWvtBTQ0oOkhiQki9zyZiHEBMRF8GufPTA42l6Kh4UqqUJtuBHoLeo8MGrbsyTDef2G8cPRP1a90ai+f3WpQlTc0lAMAlyEI5cbxmSIeQ8BlCBiELQ\\u002fRp6uCb0BDGOYOfK\\u002fn0ZAHaKSZ1GNt6ktovxgBvTevNXLXXfpnj7Wx45h2X7tqJxGLm+odSmtPP7VB16lR+nEGaw7DLMIzE\\u002fNMeGb2B2dzl1l86\\u002fSW2vY\\u002fP917fEfOY4veufm\\u002fV4+elkwy+l6NpFZqTu7K\\u002ffnNbc017QAwZ\\u002fWEa++fix40RO1ror1Lni+bvzgiA426VpZ\\u002fdDjfh27NC7tSincIi2TojDS2rXPlOuI+OaR2GSmgfUZtpG84GqIvkE7BXabrLkgNQx4+M3E2d4h6Zee59ho0OqBb6Pf3d55FERnflB5FowM62Fzixo6LHkWq7H59Ewm4vUcGDcvV8DZNvVTX0aVrl+o6unQdUl2H6UuVoe\\u002fLOtQGRZUiD406Ai64Yx6CJ7wXDXmARiW93lXBXC805BKBXCEaGtCg2AuIRTKWR41Go+4w2i+SVvEIAFrVMqcWjzA3wjMT80x4Zg4AZ3NXuvruOe2NkoObzpRkVz+36mMvP0HCqOjwuCCRj4DDY+n1BrVS29UmqytvrrhYZ1rYDgCp4+JmrBh3cNNZmUShU+soaqCLHlY\\u002fvAANDTYHGmn3k5kXnsYmLX\\u002fMdLZFERnflh5DoxhNLJKRIAoq6mpCB\\u002fondWsVmAAikk5Xlv6E8b3R0IAIIOLpdOfojzebRwBh5dVUcp0GDQ0bHj4zcTZ3iL9rc638WzCJ4PtODIxDo+4zOzSZbsG9uKu5WdUVwqP3+jMkGTy4V4yVLL8T+qT0QTSEYZjnobtzdwDN5OootFZtgMuTum2uCk11av3FeoletN\\u002fot6llKd4haBQbEvDMxDwTnpkDwNncNR5Z8HZdaZNS1qv4IhUrsg8WZh8sNA9eqehs5YtrP0Oj\\u002fRjsBfcqeXuTil7ZCAAWhKejIXdYGI4L7o4RKfClVdbUGPRoyIUCuEKHbBDCZbB4DLbKYO0WkaF8bz6TjUbpYxCkgMmW662qpFt52FDlyTMTZ3OH2FZ3EQ0NaGGER2SfHuk+YXwmm+5Os0dbylfFZKHR4UfAZUvkKgDo6BqsvbP6vmoSwzDrGAH0ff3nBmpD98IrKwmZl3dicCUezXfAdPOTW8wLS0VDbmJDx7o2dd+XK2FDAJ6ZmGfCM3MAOJu7Rsn5KqTajvUpp6MODVnixxGM849Bo+6Q7B3sIef2BrtgHr21txqar2OO5ZDl7SYBXBrzx4ae7\\u002f0Rsqztn6AY3gV3T56ZOJvbr1LWXqeg18HGQ0739mAQ5Bi\\u002fKDRqSR7eNxUAAAIutW4\\u002fX1rfe2TQsLzCHcOwKxiVso80yo1GQzP0dYmTf5gbXhHoJnW2m5rnshn0XnY0Rnq\\u002fl+sxScaU4AQ06iZBPHqLFABApscVh6EJz0zMM+GZOTCczTGPki2uRUOWTAqMo7sdsZMQQIwPiNndUIAOYDT50zxvoTa6Z\\u002fmRCd0VuwMQ0KmBhjquBQTX6hdYWh3Mhx5Pnpk4m9vvRGsFGhqQH0fg1GsQbZPiHXKstRyNDiifZrfDoSolKqi8oR0AjuRWHsmtnD7Kg5oFWYneXxeGYQCgkn+rkn1Akv4szhSd5hiTNRIIlkFfQRklLPYEDv869A4uQfcSOZabkjqD5mcwz38fOc4\\u002f2l1LEq4UwKF9NSLdmYMNFnhmYp4Jz8yB0f3+OJvbZtVD89EQ1hcbVriPC4hBQ+4zISAWF9ztR7dDi96tf+82vLD3h17BnWbP9wHwGNa2pjFQRjQ0nHjyzMTZ3H7H2+gV3McFxBBA79dxgXj63QvLpK0ag55D81zI0DNlRMyOk4UAYDRSj33+9\\u002fiUqMzEcF8vPpPs1all\\u002f7lSXxHfPELL8mkZaMhxhvs\\u002fIYbZQKP6i8GM8Q7cRRAicVMc3+s5FmcKgF4l+0Il\\u002f1LAGonewSNpXZgszdF980H3jZTrjfekD5ZcBovLYNFaUqF14VoPzJXwzMQ8E56ZjoWzuW1uf2EFGsKu0KlVVsnb0aglHvU3PtovAg1h9Fm\\u002f4Nqkj+t\\u002fXSiA5qrnAfCYNF64HLjJofW1V51xmBfcB9PMHBjO5gi90XC2vRqNDmicfzQa8gA27KVsoIyl0pYRvuHowDAzKzMhxE\\u002fULJYBAEXB6aLa00V9XHX32ZbjaIgOpxbccQ93DKPNqK9icxcQhAgAgOBTlGnnECZP9BCLM04pe7PX0a5i\\u002fTszE7rJ1VHoPq71SzzcZaSHfZajezKc7r8INljgmYl5JjwzB4azOeY5Srqa0ZAlPmx+jOM6WdsvThTIIPAHXnuRg+o59Hfc5pNMOr+4H9v2JZYI6xcdD\\u002fMV7p48M3E2t9PFzga6\\u002feLTfELRkAcIsqnJVbW8Aw0NPywm4393LOSw6L1R9yiWf\\u002fQX0jeiIQwb7sievx2S9DYaLu+NzmRPVsk\\u002f6fnSlYQsjlhDI19KaO6c7ijtGjkaGhCtpSWuRwCR4ROGRt2KQ1p+YTfnygsDMZfBMxPzTHhmWoSzOeY5quh\\u002f4E\\u002f2CkZDbsUiGbHCgHJZKzqADV1eVu84ahGtszW+HIcV3K2vIw\\u002fzgrsnw9ncTgU0+5gTQCR5WAIy8ecISIIwUvSur2hSdaGhYWlMUvgPz1z39saDueX05oOHsPwZg006LGNh2NBAMiIM+spLt+N0miNcwW3dY5QaKBpXpjtQAEco1ijQaP9a1KaF+a7WpqaX1B3Yh9EZooV+Ise9rXcIJs31FPSSPx1GSt+kKgaAcH4GOoY5GZ6ZmGfCM9MinM0xz1Epa0dDliR5e1y9I9k7GBfchxUHbhNCq+Du47gV7jS2HXZ2TsJshbO5nYq7WtDQgML5Pnymi1bf08IgSB82n9ZkAIBmlRQNDVfJUUHfP31dVZP4QnlDfVuXXKXV6w0qjW7P2RLTAVeNTRJyPfGfHqwpuGMYhmBxJmiUWyhKRRA8Nne2ousFhfRVDnex0dCoVvzAYCaid3CJYJ5XqZRGWqpXdKIhl6hR0FstFSnwRUOexKOum\\u002fY0Mn3bxprHAeCJ1D3oGOZkeGZingnPTItwNsc8RzX9Bu6etsIdAKIEfmgIG9IcuMKdFnc9LuaZcDa3U4mUXk+zCFf9YDbg0W98j1e4I2JD\\u002fWJDL2dz84L7AyumRAb59Ax5FFxwxzDaOLzrjEYxRSkIgsfhr1UrflHLv1HLvwEAAIbQ96Peh7tIvCjgaEsZGu1fMf2+nA5RQvNkdaRnf0yKFuDiUb80Bnpn8jEHwjMT80x4ZlqEs7l7qZWa8ot1knaZTCwnCELoK\\u002fAJEMWPiOQJHLZmdhCxYYV7tNDj\\u002fqFDeF5oCBvSBCz3\\u002fLW6rLc1NijgbG4PA2WskLWh0QGFevBLPZtm90IAaMEr3AfE47CEPI5cpUEHPAztf3hsqKGU+paRAMAI2Ekwk9BRrC9M9mgR+2vTbYLgegdsVSvW6fVlJCOEw7uGyXJP94xEmkuKCiSNFFAEWH3FoiOoDboyKb2reuNFAWjIk0R53gdLz6ExuqcXIQZ4ZmKeCs9Mi3A2dwudVr9v48l9v50ou1Bj0KM9kRlMMn5E1OxV4+ffMIXLd08tz\\u002fUMlLFFTfsDfyjPGw25Wyjf434kzKlsWExqP5Ig6G7BjQ1tOJvbo1reQXc31xDPyz492PRfHFQG97QpHkSCfIW44I5hQx9BevFEj6JRlxvtF4GGBtSpVRZImly8ed3Z9mqtkUbu5DJYad6euNt4jzAPTu1up8Ur3N0Hz0zMM+GZaRHO5q53clfup0+s72ztt7hs0BtLc6pLc6p\\u002ffWfH3f9bNXfNJPSIoUisUdDd5I1BkMGet8ZwOL\\u002fsqA06qU4t12kUeo1Cr5HrNUq9Vm3Qaww6tUGvMeou3zbo1Aa92qjTGvTdXxr1aoNOY+j+P\\u002fqtPRjHHQV3rjsedPAaDjMTZ3N72NBg58uSw1+WHEajg5ZajwvuFgT6CCsb6TVEcj1ccMewISJWGBDAFbbT2fbkQFOxi5P6gaZiNDSgUb4RdLezczE\\u002fjgANYZfgFe5uhGcm5pnwzLQIZ3MX+\\u002f7VzX9+uheN9kMuUX7w4E95x0sf+egmkkFjN8XBiO4ObwAQyBXR2mTSNQK5IjQ05HRo5FXyjipZe7W8o00ta1XL2tTyNrVMrvf01X\\u002fOwKHfvcF+NrSMGA6G88zE2dwe7tpC1nOojbjgbkGQj4v277UHTgwY5khGY7tWtZ0ruA0dcInpwYmba3LQaP+21lx4IGWmyz4aqQ26nfX5aHRAEwJj0ZCH8WHz0dDg16QqQkM2adNUoSHMVYbkzMSGADwzrYGzucusf3fHldV2\\u002f1CfgFAfgRfPaKRUcnVbQ6e4pdfeZft+O8kVcP7z1hrz4NDTQb\\u002fgHuCRZ9SELA5JEHRX63u4OkXnxc76i50NeZ0NFbI2mU6NHjGMuaW1i8tegT0cnpnmcDa3WRsuuOOWMpYE+eKCO4YNMwZdmaLrBXcV3K8KTaWV1FvU0l0NBUsiRqADzvFXdTbd5QwLw9PRkIfx4wzB4tH66kfQEDbYDMmZiQ0BeGZaA2dz1zh3IP\\u002fXd3b0fJmUGbP4tukT54\\u002fy8kOrxl0dslO7L\\u002f7zw+Gy3FpTZPu6QxkTE6YvH9v7wCHFhhXuXmweGvIABBACJmcIFP46NPKjLeVHWspOt1V1avFFhH0jCcJl9UpzDMKlrbc9Cp6Z\\u002fcHZ3GZ4y1BPbpfkISakRsmUagAQefDmOrjgjmGORFHuPBk7NSjenyPs0NC4cu3TooPzw9JYzr80TKnXflN2FI0OaIRveLTQH416EpIgBEzPfX3Hhi08MzHPhGemlXA2dwGjwfjtS5tMtwmCuPOVa1fcO4fop2Tm7S+af8OUeWsnb\\u002f5y\\u002f7pXNlMUBQA\\u002fvv735MWZTJbTn3N36dDSL7izuGjIM3ixuIO34N6ilm6rvbi3sbBQ0kTBkFqn7wykO6rt4L7HdSM8My3C2dxmrcN+hTtmUVZyRFYyvZ0SXA8X3IciY6e+dRzBymD4b6U0x4yKbyh9PlA6ghlH8FaT\\u002fDUAfb6CMyjtGaPiG0qXC0YZMAII9kRScD\\u002fB7OO6IUrzr1H5G6XLBWMXkD4EewzJv5Vgj+99kFLfMpJgJjICdtH4zvoSo2IdpT0FxjYgeAQzmeBdQ\\u002fKuBRgcb2IoikZCdTgmyVgeNWpd2XF0oH91CvFnxQcfTbsKHXC09wr20ephBwArokajIQ\\u002fjgjdDbsQmeUzSrk\\u002fOeqNaa1ShUcz5hvbMxAYvPDOthLO5C5w9UFBb0mS6feNTS665z\\u002fJTR5DEtffP1ai1v7y1HQCaqtvO7subtGg0etxQoaK\\u002fY5sXyxNXuAOAF4vXABI06tn0RsPexqKttRdOtFUMsX44TuWuleakmx7X9fDMtB7O5jazoacZhnkgXHAfsih9hVG50Sh9EQgOQQZSVCelK6B0L1HaEwyfT6+sX1OaA0bZuwAEMEKApMDQTKm2GtT7GP6bCGaC2YEGQ9fTlGorAAAhIBihlLGdUu81qPeSgrtJ0VNmRwIAUIZqSrXZ0PW0Fd8ZjMrfjNKXAAwADGAEg7GT0p6ltGcp9T8Mn2+AYJsf7GIdjdFoqG9ufttxY9yEnytO6YwGdKB\\u002f68qOj\\u002fOPmRrc69\\u002fCsfY1Fv1edQ6NDiiAK1wRlYlGPczQ3h9pZvA9I30WolE6Lkp27m36GI1izje0ZyY2eOGZaT2czZ3tzN6Lphv+oT6rHprfe3Agqx+cv+fX4631YgA4e6BgCBfctUba17MLWR56CYtbmnrbTGPQb6rJXld2vEnVa\\u002fMAzBoEDJfCt+vhmWkDnM1to8IdzLEhAa26YkMHpTJKXyZFzzKDchiBB5nB50nvNwEYlHqPUbkRPRjAKHuX4C1jBp1gBh5hBp1j+P0OZABQCqP8w16HyT+lVFuBEDJ8PmMGZzMCDzKDLzB8PgSCb1R8Y1T+an4wAAClM3Q9bc13prQnjdIXgWCQXq8xQwqYgUeYwXkMvx+BDKI0x4yyt80PdgcDyQhjstIH\\u002fo9kuHRj8SsF87yWRY5CowMyUtTDZ\\u002f7IFdejAw6S3VH79PnNdK80vD1hiud\\u002fOhraqzX5DG80RBObRNvgYq4xtGcmNnjhmWk9nM2drehspenGpIWjWGwaPyGTzZy0cLTpds83GZK0dCpEJkxP7arhsT8YwkAZ11eeuWrvR69d3IlrmrZxSwP3IQ\\u002fPTJvhbG4bLe5gjg0JOCENZQRvKSm4DQgWAAAwSN4qUnALAFCK73ofCABAsDIY3u8CGdD9JTuLFN4PAJTmxOWDjBKj4jsAYHi\\u002fRnAXXGpNwyC4SxleLwOAUf4hUOjlP1Z9ZwCj7F0AIyl8nOSv6bn2gmBPZXj9FwCMqg3g1vboACDwetY7cOfA\\u002f\\u002fG9nkHv5nL3p8zgMkz\\u002f6NZSGbS3Hf\\u002fpQFMxOmC3\\u002fU1Fd574he4u2+F8nzWxY9Go52ERQ7l4xGN4oSGaOLjg7iZDe2ZigxeembTgbO5Una3dG7LFpIb3HrEsJrV7dYWkbSjv6mbDCnePPanG9NQfzNzFzvrVh759\\u002feJOWh2fMcTwae3iMnhm2glncxto6CcgDPNAuOA+lJGcuUiE4C4DAMpQRxnQU6YEbxUyHwhWBgAAJQOqe\\u002fdqSnMYKDWQ3gQXbTRB8JYBIQBjF6VBm5RZ853B0ETpLgIAyV1y+TgAACBY4wEAKB2lzUWGXIwgfdHQFQhChIZcLoTnfUvCJDRqidqge\\u002fD0b6\\u002fk7lDQ3Ky8P0q99n+5\\u002fzx0+ne6GR0AXhq1hO77EszheEx7V7hzGLjgjmEYZiOczZ1K3qU03fDyo52qBN7dncp7vsmQpDXQXuHusQV3j\\u002f3BTPRGw1t5u9ceXlfU1b2vAIZ5AjwzHQJncxto8Ar3IeffvIqRj33448Hz6MCQ5rqrQjA3YEQhAYIZ333LUAOMXlv6Esxk8y8BAIieTyDdb7gpfRkAEMyEvrZdZRLMBEqXS+mLCZhnPmDddy4y3dC3Tbk0dAVjBxpxIYH3\\u002fxisFDR6BYIUoiF3uCdp2u6Ggho57Wfs96pzexsK70yauiomS8i0sRGnyqDdXJPzVclR29ZBLI0c6dSmdZhFUwNvAQARs\\u002fuqFJvhFe4YhmH2wNnceYTefEm7DAAUXbQ391ZIu+\\u002fCF3noHqEOQavpsInHdm7x2B8MAFrU0sfO\\u002fJkjrkMHMMyt8Mx0IJzN6dIYaZ8VGMImP\\u002fuFF4+z+4U7eiJytWbNhxvEMuUvD6+JD\\u002fY3OxbzLI4suGuN6nZNQ5euXa6XaI0avVFr3hlqtM8MH3aQ2eGY8125yyjBAyAAKKCu+HRhTaXY1C7mcrm8N1P8ysYv1nxno+leJMGM6T1ghnDnpxqu4DY01Bcma7RP0CE06nJcBuu1zGW3HvvJQBnRMUs6tcp38\\u002fd+WnRwbljq7JDkSUFxXiyrnnmVQXu6rfpgc8mu+ny5rafi40WBL45ajEYx15oYcD0asgmPIQrlWT5NhWEYhvUJZ3Pn8Qn0MhXc68qa0TFLaku6F3v6Bdvbe82TMUnaRWojzabALkN4apuRAknjPSfXizVoQ04X4DHYfCabz2SZbvAYLB6Tze\\u002f+P5vXHe8e3Vp74UhLGfotsKELz0zHwtmcLiPlodnEE1AU\\u002fHf97vqOrk\\u002fvuBpX202Uam1OWUNVs1iq0Gh09C6PeHTVdDTkOPYW3I2UoVJ+sVh2rkqe16apH2DvhUh+Ei64uxp1xYWulBJM\\u002f0YEHx2yZkt3UgRwqex+pe5y\\u002fJU9Vaz5zqaiPMEI2D2oOx0RBIfBdPUZ4D5l+Uc\\u002fkDLz46J\\u002f0QHrqA267XUXt9ddJICIFPgmewdH8H2DeCJfNp9DMtkMpt5o1Bh0Mr2mRSVtVEpKpC2VsnYb3kOY82LxPp+4VmDr2XvM0\\u002fCZvjfEfIxGMQzDMKvhbO4kSZnR1UUNAHBi54U7XrqGZFj75tOgN57c2d3kMCUrrvfgkMKm34ZFb7Rr5gw3+Z0Nd5z4RaZTowOOwCDIML53lMAvSuAXxBUFckWBXGEAVyhicUVMrpDFobW56LmOGjSEDV14ZjoDzua0cBksG66yGia+2nvqcEHlk1fPmJYai44NP3KV5sutJzYdydPpbZwwHlpwVxuUJzt2nBPvlepoXxqDuYihClgjzAOUvtx0g2BEm8etRDBToLuxjOGKrjJ60zcnmEm949bpbjtjoHRFBCsdGcRsc3fytKKu5r2NhegAHRRQtQpxrUKMDjgaj8H+dMJ1UQI\\u002fdADDMAzDhjGczZ0ha3b63g0nAKC5pn3bdweX3zMHPaIfW78+0Fzbbro9Yf7I3oNDCpuk\\u002fTlRT9n4cXcYypc0OramySQZI33DM3zC0nxC03xCYwT+g2KrWMzT4JnpPDibW4\\u002fDYMpoNpWZG5bKcW2jebc4Ulj11d6T10zMuGnGGGTofEXDtwdOX6xu1ur1UYE+V49Lv2n6GJLsXvz665Hsd7YePvTqPTvOF\\u002f15Iq+pUxroJZyYFPXw4ik+gu5rJqw5xmTgxwIAioL1R7N\\u002fP36xUSwN8haumJCeHBbYM6rQaOe8\\u002fE2oj9eWp2\\u002fuCZosf\\u002funpk7ZoVfv4bEt\\u002f2t2ydX3vP9nWX33uzIPRPuNFABQQJ3p2HWgZaPKYEsTqIHtavr+RPt28whJkE+kfCdiWt6vEkMY1bsY3GXmEUq1DQAIRhQwws3jViI404AQglFKqXYQvKvNhyjVNqAUQAgITv9N2PtHMCIIVgalyzcqvmb4fIIOYzYhgHhn7DWdJ5Rn26vRMQ\\u002fDZ7K\\u002fmnTDWH9bzgNhGIZh2BCGs7kzTJw\\u002f0j\\u002fUp6NJAgDfvrRJ4M2fu8bypnb\\u002f\\u002fHhk3aubTbcjEoLHXZXRe3xIsWGjUY9d4U55WHcCsUbx4KnfHFLTDOZ6zQtPmxoUPzYgmse4opsohtGBZ6ZT4WxuPS5pudiKeCJ9XqRgiNcMa9slz\\u002fy6a2x8xPPXoqsEdmYXP7t+d1Sgz4oJ6RwW83xF\\u002ffvbjlyoavzg1qXmPdWe\\u002fPmf\\u002fNqWuaMS549OOl9Zv+lUXnlz+y8Prbl8hBXHWPNYn+48\\u002ft2BM3HBfrfNHitRqDYeuxAgutyYWsBhLxqT8tfJvAvVjaNjwnrihfUtlS3iZePSrKm2A8C7vx20udoe4icaERc6Mj4UHXAo2gV3uV7ye+171YoCdMBBMn1nIwV3I2XM6Tw4PfAa8yBmDUq9zyj\\u002fjBTcCQQXwGhUbTIqfwEAQnB5vwV6CBEpfMgoe8MgfZFBsAnuPAAGgIFS7zZIXwYAUvifvlrKWIUUPWcQ30ipdxq6WKTgXoKZCABglFCGWkrzLwCDFD6I3gezhE0yv5p0\\u002fQOnfjvZVomOeQw\\u002fjuCT8deN8Y9CBzAMwzAMw9ncCdhc1o1PLfn40V8BwGgwfvDgT\\u002f\\u002f+eXrp7TPHzklnc9GPeWql5sy+\\u002fO3rDuWfvNws+M5XVjKYNFofDDo2FNw1BnqNU11GZaC5VNKZDJTx8bN\\u002ftail6AAdTJKxMDx9VUxWln8UYU33TgyzBM9MF8DZ3EocBu1CpdLWPvWDhVqrf\\u002fj7bb4C3ge3LmX27oMnUahe\\u002fXN\\u002fVnz4N\\u002fdey7i0\\u002f8p\\u002f1+\\u002f653zxoYKKWRnxPUfmVjdteOT6pLAA05f3fbPleHF1bnXTqJjLdeeBj7HmsRo7pd8fPBsb5Pfbozdw2UwAuGfehOve39D9AAAAsHryqL9O5m0+lW9ecN9+rggAlo1Lu3xc\\u002f+rbJLtOF\\u002fd8yWUz52QlpkYHi\\u002fgckiBe\\u002fH43RUFEoM9N87OAoqRKTUVD+7G8arlKAwDLpqS\\u002fdOu8y9\\u002fLaejN42Z19a\\u002fVr3fpbDyHYI0QbkwoN7ZJXWUezO7cjwvuNiCFDxvlHxkVXxBkMEV1gVEKAAR3Aclfix5qNVJwOxgajcofDZIHgRAQpB9lFJu6t5P860nB3egdrEawxzF8PjR0PU2p\\u002fjao\\u002fgZgAkEA1f3+GFlQj1mPx2B\\u002fOen6F3K2ba+7iI55gHSfsE8mXBfK80YHMAzDMAy7BGdzh5t\\u002fw5QLR4oPbzln+vLCkeILR4pJBhkeHxQQ6ssXcSmKUsk1rfXixqpWythrifSaRxdOmNerbePQw6V\\u002fbb5Up0JDnkFl0KIh91lXdvx0e6+PurQwScYNseNvS5wcxLVxkROG9QnPTNfA2dwaPPoJSKH3oNd5Z3j378MVzR1TU2O8+VxkaHdOqVKju2HamJ4KOAAszEz553zxkcIq84L7\\u002fNHJPZV0AJiZHne8uLqmrdO84D7wMdY81sH8CqORWj1lpKnaDgCBXsIlY1N\\u002fPNj9jgsAUsIDM6JC9lwofXrFTAGHDQAGo3F3Tkmor2hcfGTPYQPYe7a053Z6bMiH9y\\u002fz9768iP61n\\u002fdrdHoRn7NyxuXuf1Kl+t2Nh3aeKtp2vIDPYT25dlbPkJPQKLi3qmt\\u002fqHxRaZChA4422ndmU1Ov1\\u002foOTVOtsjiKn2IexCwiedcQrBFGxXeULh9AT7DSCd5qkr\\u002fGzl1JSa\\u002fnCe4so+IXSpdDGZqA9CHYk0j+9QRnOnooTQR3EZOVaVT+TGmOUoZaAAPBiABGBMGZRXIXokdjVmOTzLezrsnwCXu\\u002fYL\\u002fW6CmLjwgg1sSOfSpjvg1nsLFBwUjpm1TFABDOH8oX3WMYhrkGzuaORRDE45\\u002fdqpJrzuzL6wkaDca60ua60mazA1Er7p1zy7NDfyGID7tXw1ZrdDmiE4UzqPWessK9RSX9quQIGrXahMDYl0YtiRH6owMYZh88M10JZ3OLfNh8NGSJYkivcG+Vys9W1M1IjztcULntbCGyBrygrgUAHvlhm3nQpEOmNP8yNeJyI3UAMNXupapeuXvgY6x5rKoWMQCYN20HgMRQ9PVh9eSRL\\u002f62d2d28apJIwHgRElNh0x599wJ5j1wBlBQ1f1WjcdhfdC72g4AHBZTo9Ortb2yvxef++rtC4xG4+4zJb\\u002f9eyEm1G\\u002fVzFHmBzictX9LCn3XT9WvuqDaDgBJoqxdTT8gwTJZDi6400VRGoIzk8GZiQ6YI\\u002fjMkO6dVBEEM6nfIfYUBttSr3abvjMwQknR0yB6Go1jdrspfuKkoLjnsv\\u002fO62xAx1wuQRT0SubSTD+rzl5ig5RM37ax5nEAeCJ1DzqGYRiG2QRncwdisZkvr\\u002f\\u002fPX5\\u002ft+\\u002fXt7VqN5Zqst7\\u002fogXfXTl06Bh0Yivw4vT67WqNL66Er3JUe01Lm3YK9apt+GJIg7kuecV\\u002fyDNLKUgSG0YFnpuvhbD6AEJ4XGrKkXSNHQ0OIkMvZ8MhaI0Utf+vnt7cempQcFegl7BmVqdUAcPOMLF8heqY8KsDH\\u002fEt\\u002foeXMPvAx1jyWQqMFAD6n12UKfA66l8PCzOR3\\u002fz68+VS+qeC+43wRACwba1U\\u002fGQCoaOww3ZiTlRjQu9oOAGwWAwA0WvSEFkHAMzfMOZpXpVBpP\\u002fnr6JysRD8R7bM71rOq4E4B9UfdB1Jd9+\\u002fTHxbJ8WEFchl8DkNQLstBh60WwAkXsfxkOrF5sFJ+cU6w7Y1QhivP2iAIc7sEUdBvM+7cUZf3SdG\\u002fDUoJOuwSITzvu5KmrooeM2x3qB8+NAYFGsIwDMPshrO5AxEEserBefOun7R93eFDm882VLSgRwCQDDI+I2Lu2snzb5hyZYf3oSqAc\\u002fnDvJU8tuAu0fZa4uculbL2nfX5aNQKDIJ8Y8zypZGXr4vHMAfCM9NdcDbvTwj9njZNSikaGkL4bJaQywGA51fOeeSHba\\u002f8sf+zO5f3jAo4HACYMzIhM\\u002fZyP\\u002fQ+WXNebOBjrHks05anEnmvhfNancH8SwDgsJjLxqatP5pT0tgW6e9zML9idExYVKAPclh\\u002fuhTd339EbEjvEQAADosJAHJVH42GRHzOwvEpfx2+qNTodpwovHn+WPQIx7Gq4J7T+W+lvN8OU1H8lEzfWRH8pCBOFEl09yp5IW9F76PoiReMvCA5ZB6pV5VpjCoOiZ5FwTCMFgKIpZEjF0VkbK+7+PKFHa68ii1eFHhT\\u002fIQVUZk2bMOFDUYao0d8vsUwDBt6cDZ3LG9\\u002f0Y1PLbnxqSWSdlnZhRpph1zWpSQI8PIV+gSKkjJjBF7D7jOIDSvc7dxu0Ul0RoPMM3rdbKg6g4as87\\u002fMZe6taVIUXsU1lOGZ6UY4m\\u002fcplE97hXuzqgsNDUWzR8TPH52050KpeWOZ9Mjg7ecKz1XUDVAEdxRrHisu2A8AyprbJ6dE9wSrWnutqDZZNXnk+qM5f58pHBEVotbqrx5v7fJ2AFCqu4vpPlestQcAIY8NAHKVhqL6OIUwNiXyr8MXAeB0Ua2bC+56Srev+Vc0CgAAkfzkZeH3hnBj0AG7xQjTkYK7kTI0KMvihO58QcewIaOoq\\u002fmn8pOuyeh+HMH8sLQVUaMzfMPRMWxI0+IV7hiGYc6Es7nD+QSIxl2F9x0BAPCnX3CX6dQynVrE4qIDbtXhGX0GFHrN37W5aNQKN8SNXx41Go26lm3NRrBBAc9MT4CzOcKGFe6Nw6PgDgDPXjP7dFmdeWOZRWNSPtt1\\u002fJfD2QszUyL8Lz91HTKlkMfmMC1Xfa1nzWPNTI9\\u002f7+8jf564eM2EDBGPAwAylcbUMQYRF+yXFR9+IK+sQ67gMJnzRyehR\\u002fSPySD1BiMAqHV9\\u002fOGIBFwAMFJUW5c8yAe9Yi8isPsnr7rUl8ZJLD\\u002f1OZ3\\u002fyvUSNAowI2jl7KC1PUvaHSuI00f3qDZNAy64Y5idDJTx48J\\u002ffyg\\u002fYaCM6JjjiFjcdJ+wSYFxk4Pi0nxCCbjirCI2DOAV7hiGYU6CsznmbFwGy5vNo9slplEpSfbu4+JuN2rXeMTp\\u002f0PNpTbs6Rcp8H0yYx4adTm1oY9yBjY04JnpXjib9ymC74OGLCmTtqKhIcpXyPvvillP\\u002f7rz1T8OfHrn1QDgI+C+ccOCJ376Z9V7vy7ITA72EYrlysoW8bmK+p3P3h7mR\\u002ftygQFY81gR\\u002ft43Th\\u002fz8+Hzaz\\u002fcMCsjXmcw\\u002fJtXERngXd\\u002fRx0mR1ZNGPf3rTqmyataIeFPbHCuJ+Fy1Vg4AYmkfNYeezuw1zZ1XFtx7Gsp3yui9yaHLcsH9nHgfGgIY77\\u002fwquAb0KjjBHD6ONvWrnH\\u002fbhIYNqi1q+WPnf3zXEcNOtAXkiBWRGWySUadorNNI5dolUq9Vm3QGSgjRQGLZHAYTDbJ5DFYfhxBEFcUxBOF8LziRYHJXsFh9HMk5jmaVH2cf7ZBm6YKDWEYhmF2w9kcc41YYcAFcR0aHVCD5xXcGxSdaMgdjrWUoyErPJUxn01a\\u002fsDubENmHTF2JTwz3Qhn8\\u002f5ECHxFLC6tbmBtaplYo7ChGdpgtHBM8u4LJQfzK7afK1w6Ng0AZqbHb3z0+nUHzh4prOxUqLz53Eh\\u002fnwcXTvF3wo6g1jzW48umB3gJ\\u002fjp5ccPRC0HewjVTR62aNHLKc1+YfZtuV41K8NvKF8uVV1\\u002fqkGOlsACvNokcAErr2tAxgNhQP9ON7NL6cSnoem75pXY0eqMTT3SBxYJ7p7alUVWBBAM54YtD70SCjsVjCPlML6W+Vx\\u002fAdm2j+ZdYv0hfZogtiRMb2oq6mu49uaFNLUMH+pLuE\\u002fby6CXpPv125sKGsPXVj6AhDMMwzDPgbI65jA0F91Jp6+zQFDTqVjWKPvrGut7xNvQztUVxooA5nvFkWvmCgw1GeGa6C87mAyCASPMOPd1Ob\\u002fFWcVfz5KB4NDr4nXjjP2gI4OPblyGRxNCAt25ciAR73Dh9zI3TxyDBBZnJCzKTe7605hiTgR8LAAgCbp2VdeusLPPgxQ8eNf\\u002fShEGSDJII9BJMSrrc8N0aKVFBueWNAGD6PyIhPMB0Y8\\u002fZkruXTkLauBfVtJhu0FpTbwMLDWEqFXloCGBW8BondZIx58sKQiJSnXPb62DYEJbX2XDrsZ+syegEELclTN44\\u002fY7hk9ExDMMwbFDA2RxzpThR9+dV6xV3NaMhd6uRu7\\u002fgXiPvaFfTbiV\\u002fXcxYNOQmw6c58nCDZ6a74GxuUbov7d\\u002f3YiduiTH4nCqtbZMqlo1LI0l6nY7GJHa3RWnskJY3tPcehLEpEQySBICa5s7f\\u002fs0xH9LqDX\\u002f8e8F0O\\u002fxSM3cnsbDCvU5ZikR4DGGG9xQk6AxcBnoxiMbg3PY6GDZUFUqabj\\u002f+szXt+Vgk452sa+aHp6MD2PDDJnlM0q59z\\u002fRGtdaIX7cxDMMcA2dzzMVihf5oyBIPLLhXy9HP4a5XadPPMDcsFQ25g9qgE3tGH3zM4fDMdAucza2RQf8Ew4nWinuTp6NRzIMZjdTXe0+zGIzrp41GxyyZPCKWzWRo9QYA2Hmq6KFrp5mPevG5WckRZ4pqAeD93w9VN3cumpjiK+LXtXSu++dMxaW9UjMvVe2dxELBvUODLs6PFWS4Zo8F9hWFHly4wTAbiDWKB07\\u002fZk1GZ5PMryZdPzEwDh3AhqWZwfeM9BnoSjGLLkp27m36GI1iGIZh9OFs7ljfvbTJdGP1wwu8\\u002fNBVPgM7dyC\\u002fpU4MAFmz0kKiaa8BH0QSvdALji2qU3RKdSovFg8dcBMDZSzygHMAlTLaZc1YYUAIz7kr76w0fLYiHIbwzHQ9nM2tNNKXdiX0Qme9Uq\\u002flM7v3w8Q8lpGiPtpxlMtinSytya1uemjRlEAvdF9Ti\\u002fgc1vRRcfvPlwHA9uOF96+YYlrS3uOW+WNNBXeKgr8O5f51KNd81GT51Aw05FAWOsN0atFXsQh+EhJxEg6JvlHTGmnsmYBhGABQQD169s9mK662I4B4Y8zyYZvRsSvxGfa+mWaT9EoYGIZhWJ9wNne4TV\\u002fsM\\u002f0n71KiY5Yc2Xr+syc3fPbkhiNbz6FjQ0ukwM+fQ+8zMAXUmfZqNOo+5bI2T9hWsUZOuzNqBv12Ck6CuzQMYXhmuhjO5tYL4\\u002fvQbWumNxpOtVWiUcwDUbA7p\\u002fT7f8+KZarHlk67Y8549ADrrJmTSRLEVVmJHz+0HKm2A8DE9OhZmQlI0NyqmaPiw2lfyUeLhRXuagN6kY6A6YVEnIRBoD8bBc7dQBbDhp7NNTlnrfvYc13s2EURzj2\\u002fhw0uPIa9r\\u002fYcXHDHMAxzBJzNPUpAmK\\u002fpRkV+fe+RISjTL3J\\u002fUxEaHdDptqqrQj2i4wQA5HtGSa5LR\\u002ftC7VghvUqT8+R5xnOIOQOemS6GszktM0OS6V6Esb0+z9M27sauRJLE3hfvRKP0ZSaG\\u002f\\u002f3m7WH+\\u002fVYtXr1jwSOfbj1f0se7tbljkx6\\u002fbgYadTT0JABCR6GLyvl2l2CspDKg23ewCOduIIthQ4xMp\\u002f6gYD8a7YsfR\\u002fBY+lVoFBveeEx7V7hzrtiKA8MwDKMLZ3NP4xPY\\u002fWmotqSp98gQNMY\\u002fCg1ZcrKtCg25zynP+GEUei0asiSUb+\\u002fbMIcwUtTx1go0ig0VeGa6Es7mdM0OSUZDlhxsKpHSP42EDV4DVNsBgM9hffX4yuduumpEXCifyyYI8BJwp46Mff\\u002f+ZW\\u002fds5jFZKB3cDR0FTmCBIYRXVdO9f7SWZQGdMvmK7u6Yxg2gM21OZ1aqy6UXhs7TsjEJ7SwblMDbwEAEdPeBSx2rnBnEBZOCV9JT+ELoTAMG2pwNvc0Il++6YasE10eNPSM8Y9EQ5ZUytoqZG3xokB0wOU8pyRnTb9mhMAz2hBfENd1aIb+PB+28Mx0JZzN6RrlF+HD5kuse9JMtEb9P\\u002fX5a2PHoQPYcEUSxDXTR1wzfQQAGCmKJFyxI2kPC+WMK2vcV9bBnUSulyARL5Zz2+tg2FBCAfV7lbWtRa+OGoWGsGFsYsD1EwOuZ12xkQZdPIYolJcSyrPxsj4OaeGU8JU0Bj0awjAMG8xwNvdAHG53vUkmoVEFGKTSvENFLPTzoEX\\u002f1OehIXe42FlPq1LjPDqjAQ1ZwmN4RFlzb2MhGsKGEDwzXQZncxswCHIO\\u002ff4wP5SdMOA1WFhfXFxtB4sFd8EVLQUUeikScQalXtqhaUSCvuwgJIJhWH8qZG3V1m2DE8b3ieB3dyPFMAfiM31viPn4hpiP0QHrsBm0C+74EkIMw4YYnM09kEbd3YTBaBj6H+mZJGNGcCIatWRnfT7lqquiB7CroQANuQmXwUJDlujpV0IdTmXQbqm9gEaxIQTPTJfB2dw218WORUOW1Cs7t9VdRKMY5g4WCu5+7BAkUq8sRSLOUKnIu\\u002fJdWjA3GolgGNaf8+21aKgfEXwfNIRhHoBLv+DeqnbRNVgYhmGugbO5B6ou6l4VJPDq7i0ztF0VRnsH1FqF+FhLORp1LZ3RsMNjai42XLSnNujQkMttrc2V6dAd3bChBM9Ml8HZ3DYZPmEZvuFo1JKvSo7g654xT2Ch4B7ERffJqVYUXFkKd7hi6Vk0BBDJp71nAoYNWwUS9BqR\\u002fvA9oxMfhiF82bRbwNfIxWgIwzBsMMPZ3NPIJcr9v5003Q6JsXezk0FhWnCCDctgfyjvfpbc5d+mYivbJbsAj\\u002f6fZ7u7+1NrDPp1ZcfRKDa04JnpMjib28yGhux1CvFXpUfQKIa5nIWCe4wgHYkoDbIyWTYSdCyxtimv6xgSZJFsXHDHMOuJrf6M0aLCi4IxTxTMExFAr89acVczGsIwDBvMcDb3HAa9MfdYydMrPhS3dJki6ePjex8yNPEY7ClBtH\\u002fTU22V+Z0NaNSFvvOkklwQV4SGLGlQStCQa31ffrzR3T8D5mx4ZroMzuY2WxSe4cOmfT3ZurLj5bJWNIphrmXhGqIYQRqLZOuM3Z0KTQ61\\u002fpkkyjKPONaBlo1GCm0Nligcc+UOrhiG9adLa20z6yp5u1Sn8mLZu0MmhjkWm2T6cvhijQId6N\\u002f5jho0hGEYNpjhbG6\\u002fnT8d\\u002femNrWj0kofnvUmSlk\\u002fuUhSo5Gq9rtcnlLlrJ5t\\u002fOYRdE515oKkYjVryVv6eX6fdjkZd4mBzifXrSV0gjI\\u002fui2ZRkcSdawjqFJ3flXrQGQvMSfDMdBmczW3GYTBvT5z8QcF+dGBAeqPh0TN\\u002f\\u002fjbjTgGTg45hQ4LRSLVJ5MF+tM8aupKFFe5skpvqNREJ1ilLzon3IUFHye48cFFyFI0CjPKdgYYwDOsfk7Tw191DbdBtrOyjiROGDcBA6SvlZ460rtvR8NZftc\\u002f9UftMTuc29CC7hdNsYtiqluFF7hiGDSU4m9tPq9ZKxQrkv55RuUR55eiV\\u002f8k6FUi1fdEt02JSw8wjQ9iM4CQbNvHL7qh1Sxd1ndHwYcEBNOpWYTwfNGRJnqRB56bdKfVGw+Pn\\u002flIZei25w4YkPDNdBmdze9wUNzGER\\u002fvkUIWs7alzm13QEBtzi\\u002fyq5kVPf7fmlV8+33K8osGqHYldz\\u002fKf\\u002fTi\\u002feWgIYEfjNzWKIjRqt1JZ9t8NX6JRAF92cKrXBDSKYVj\\u002fREwaV4R8XnL4dFsVGsWwfuR2\\u002fvNt+c2b61440\\u002fFHsfRgteJcrSJHrKkzP2Z30\\u002fub617Y2\\u002fSxeZCuVG90426LttZeQEMYhmGDFs7m9suYlDjr2vG+gV7ogB1mrBh77xvXodGhiySINbFj0agV3srf4\\u002fr9zL8sOexpnQSSvIPQkCVKvfZUWyUadYm38\\u002fe6tx0Q5jJ4ZroMzub24DCYD6XOQqNWONhc8lruTlxzH5JOFlQDQFl9+\\u002fc7z+RVNaHDnsFywT1GkB4rGIEEDZT+p+qXczoPInGbGSnjodY\\u002fNtS8cWUzGQCYGbSKbidfDBvmaC0N1hsN953asL7yNM5G2MAoMO5oeHNf8ydyvYXTyGxSUCk\\u002fc1Gys11j+\\u002fvFNB\\u002faiwe31F6Q6dRoFMMwbHDC2dx+CSOjnvrq9g2F73xx+IW7Xl05dnY6h2fjlnRefsIpSzL\\u002f9\\u002fuDz3xzJ4ttoTPnEHNt9Bgbtk4VaxRPnP3LQBnRAafJEdd5VPd2kzhhoA1tDTbV5KAh5\\u002fum9Oj6ytNoFBui8Mx0GZzN7bQsclSyVzAatcLGqrMv5Ww3Up7+TEp11jYdwkzOl9abbjBI8qqsxN6DnsKqd4oLQ2\\u002f9quIppBSuM2o3139SIjs7K+i6YG60+RAtFFCV8ov7WzbUK0vRMQAACOclZPrORqMYhg0o1Yfe0mC1Qff6xV1\\u002fVWeviR23OHKEkP57L2w4ONTybbH0kOk2i+QGceIFLL9SaR99wOKFE7LFWwCgQnYqgBOLDltnhC\\u002ftgrtMp\\u002f669OgT6XPRAQzDsEEIZ3MHik0Lj00Lv+a+q6RixXXJj5uCH+99JiQ6oPeBfSL4Ii6TxUDDw4Y3m3ddzNifKk6iA5ac66h5\\u002feKuF0ctRgecoE4hfuDURr2b+l0MgCSIDN8wuktWDzQVV8s7YoT+6IDT\\u002fFJx6qNCz+rGgzkVnpkug7O5nUiCeGn0kpuO\\u002fmDDGdy\\u002farI7NIo3s5Z7YGd8lUH7T33+71XnRvlFPD9yETqM9a+1U266kR4bLOR56B+IVQX3UF7c9MBrD7X+gQ4AFHSdLOw6lSjKTPWakCTK8mJZ+7JrpIwt6uoKxcVz4r0dmn7X\\u002fzMJ1vKI+\\u002fHydgyja1xADAEE3bPiJdKWV3J3vHZxZ6pPaJZfVLxXYBjPO5Tv7cXichgsLslkksP3oybWrqk21dCZBHta0O0jfRaxSA4AvCedjx4KEMZLM83AelWBzR3BUrxD\\u002fDnCDk13NrXST+Un54eljfANRwcwDMMGG5zNncHLTxAU6ddaJwYAoY\\u002fAy0+IHoH15Z7kaZtqsuV6DTpgyW9VZ7kM5lMZfbxbcKBahfiuE790apXogGeYEhRPt6xpoIxv5u3+etIN6IATGCnq3fy9NpxQwQY7PDNdA2dz+432i7wzaerXJUfQASscbC659uDXH4xb5SGfECmgsjtqd9Tn\\u002fVOXZ8qqo\\u002fwi0IOwAXV0de\\u002fHkxBuzbIJ97Cq4A4As4Kua1SVl8qy0QEACqhSWbZpyIvl788O8+f0cfquTJbTpK5S6mVKg6xD09ioqtAaLV\\u002f1vzjszhBuDBrFMMySUJ73aL+IHHGvttpWMlDG\\u002fM4GR\\u002fXIYxAki2SwSSaHwRSxuIEcoT9XEMQVxQgDEkSBcaIAHzYfvQ\\u002fmkS5KdpveJs4LfSTNew463BuL5Hixgrt0zWJN99VeNiCAmB6csIVmW3YDZXzo9O8bZ9xhw+46GIZhHgVncyeJS48wFdwx6\\u002fmw+XcmTbVtnemP5SdlOs0LoxaxSWs\\u002fftKSI6574NRGj622A8C8sLQPCvajUUuOtpStrzx9Q5zNSxes0qGRP5v999GWMnQAGwbwzHQNnM0d4v7kGcdaygskjeiAFRqUkrVHvlsZnfVw2mxfN\\u002f2OFFD5nY27GvJ3NRS0qKToMEaHztB9rYOP0OMuXOhh7TsekiBXRz3+Q+VLDapydMyMVNch1XVUKfLQAYDj7X+jIUumBV4ztq8tWzEMs8b1ceNtS+qOZaCMBoNRbdCBDtrUskpZG3JApMBvrH\\u002f0uIDoacEJ\\u002fhy8ysxz1SpyAMCHHWax2m4iYPp16ZqVhk50gI7ZoSl0C+4A0KKW3nLsx28m3RjtwstdMQzDnAFnc2eIS488tfsiGsUsuTl+4obKM7btg7qpJrtU2vL+uJURfF90zA5ao\\u002f6L4sPryo7b0GfAlaIEfsneISVdzeiAJW\\u002fl7QnkiuaFpaEDDrK3sfCVCzs8+VwF5lR4ZroMzub2Y5KMt7OuWXnoa7VBh45ZwUhRf1Sf29WQf3P8xLWx4\\u002fw4AvQI55DqVMdbK440lx1tLRdrutdlY3byEfLaJHIAUGv16JjHsLbgDgAckn9b3Ku\\u002fVr9erShAx5xgcsDSeSE3oVEMw6y2KCJjXdnxYvrvn1ysTiGuU4i31OYwCHJsQPTC8IzFERk2bOCDOZtM1wYAobwUdKAfpoYzeqMWHaBjRnBiAFfYrqbXVQYA6hSdqw9\\u002f+8LIRUsiR6JjGIZhgwfO5s4Qm4Gv3bYFl8F6YdTiB0\\u002f\\u002fhg5YJ6+zYemBz+9MnHpH4hQbtmBFGCjjzvr8L0sOV8stbOTuIa6LyXo19x80aomBMj5+9q8nMubeEj8JHbPPxc76Dwr2n2mvRgewYQbPTNfA2dwh4kQBb2WteOzsnzbvgyrTqT8vPvRt6bGF4elLIkdOCIhxRmeeRqUkW1yX01Gb3VFbJmu1+afF+hMX5m8quJv+75loFNwBgEPybo19eWfT92c6dqFjjkMS5IKQ2yYFLEEHMAyjgwDijTHL1x75TmPw3JN+5gyU8XRb1em2qvcL9q2OyboxfkIw1ws9CHMfPaUFABZh7fstnVENAByGXWsHmCRjZfSYr2zq1ifTqZ86v\\u002fm36nP\\u002fSZ4xKSjOsduBaAz6RpWkQSkZ4xfFZ7LRYQzDMAfB2dwZ4tIjuHwOAJCkI1PDcDAnNOXqyFF\\u002f1+WiA9bRGPSfFx\\u002f6vfrcdTFjV8dkBXJF6BFWqFN07m4o+LP6fL3SqqvovFg8qU6FRl1uedToj4v+7dLS\\u002fkkMlPHtvD1HmsueG7koTmRvs1oDZTzSUrax6uyxloGuXDcnZHJ4THabTVc2YJ4Pz0zXwNncUeaFpT2dMf\\u002fNvN3oAB1ao\\u002f7vuty\\u002f63JFLO604ISx\\u002ftEZvuHJXsEs+sV3A2VsUUkblJIahbikq6VU2lIqbbHhDwqjZeqImNOFNQBwodyWFkOuQa\\u002fgDgAMgrk07O4E4eh\\u002fGr\\u002ft0rWjw3YL4IRdG\\u002fFwBD8JHRicDJRRrtPI9GqFTiPTa2Q6tVynkenUcr1GplPLdBq5Xi3TaeQ6tUynkenVch29bYi21eWeaK0QsjhCJkfE4gpYHJHpBpMjYnGETI6Qxe25IWRyhCwOgyDR7zJ46IwGub7X09X9lF66Lbs0Kteru7SWNwlAPJf993ucfSImR8DiiJhcIYsj7LnBNLvN4oiYXAGLI2CyHVvCc7gU75AXRy1+Pnsb3R1a3EumU68rO76+8sztiZPvTJxq\\u002fxoozCH4TB+Zrk2ut3YdWae2EQCETHubulwXO\\u002faH8hM2vzfN7qi988QvEXzfq8JSJgfFZ\\u002fiEWdmaUGvUd2qVYo2iQ6NoV8tb1bI2taxFJWtWdTWppGKNwvRntXvuQ1FMP\\u002fTO2JCDExDmRjibO1xYbOCWmo\\u002fRKGadZ0cuPN1e3azqQges1q6Wf1586KuSI5l+kVODEyYExCZ4BQ6wgtJAGRuVknxJY664\\u002flxHTaGkCT2if0lewb\\u002fNuPPqf7+sU7i5az+XwboxbsLnxYfQAeucbKtc9u\\u002fnc8PSVkWPmRAYS\\u002fcznd5oONdRc7C5dF9jEa1\\u002fOzbJ\\u002fHryjW1q2SNn\\u002fkDHsCEBz0yXwdncUW6Kn9io7HLIbroynXpnff7O+nwAYJGMKIFfKM87lO8dxBXxmWwug8VlMEmC1BkNpv80Br1Up5JoVRKtslOrbFXJmlRdHt7TbEhaOjn9622n5CpNm0R+PK9qyohY9AgPQLvgbpLqNT5eOPJ4+7aT7dtVBscs4OcyBNMDr5kUsJRJuP8PeAB1CnHPh+3Ln70v1dBNn717augqg129FCxSG3RWruzowWOwhSyOiMURMDmiS1X4no\\u002fxpsK9KShkckP5XjyGE1du6o2GeqWk52k0nX4YoIRhc8XNSlKditb6F5IgBEzTM9nH6Q0R6\\u002fJ5DtMTG873ofsWxH4rojL1RuPLF3YMrrwOAGqD7oviw5trLrw5ZvmEQE98AR1uAjmxMl1bvTJPa1SySQs16wZVgcrQBQAR\\u002fAx0jKZgrtfN8RO\\u002fLT2GDtBRr+z8sfzkj+UnASCAKwzhevlzhRySyWYwKYoyUEaNQa8x6pV6rUKvkek0XVqVs1\\u002fAMffCCQgbXHA2xzyHiMV9M2v5ncd\\u002fsbPEYKCM5zpqznXUmL4M4orC+T4CJofPZLNIhsqgUxt0Sr22WSVtU8tseywBk\\u002fPR+NVcBivVO8TtBXcAuCNxyqaaHFpVRXNGitrTULCnocCXzR8XEJPpH5kgCooS+AZwheYf2SigNAZ9i0rarJI2qiTFXS0FksZCSZNtXY9fG3N1pl9kp1ZJADHoXn8wK+GZ6TI4mzvKUyPmibWK7XWO3I5FZzRUyNoqruhrj3kgEZ\\u002fz9PWzXli3GwA++utoZlIEn+NxlWQbC+4AwCa5s4JWTw5YeqHzUE7nvwNvpjqwQE7EOL\\u002f5mb6zuQwLFRy3M1DG+fs+QaODisqgVRm0bdZdePVq5rKV0WPQqOMUS1tWH\\u002foGjQ4eRoqS6dQynbrZuiLJwfmPBfPccB3WqpisUL73M+e3DMY9OppVXXec+Pne5On3Jc\\u002fA1SL3ShBNqpSf0RpVh1u\\u002fmxvyEDpsxkjpD7d8a7qdKJrae9AWdyVN21ST46gJ3K6W29AUHhticALCBh2czTHPMSEg9oVRi16+sAMdsEOrWmbbdqwDeDVzaYzQHwCSvYP3Nhaiwy7HZbCeypj32Nk\\u002f0QGaOrXKvY2F5r8Ri2RwSCZJkFqjXmPQO6qW92TGvCURIwDAl81P9AoqlbagR2BDAp6ZroSzuUMQQLyVtcKLxVtfeRodw4aHRRNTmzqkX2w9UdnY8dhnf791z2IfIQ89yK3s\\u002fQvhkLwJ\\u002fgvvTXj3seSvloXfm+E92Y8dYs01znymV4Jw9NyQGx9I\\u002fPihpE8nBSzx\\u002fGo7hg1eU4MS\\u002fp5934qo0SRh+c\\u002fT0xgp6oviw4+d\\u002fVNnNKBjmAuleV\\u002flxQoCgNzOf7Y3vC7R9n1Bt0Tb9Ffd842qIgAI5aVGCzLRI+gTMjkvjcIbe2AYNtzhbI55jtUxY29LmIxGPcltCZMXhndfZpfiHdJ70G0WhKcvjhiBRu1m6nsm1anUBp2japp3J00z\\u002fyceHxhzeQwbcvDMdCWczR2CAOK5kQsfSJmFDmDDxh2LJ7x06zwum3m2uG7Viz9\\u002fv\\u002fNMbasEPch9bF\\u002fhjvBlB4\\u002fzmz\\u002fObz4AaI1qsbZZqhPL9Z0ao1pv1BopA5Nkswg2nynyYvn7sAK9WPZ29cUwjBYfNn9lTJZMp9nfVISODQb7GovuP7XxkwnXeULbuOGJSbAXhD6+qe45A6UvkR4pkR4J4MQGcmJMo02q4v3Nn7WqK5pUxRQYAYBDChaGPWH+HewxNyx1dUzWH9Xn0QEMw7DhBGdzzHM8nj63QSnxhJXjV1odk\\u002fVkxryeL1M9puAOAK+MXlooaaqSO347NAe6K2nqI2lzzCMTAmJ\\u002frcArSYcyPDNdCWdzR\\u002flPyowAruC1i7v07j4BgLlSR5eiVSJvkyh0esOk9JiDOeVimfLzLcc\\u002f33Lci8\\u002f19eLxOVY1x\\u002f71+evRkOM4rOBujk1yQ7gxIdwYdADDMJfTGw3HWyt2NxT821wi09Hex8+jHGstfzZ76\\u002fvjVlpzGQ3mDFGC0UvDn9\\u002fZ+I7WqASAdk1Vu6bKNNSsLm1Wl\\u002fYcyWd4L4t40Y8d0ROx3zMjFpRJW3PEdegAhmHYUIezOeaBSIJ4f9zK57L\\u002f3laXi4651dWRo14a3evCuBCetzeb16W1rgmXk\\u002fGZ7M8nrr3x6Pce21DigZRZ\\u002f0mZgQTHBUSTBGGkHLNIGfNAeGa6AM7mzrA6Zmyqd+jjZ\\u002f+iu8EhNnjNe6Lf1qBSpVqq9Ig\\u002fLqcU3DEM8wSNSskf1ef\\u002fqsn22PdMNtjdUBAvCrw\\u002fZSY6gLlKgmjSLXFfHW39oVR2xEj1sY6AADLFe+b0wNtFrEB0zD5cBuurSTfcfOzHkq5mdAzDMGyIwtncNcpya8\\u002f\\u002fW1CWW1Nb0iSXKBVSFUEQAi+e0JcfnRyWOCpq7FUZcemOPIs8NDAI8s2s5SIW13O66C6PGv2\\u002fzGVXFoBSvEJOt3evEnC7GKH\\u002fuik333L0R1rbZbsAgyBfHr3k2r428fJi8ZK9Qoq6+u4oiA0NeGY6D87mTjXCN3zTrHteurB9d0MBOoZhboIL7hg2BNUpxF+VHN1Wl2ugjOjY4PdlyeGpQQmj\\u002fPCHXrfxZgUvCX9Gob+nVnmhSVWi1Is1RgWL4PKZPiG85BjBGCEzAL2Pg4hY3HWTb7rv1Ia8zgZ0DMMwbGjB2dw1jm3P3vj+zsqCenQAQNum62yT1pU2H9ue\\u002fcNrWxNHRa19fPGkhaPQ44Y3UxfdEJ7XR4UH3DtXGQT5ePrcWxMmoQMAAJDi7UEFdwBI9gr+ceot953a0KKSomNu4scRvD925YTAWHTgkgmBMZ5f1sTshGemw+Fs7hoiFveDcaumBSe+X7BvKJ3VwAYvXHDHsCFFqdd+UXL454pTQ7iFmZGiXsjZtmnWPSySgY5hLiRg+qZ6zUr1cvU2NX4cwc9Tb3s2e+uuhnx0DMMwbEjA2dw1VHL12\\u002fd+f3rPRXSgH2W5ta\\u002fe\\u002fOXUpWMe\\u002f+wWLp+DDg9vdyROyfKPevLcpgalBB1zCR82\\u002f8NxqwYoySV7B6Mhd0vxDvljxl0PnP7NE5YRZPlHvzf22mCeFzpgZnxA7I\\u002flJ9EoNuTgmekoOJu73oqo0VeFpnxefGhD1dlB8bQzCHJOaMo1UZnoADagX55zYu91R7FccFcZ5DyGEI1iGOZ5zrZXP3N+S5OqCx0YcsplrX9Un7shbgI6gDkTBUYCSDTqDhwG8\\u002f1xKycExryXv0+u16DDGIZhgxnO5q6hkqufX\\u002f1p4dkKdMCSY9uzJe2y\\u002f\\u002f32AK65I0b7RW6ede\\u002f\\u002fcv\\u002fZUZ+HjjnZvLC0\\u002f45cEMwdqCTnUfum9gjkin6ZdttnRYd+KD\\u002fhrtWvXAbrwdRZt8RPIgm0Dw9ibEA0gyDd9XNiroRnpv1wNncXEYv7zIgFq2PGvl+w71BzKQUe2t8\\u002fiCu6NnrM6tisgZMX1qe0GI87iX4lywX3Nwpv8mUHh\\u002fPiw3gJ4byEMF48l8FHD8IwzN2+LDn8efEhm7eLYZGMaIF\\u002fnCggiCcK4AhFLC6bZLBIxpUdMC3SU0YDZTRSRpVBp9LrlHqtRKsUa5UdGnm9QiLWKByS874rPb4qJotNWn4Rwxzl4+LlQdy4EF5SMDcphJvkx4m0YXo40OqYsTNCkt68uHtfY5FDJhWGYZjb4WzuMute2WxebScZ5IhJiaOnp0QmhvgGe\\u002fGFXKORUis1HU2S2pKmnMPFhWcrKGP3r5x\\u002fsuz7V7f85601PXfHTEQs7jtjr10bN\\u002f7d\\u002fL0XXLLJebTQ\\u002f\\u002fmRi6YExaMDV4gXBbJIhs7zFjyySeZj6VfNC0t9JfefAkkjOuxk88LSnh4xP5TnjQ70RcjkpPqE5nvAqmfMBfDMtAfO5m4XJwr4fOLaClnbzxWnttXlagx69Ag3ETI5c8PSlkSOmBAQa\\u002fFkEjaoWfX30Klt6dS25HedAAACCD9OSDgvIYyXEM6LD+PFs0kuegcMw1xIbzQ8m\\u002fP3jjprL4g2l+YTOi8sbax\\u002f9AjfcNdcBaY26MqlrUVdzec6ak61VbWpZegR1mlRS3fU5V0Tja+9ch09pWlUFTWqikxfskhuEDchhJsYwk0K5iX5scOB\\u002fltAOwVzvT4av7qkq\\u002fnLkiO47I5h2KCGs7krVRc17Pz5aM+XU5Zk3vXqyuBIf7NDernhySWNVW3fvvjnqd3d\\u002f0A7fji8+Nbp0SlhvQ\\u002fEAAAy\\u002fSI3TL9jT0PB16VHi522z3msMODG+AnXRmdaWeJhkow4UaDH7rue4Rv+58y79zUWfVp0sFzWig47GoMgZ4cm35s8PdU7FB0b0ISAGI8qa2LOhmcmXTibe5R4UeAro5c+kjbn96pz2+pyq+Ud6BGuEszzmhGcOCMkaXJgPIdhVdrCBjva\\u002f8wUUB2apg5N00XJUQAggAjghIfx4sN5CeH8hFBuLIt01sWVxxpqbtzxJwDE+fj9e93t6LBNvsk9+\\u002fH5k0F8wTfzlyf69vsm234GLUPTyQMAkmnk+ivR4SHNGf9wLqZTsHVyNhoF4PqpSJabl8kYKOMT5zbtbSxEBwbEJplXR426JX5SnMhZm1v2h8tgZfiGZ\\u002fiGr4rJooA60169sfKsbaXSv+tyPTCpD2ExgrFtmgqFvtP0pc6oblDmNyi7G6mzSV4wNzGYmxjCSwrhJvmwQ11Wf0\\u002f2Dvlo\\u002fOoGpeTv2tyvduZo5JfnEiHUk3GO3zMnkCsa6Rs+yi9ipG9EON8HHXYJSsY01vIJoZ6MdkNOWb1uY27D5ZrFpNioH2+61mwcwzB6cDZHo052aPPZnuXqi2+b8cA7a3uP9yEsNvDFn+\\u002f77MkNO386CgCUkTq46cytzy1Hj\\u002fNI92Y9XXmxBgD+t\\u002fWpCYvHoMPOMT88fX54+sXO+t+rzu9qyFcbdOgRNiEJYmJg3E1xE6aHJNJd7JnqHeLigjvdZ35uWOpVYSmn26r+rM7e31REdz0+s0kr2CvhFCmZLTpSZaTYhFHI0IWxtUk86eruV4l4UeDC8IxrozORpth\\u002ffrDj19c2+QV7v7z5iejUfvc\\u002fnBAQu67sOBrFPM+Xh09\\u002f\\u002fO8JNArw+11rRkXQq2WD3TPTGv3NTCt5yMzE2RyNegZfNv\\u002fe5On3Jk8vk7bubSzc11hUKm1BD3ICP45gtF9kln\\u002fU5MC4ZI9sa4Y5FUFZusjlhbwVaKh\\u002fJEEGciJMzWfCefEhvFgmwUIPspUz6rbp33+i0GkB4PYRWS9OnoUOO44zfvjBYgj87p9mn3r\\u002f7DE0CrBl+Q2ZwbTfrzjWyxd2\\u002fFF9Do0OaGZI0n9HLIwU+KID7pMjrnvy3KZGmrtskQRxYP6juOWZiyn1kjZNZau6olVT2aauFGvrjFQf77k5DGEwNyGEmxTMTUz2mo4OO82Gc7mnamuru8Q17V1qlcEhBXcOgxkj8I8VBcSJApK8gkf4hlt5fatTvb3vyPcnzzNJsuD5h9Ex59twLre4ua1TpSpqbqvr7MIFdwyzE87mLs7mD131RlluLQAERfh9e+oVNsfaDyxaje6uiS+11osBIGFk1KcHnkWP8Eh0y74Op9BrTrVVnWitONFWWWPTAkMvFm9KUPyMkMRpwYm+7EHT4NSeZ16mUx9vrTjaUna8taLVimWn\\u002fCNS36+aCF0fn+6pVGHqt3MnBMZOCYqPEfa9yOxqv9tUMhUAXPPQonvfvxkdxgYbxxbczdGdmQMI4opG+kYMPDMHF5zNXZzNbdaglOSI63LF9bmd9SVdzY46h+TPESZ7Byd5BSV7h4zwCXf9GRTMo9Be4T4wI2VsUde2qGtzOv8FAJJgBHOiwvjdzWdCuDEMwsGP6Cgs0iN2AsQ80+0jshbFJYnVqk61qkOlfO3kIdN5Grf7qyabVkZnkoynM+Z5zn4mPTL9ItdPv\\u002f3moz\\u002fUKbpXT1vDSFFHmstWxWShA5gz8Zk+0cwx0YLuD40GStehqWnVVLapqzo0NWJtnVTXBkBpDPJaxYVaxQUAcGXB\\u002ffqxo64fOwoAPj188rPDp0b7Rd49YXSZtLVJ1dWmlrepZR0ahdqg0xoNGqPeYDSyGQw2yeQyWBySyWEwvdm8AI4wkCsM5IoCOMIgniha4B\\u002fO97Gtv16LTP71sTP3TZsQKBSgY3abEhf9R3be3JQEdMAlTE8yXHqeew9iGEYPzuauz+btjRLTjWlXZ1lfbQcANoc1Y8XYPz\\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\\u002fG2kDE3qqiZ11XnYBwAMghnMjTYtfg\\u002fjJwRzokjCzen8kbGTPzp3ItLL++Z0D734BfMEAhYr3scv\\u002ftKX75895gkF93pl55sXd6PR\\u002frFIxkfjV88KSUYHPIOpGfeaw9\\u002fROr2c3VE7WJL6UMUgWEHchCBuAlxa863Ud1bIz1TIT1XJzxoox1w\\u002fbjMugzU7NGV2aAo64BKHyqrWn81dmzXKGQX3qfHR55++H41iGDbY4GwO7sjmsq7uZlxRybRXekYldd9FIXVDR68hIITnHcLznh6ciA5g\\u002fQvkigK5ogGetG+fWf+nvhQAwuJDPjz8im9wr0vxLF7UDgA3vXDtL\\u002f\\u002fbFBoTuOy++ejYcNUmU7ywbf+h0kok3qUydKnUZa0d+4srACAlJHDLvTfatDbDifhsVlyAX8+XH\\u002f97wlEFd3MWZ+bwgbM5uCObOwSbZKZ6h9LdNgDDLLJccJ8TfH2zuqpJVd2pbbahkZM5A6VvVFU0qirOAgAAk2CF8GLDefGmLVgDORGky5ct3DVy7F0jx6JRDBsM\\u002fpe7U2Wg8bbpf5nLPDajm6R6h66OyVpfeQYd6F+2uBYNYS6nM6qb1aXNqpJmdWmLukyibUKPGK6OV9agIQzDsN5wNgd3ZHOfAFF7YycAcHl9bNIzMDa3e0W8l9+QWpuJDWoFx0tMN659ZBFSbQcAwopi8MpHl6x8dAkaHcY6FMrr1\\u002f1e19ll+pIgIMRL5M3j6gyGDoVSolT3HDkiPNiKJxgb4nA2B3dkcwzzZJYL7jODVpluaI3qZnV1s6qqSV3VpKpq1dTqjDReUK6kp3T1ytJ6ZanpSxbJCeXGmha\\u002fh\\u002fMSAjjhdLfBwbDh41xHzdGWMjTav5XRY5ZFdreA8GS3JUzeUHnW+nN79QqJxqB31Dbfr7z+d05uzbqvbvd3\\u002fkdoVz6WE1AdmromVXGjqqhJXdyurqbAiBzhzQoJ5aWE8VJCee5ZXe52OoPhdFUdGsUwDDODs7mJY7O5NYIi\\u002fEwF964OOTpmSWeb1HQjOGoodBzGhob2RrHpRtzI6N4jmI3e2XPEVG3ns1mPXzV16cgULx63Z7RDoTxX03CkrGpvYfnKMSMu3w0blnA2N3F9NscwT0bjL4FNcqP4KVH87tIJBVS7pqFJVdWsrm5SVzWrquR6Sa870KQzamqVxbXKYugAAGCT3DBefDgv3rQFqx8Hb+mLYZd9WXwYDfXPl81\\u002fImMuGvVIYXyfkX7hueJ6dKAfFFANSomjdiOhKIrLYdnWrZsuVz6Wo1TKzzSqippUxc2qEo0R3YmUTfJDeMndRXZuCp\\u002fpgxxgj9LW9u9Pnj9dXdcmV\\u002fJYzOTggBWj0leMSrP\\u002fCaT1nQ1G487C0p35JflNrRKlis1k+Av4KcGBs5LiVoxKMx1TI5Z8fexMUXNbWVuHzmAAgCVf\\u002fWz+Te6aMu6JOVPNI6vXbcxtaD71xL3ePO6Gc7mbLxRWd3RSQIV7e81PS3xwRq\\u002fGow\\u002f9uWNP0eU39ImB\\u002fjvuG2hnM2t+ZpOLDc3b84vP1TTUiCVqvV7EYccH+i9KT1qTNZKJtznBMOfA2dzEsdncGpMWjio8UwEAJTnVS9FBCwpOVZhuTJg3svcIhrmNRqkx3eAKOL1HMFsoNNpdBd3rAj9avWR6YkyvYQB\\u002fAX9+WuL8tMSXFs9hM93cJhdzO5zNTVyfzbFh68M\\u002fj5hu3L5wvLfw8tlQaxzPr27qkALApLTo8ED0mjAHolFwRxBABHIiAjkRI2GaKSLXS0zr35vV1U2qqg5to5FC1zxaT2tUVysKqhUFpi+5DP4M7zd6H4Jhw1SdQnyqrQqN9u\\u002fm+IlerEGzfcdY\\u002f2jrkzoANCg7HZXUX35+ORpyGlc+lqNsrnvB\\u002fEsGwQrkxoZwk0K4ySG8ZH9OlJMuS\\u002fo9O++Vfw4YKIpBksEiQadSdbam4WxNw86Cki\\u002fXXM1m2P4hh9Z3blcoH\\u002fh9W059d8McDpOp0upqNJIasYTNYPQUr8VKVV1nl5DDHhkecr62AQAywoL5rMs78kX69p3UG7ukz27f929JBQBwmUy1Xl\\u002fW1pEqDkIOm5ua4MfniVWqijZxeVsHMoqw8mcGgNyG5tXrNppu81isYJGwXa44X9twvrbhaHn112uX9xyJYZij4GxuzoHZ3BqzVk749Z0dGpX21K5cWadC5GvtThut9eLTey4CAJvDmrVyPDrsqazpKII5g2ueebVCYzDY\\u002frkbu1J1R6dWbwCAQJHgymq7OVxtx3A2N+fibI4NW7\\u002fuPW+6sXLGSLoF931nS7efKACAB6+ZeuvCceiw49hecL+SkOmTIMpMEGWavtRTuhZ1TZOqqlld1ayublHXqg3oikjrqQ2XdyViEAQAGChqS2nBtoriMnFHu0rJZTIjRd6Tw6NuyciMFPVdzgCA7eXFDx7YgUYBPpy9aEVir7V+AzhaX7O5tOBCa1OrUq7Q9bsl4G9Lr5sYFokE7fnhHaK8s2NTacHppvrqrk6ZVksQIGJzIkXeaf6Bk8OjZkXFC8wKQ31S6XV\\u002flxcfb6jJb2sRq1VyrYbLZAbxhXE+fmOCw2ZHxaX6B6L3AQBH\\u002fO6NcunvxXlH6qtrpV1dGrUPhxvj7Ts7Km5N6kg\\u002fruWkZefdPce2uovWX9jFIhlrYp34IuJwdLcrkeout1DEXCNRNGWkz8JoQSZJODKJ9OlUVd3L\\u002fxxgkuRLC2ddOzrdtNT6eGXN01v3HKuoeXff0ecWzETvYx1a39lIUff\\u002fvu1CfZOAzX5k1uSF6UmBQoHBaKyXSA+XV2VFhvUcmRkR+sstqwBAqdVlvvUZALy1bH5ikOW2Ay\\u002fs2N\\u002fUJXtn+YI5yfFCDlup1Z2pqffjoy9NSzNSlmakAMCfOfnPb9+HjJqz\\u002fmcGgFHhIctHpqWFBs5LTQz1EgGAzmD46XTOu\\u002fuPHiqrOlNTPz46wvx4DMPsh7O5ORdnc\\u002f8Q77teXfnZkxsUUtXHj\\u002f367Hd3kQzLl\\u002fKoFJq371mn1egA4ManlgRFXN6Q0BNk78\\u002fb\\u002fePBwpOlnS1dfC9eZHLYzNWTF94+i8VhsTgW8nVTVeuJv89ePFJYlVcnaevSafQ8ETcoMiApK27WmimZszPQOwD8\\u002fcWezx\\u002f+ISY98psL73Y0dn7x2I\\u002fn913ki3hTV4y\\u002f7X9reEKu6ZgtH+9sbxSHJ4Re++jieTfPQL8LANj06Ijs\\u002fXn7fj1SfLpM3CxRyfudS+\\u002fuf3HUjD4+8WnVuv2\\u002fHjm5\\u002fVxlXq2kVcrmsgIj\\u002fDNnZyy8Y3ZMOvpp7kr2PPO2qcqv3fzRzvZGcXu9uL1RrLi0CTAA3Df2GbMDAQAiksK+L\\u002fgACQLAod9PvHHjJ2gU4Okf759zQ\\u002feKugFseGPzjy\\u002f9AQC\\u002f1X3lF+IDAFX5tf98sz\\u002f3UGFbQwdlpHyDfeJGRo2dP2rBrbP6+\\u002fuy85l3HpVOb7rhxXXzFQMnKmq25hbl1je1yRRKbb+Vh59vWzU+xsFv1eo6u\\u002fYXlZ+tbihtbe9QKLV6g4DDDvMWZYQFLx6RPCkuCr3DFSraxFsvFJ6tqa\\u002fpkMg1WoIAEYcT4euVHBI4MTZyZlIcnz1Q5cHOu7sMzubmXJzNMcwGwb7dTX1L6tp6jziYU94BmDAJVjgvIZyX0BOR6Npa1bVtmvo2TUObuq5D26jQd\\u002fdApEXIZrco5Hfv\\u002fTu39fLWfDqtobCjtbCj9ZeCC2\\u002fPmL88MdXsHg6j1OsePvDPvupydOAKBACjr2vw3fjDGyjqzVOHv887b+y9T32HStmhUl5obdpQdFHAYh29\\u002fu4Bqs+\\u002fFee9c\\u002fqIWK0yDyp0uqquzqquzgM1Fd\\u002fnnT9947199h+w53enAD45f\\u002fLznFNag6En2K5StquU55obvrxw5vlJM69LGWF2j17svLunOdzcfYWjNSYFxnmz+\\u002f0H9UBhfAvnXRAqQ99vPcWdimvXfPbx+zf8vT37+MnysWNiXnx22Z+bz234\\u002fVRQoOi\\u002fTy5OSuxuVPXvoaL\\u002fvbmt547bNz0iFPbx3nrfgYJt\\u002f1yoq+9QqXS+Pvz4+KB5czJmTOu1143FYyw+1l9bzn3+1YEtvz+470DBtp0XWlq6AvyFWZkxd942w9v78r8jRVH\\u002f7Mrd9s+FhoZOparXdhoH9zxt\\u002fqUzlMmOl8mOC5i+QdyEYG5CEDchlJsiYjllLcN7B44aKerR2VOuM+uPOSUu+pl5Mx7fvHPj+YsPzZok4vTx72URre+8p6jsQn0TSRDrbliRealUzSDJaD+fm8d3n2a2U0lL+5a7b0gK6n4a+WzWzMTY3ofQQ\\u002fdnfnv5fPMvWQzGnZPH7i4szWtsuVDfhAvuGOZwOJub6y+bO8\\u002fiW6e31Lb\\u002f+ene4ztyHlv0zs3\\u002fvXr0tOT+yoJqpebkrtyf39zWXNMOAHNWT7j2fg9qCKDXGT64+6v9vx7tiXS16brapPnHird\\u002ftfeVzU+I+t8wpqWm7cN7v8nen4fE5Z0Keaei8mLN7h8OTlyS9dyGhzl9bTBbX9akkqv\\u002fu+iN6oI6AFBKVVs\\u002f291a2\\u002f7ypifWv775p5f\\u002fMB1WlV\\u002f73h1fEgBze9fc7Xx0AFArNG\\u002fd\\u002fOmJbefQgSsQBMFg9vHve+Fgwft3fdlS094T0Wl0ii5ldUHd1s93r3hw4d1v39jfxLDnmbdHfUnTnp8OoVE3ETd1+oX4\\u002fPK\\u002fv359bRNlvPwxUyVvbqxoLsuuWnTHHLPDL7PnmXe2IFH3VS+14i65Rivk9D39nEql1T2xadeB4gp04AoEAQzSkddSNEikL2zbf6KiBolLVWqpSl3c3PZXdv6s5LgPVy3msvouKBmM1Hv7jv50MhutPOiVHQplbn3zH+fy+GzWgUfv8L1icQnYfXcXw9ncnOuzOYbR5efFN92obLRwvbid+n59dBIfVqAPKzBJlNUTURuUHdrGDk2TWNsk1jZ3alvF2maZTjzwGUISiLv3bM1tayYAMgKDE339WSSjUiI+19xAAWgM+icO7Yrz8R0Z2Efb93mxCQfX3NGpVonVqk616vOcU9VdEvSg\\u002fj1yqdouYLFvHzEmzT+Iw2RWSsQ\\u002f5efUyboAgCSI92ctTA8Iivby6XOzCHt+eDu9d+bYdxe734xGirzTA4J8uTytwVAvkxZ2tMq0GgDIDAoboNr+4rEDPxfk9HwpZLPjvP0ELHarUt4klyn1OgBYmdS9UPRK9vzuTx7a\\u002fVdJvul2EF8wNiTcm8MVq1VnmxrEaqVMq3n68J4WpfyhMZN636+bnXf3KDKdukBy+YyFRdODE9GQZ\\u002fNhd7\\u002f8WUml71VuRnzx9YGIcL\\u002fMUVHHT5a99d7OyqrWZYtHb9mW\\u002fd5Hu7\\u002f5\\u002fFbTMePGxn7wztquLuXP609UVfd9kvO3P05\\u002fve5QUmLIkkWjCYDGJsm57OroyADzYro1x1jzWADwyut\\u002fF5c2zZiWPGt6Sm5e3Y5duVXV7Z99dGPPAV99e+iPTWeump12\\u002fXUTpTLVxt9PN7d03Xf3rAnj4sy+jYNdH\\u002fNhg7KwUVXYoCpU6jsV+s4q+dkq+VnTqIgVGMZLDeelhfHSg7hxDln83iSV5TW2AMDijF4nNgBgXHQ4AOgMhtz65qnx0cioRXS\\u002f896icgCYlhDTU7l2uNnJcT3VdodwyM8c4+eb19giU2vQAQzD7IOzOWLgbO4kV989p71RcnDTmZLs6udWfezlJ0gYFR0eFyTyEXB4LL3eoFZqu9pkdeXNFRfrTAvbASB1XNyMFeMObjorkyh0ah3Vux6EWP3wAjTkBO\\u002fd8cW\\u002fG4+bbvuH+Y6YlsoTcNvqO\\u002fKOFdcU1j89\\u002f\\u002fWgqH7zi0+gV3lOtek2h8+JHxUdFh\\u002fM5rLFzZLcw4UqmQoATu04\\u002f9mD3z\\u002f+3b3mdzTRa\\u002fVfPf5zY0XzjFWTtGrtye3nAeDEtnMHfzv+62ubAsL9xswZUZ5TVZlXCwA\\u002fv\\u002fonUnC389EB4K1bPjNV23ki3ooHF8aPimZz2fWljX9\\u002fvqe5uhUACJJ46vv\\u002fJGTGhsYFs7noetgT2869tvYjvVZv+g4jpqYEhPmpVZri0+WNFc2Ukdr88U5xs+TZXx9C7mhizzNvj\\u002fjR0fe+32sHlx9f\\u002fF2t0ADAqseW+If3uvZCdGkpH2Ly1eN+KPqoq10qbZd1dcg2vrm1saIZPcgKHU2Sk9v\\u002f+uV\\u002ffwEAV8CJTo3gCjjtDeKWmja9zjBhUR\\u002fn+MHuZ97Zovx8QryEzVK5zmD45N8Tzy6ciR7hfE9eqrYLOOybJ2amhgSymcyqdvH6M7n1nd2Vh7dWzE8LDYr08+YwHfDGu4e\\u002fgF\\u002fY1GK6zWUxU0OCovy8OSxmm0xxprpeodECwMGSylf\\u002f+feN5fN63fOSj\\u002f89\\u002fsOJ7oYPEb7eqSGBvnye1mBokEiLm9tMbyxHR4T2Vy638+6uhLM5wi3Z\\u002fEpfFB39qOAQGgX4Y\\u002fbto\\u002f3C0agl60pPflZ4JJAr\\u002fGLy6gSvvts5mBxsKrvn+G8AkOIdvG3u3egw5hm8BVzTjS6Fcy\\u002fIcOTrsg24DH7PKniNUdWmrm\\u002fX1Dera8537jPvIYM439IIAAm+\\u002fp\\u002fMWZzmf7m\\u002fbW5b8+27NneolHqj8ZPzJ79bsOLyfS7hMJix3r6x3r6mL\\u002f8qKbC+4H66qW5vdTkA8Jms3atu6el\\u002fMjsq7sa00dds3VDY0WqkqHqZdIDuNPb88PZQ6LQ\\u002f5J8HAAZBfDxn8ZL4FPNRA0XltjbtqCieFhFjHjf3S8GFnmr7qKDQp8ZPmxgWaeoSAwBGijrf0ri3qmxNar\\u002f7R9n8u\\u002f9ScMFULmeS5KtT5qxJHdmzn6HeaPwxP\\u002fvNU4cNFPXB2eNp\\u002fkFXRcf3urPdd\\u002fc0BZLGgc9IIUb5RaAhz8ZjoJ+FBsYg+j7BY8JkMp5\\u002fZqlOZ7h61ScHDxdt+One0BBvuVz9z+5cjUbP4TABQCTkZo6KAoCdey72VwTf92+Btzfvi49vYlxaaGMwGHW6yxdMWHmMNY8FAAVFDV99ektcbHc6f\\u002fq5P8+cqywoakhPDQcArVa\\u002fdXt2YkLwc08vNR0QGx340OPr2zvk0c75XGcSxksL43W\\u002fuEm0TQ2qgkZVYYOysENTTQEl07WV6NpKpEcAgElwQniJYby0cF5aKD+Nz6C3MqJHcXP3UzT9w297j1wmVvabLAZA9zuXtbYDANKGxbHSQi6\\u002fKjoE3Z9ZpdP9k19yorK2ol3cqVQptFqN3mDa95XGKw6GYdbB2RwxcDZ3uEcWvF1X2qSU9fqgJRUrsg8WZh8sNA9eqehs5YtrP0Oj\\u002fXBBwf3EtnM9Nd81Ty+\\u002f9ZXVPYuCpR2yj+779tiWMy01\\u002fb7l4PA51zy8qCK3ev4tMzNnZ7A4l\\u002f\\u002fhFF3Kd2773FRD3\\u002fvL4VteXh3QVxed3T8cfGffC6ZWLeue3fD7u9sA4O1bPw9PCPnk+GsCb75eq39g0nOVF2taatpriuqjUy9PZjsf\\u002feKRwhN\\u002fnwUAroDzdfbbITHdmXTCoswl98x9ZNqLFbnVlJFqqWnrs0dKS03bO7d9bqr5Lrl77p1vXs\\u002f3uly\\u002fO7D+6If3fqNV6w79fmLUjLTFd111+Z4AYPczb4+w+JBrHlpkHtnwxhZTwX329VPjR8WYD\\u002fWHzWWFJ4SEJ3Qvddr38xHbCu57fzp0bMuZgHC\\u002fu9++ceo1E5is7p7mSqnq5PZz0Wl9vHbZ+cy7xi2Txry95wgA\\u002fHwqR6xUPTl3WrBX36cunOFsdf3+4goA4LFZf993Y8SlHYBmJsWuHTdq7Xe\\u002fFTW3GSmqQSJdNiq11z0dgcti3jppTFFT2zVj0ifFRpn3qZepNU9v3mPac2jrhcKHZk8OueJpUWi0P5\\u002fKAQAGSbx77cJFvVe3GIxUXkPzzvySKQl9r5ix8+4uhrM5wsXZ3DU+Kzyi0GsVcvEfVTnPjur7JBM2iHDY3ZVw6VAtuFNAdWqbG1WVzerqFnVNs7qmS9tm\\u002fUuVN4e7YcnqIL7APDgqMOTFybMePvAPABypr9YZDSzSkXuY7K4sM924KX000m2cy2Q+OnbKXXu2AMDuqtIHx0w0H0W45Ycvl4jVej0ApAcEI9V2AGAQxJjgsDHB\\u002fdZl5FrtO2e6L5acHRX39fyrkR+PJIhxIeHjQiycLbThd1fodO9eeujXps1d07vxC5Mk7xw5liSIV08cBICXjh+YGRlrvsTezrt7oOKu7uUG1iCASBA5uIrnbFpjrwq1RXwmGw2ZSU4KAQAWixEa4i2Xa0JDvAEgKMiLokAmU3E4IvQO\\u002ffDzFdTWdRQWNY7I6H6TxGCQPYV164+x0qwZqT3VdgCYPDHhzLnK+vpOU8G9o0Ou1eoTE4J7DoiPDwKAxkZJT8TZfNihPuzQdO+rAEBrVDaqCptVpa3qilZNhUTbrKc09cr8emW+6eAnUvf0urPV5BotAJAEEe3ng45dwrO07USf6H5nmUYDAN687pPhzuDwNpS0fubsusYH\\u002f9jerlCSBJEcHDA6ItSLy+GxWEcqqqs7OtGjMQyzG87miIGzucOVnK9CQ4PWxre2mm5MXznx9tfWmA95+Yue2\\u002fDw\\u002feP\\u002fa1pg3p\\u002fr\\u002f4sudjERePP\\u002f+8tDN8Y\\u002fIO2QUUbqwqGCq27so2ydOiGxpzH64ruvMhXcjQbjdU9dLfDmAwCTzZx+7YTKizUAUFvUYF5wB\\u002fse\\u002fdiWM6YbS++d11NtN+Hw2De\\u002ftOqla941HXb9s9eYj5p8\\u002f\\u002fxvSqkKAKavnPjQ53cgo3NumNbVIfvq8Z8B4NfXNs2\\u002fZSbz0kd0E\\u002fuf+aHh6ObTAeF+n518wy\\u002fUxzzO9+L1eZ4D7H7mXeOWSWNOVdYdLqsCgB0Xi\\u002fcWli0ekbJqTEZmZJgLtsI1XacIANePG9VTbTfhspgPzp70nw3bAGBfUfl9MyaYjzrKvdP7\\u002frYiLuf9lQtnffidRKk2UtTpqrqrr6j4V7Z3qnV6AEgNCULK5QDAIInRkaGjI0OReA877+5iOJsjXJzN+3Nr4oQFEamdGlWnVinWKN7M3adwxNJ7D68UYVbSaPWmGwajc\\u002fcbd2nqkmhb61Vl9cqyBlV5s7pqgDXsFt0xMgsp2posjE16nNylNxq1BkOlpDPZz5GLPSu6xKYbqWars3ukXdoptEpioTbhlh+edemloUkh0xj0fba7GcCfpfmmnjMCFvv9WQttPhlgw+++paxAqtUAQIKvf39t1m\\u002fJGLMu73yDTNogk+6uKjU\\u002fo2Dn3T1Qo0qChvrnzxXQ\\u002fbd2O4Veg4YGJGBy0JAZ0aUdqzkcFnmpuSGbzQQAvYHGy+ttN0978r+\\u002fP\\u002fT4+sxRUQvmj5wxNdm0Op7uMVYyL6YDgEjEBQDZpU3AfH0FTCbZ0Hj5paahoRMA\\u002fJ3TJ9QiNsmPEYyNEYw1fak2yCvlp8tlJyrkpw2Urvex9AjYLAAgAHb+55aeC1Mcgu535rPZcKlMP1hY\\u002fzOrdPoH\\u002ftjeoVCOj4n44JpFgcLLr9LNf27HBXcMcwaczREDZ3OsP42VLSVnLxXm+qopM5iMlY8tfee2z9EB63AFnHELRh9YfxQA2uo70GEAAEgaG9dzOyQmiCfimVrBjJx+uQwXHN39QamzpasnaJHFR68v7e7kED+yj7WucZeC9WV9NHwQN0uO\\u002fHXKdPv219f2Huy2+M45plYtHY2d2Qfyxi\\u002fM7Bly9jM\\u002fuNz3\\u002fi1ItX0Adj7zdH126NRnh7ofjhaSID5fu+ztvUd+PZ1DUaDVG7bkFGzJKYjw9V4xOu2azPRQb2sX7tigqr278pAScnkJTo+U4EuVB3e8SeOxWdMTY7flFgFAc5cMHQZgXlpv1CyVa\\u002fR6uu1u7Ly7i+FsjvCQbM5nsuNEAXDpb\\u002fSjgkP2FNwfTJvxaeHhCIHvDfHj0DFsECpraDfdEPGdO12d+9euMSrrlWV1ytJ6ZWm9qtS2LVL7tCg2CQ0BAACbwQgWCBtkUgDo0jj46gDdpfIcr68Xfe6loGHATo7gph8+yTfAj8sXq5VtSsXtuza\\u002fNm1uT18daxyvrzHduDohxbf\\u002fJu8W2fC7H7300Itik\\u002fqrijEIYmFskqlD\\u002faG6avOKuZ1390Ctqj7e1vTHn+OeCqw9OjQKNDSggc+imy8wJ+04I52WGvbzurs2\\u002fHFqz778nNwdn325\\u002f\\u002frVE69bNZ4wq9Vac4yVfH34aMgMl8tavHDU39tzPv\\u002fqwKQJCVKZ6uf1Jzgc5tVLbf80YjOdUd2uqWnXVHVoasXaug5NrVTXSgGNkxkDSAoOAAADRRU1t6WH9nGm02Z0v3N8gF9luzinvhEd8GDW\\u002f8znauo7FEoAeGf5AvNqOwC0yW0\\u002fNY5h2ABwNkcMnM0dbtVD89HQ4FR0stR0IzDSP25EVO\\u002fBbllz++33aI2gSH\\u002fTDVMZ\\u002fUqBkZcXygCAl59AJVORDDI46nKhkCfq\\u002fvig6b3Tu0UDP7ru0iI1Tl+fmXv2WTX2tcbiwsECg94AALEZUWFxvRY69ODwOfGjYgpOlABA3tEi87KvC575wULoK5i6Yjwa7Z+dz7wrMRnkcwtnLh+V+tmhU4dLq0wbeNZ3dn168OQXh08vHZnywMyJyPJzR+mpPPS5KynnUtBotkutK\\u002fWcbFBo+\\u002fiLTgry9xPwxApVu1xx7\\u002fq\\u002fX1oyO8afRuXBzru7GM7mCBdnc9e4PWni7UkT0Sg2OEmV6h0nCk23wwOc8gLeo4+Xb3tQQLWoa+qVZXXKknplaZum3vouMX1iEIxgbkwkPymSnyy79JfOYTDjfPx6HWem55wh3YtfLIr08j7ZCABQIek+4WyuJxgh8uo90ou7fngmST43acbjB3cBwPGG2jm\\u002ffz8jMnZNyojZ0XHWLFfPbWs23ZgY1vcbSmvY9rvntXVfpZUeMFBRLCOg++1aTkuv6pKdd\\u002fdAcjonmbmD7RQ6AJRKu\\u002f\\u002fJrBTEdeLqEnP+\\u002fsIH77vq7ttnHjpSvPGPU1+vOySWKP5z92y6x1jDYo3+gXvntLXLNm09t3V7tpeIl5IS+vwzS8270DgJBZRE29imqWpXV7Vpqto0VV3apj5f59kkL4ibEMJNCuYlomNWi\\u002fDxTg8NLmhq+fb42Y9WLkaH7UD3O89Jjt9XXH6wtKqgqdWaAj0AcC71uxQrlQDd9QJXsv5nVmp1AEASBFJtrxFLLjZ0v\\u002fhjGOZYOJsjXJbNTW5\\u002fYQUaGpxqCutNN2LSI3uPXOYb7C30Fcg76RVNejAvlfb62x7Wy69X7mAwGAAg8hUQl64sBLMFEKZKq\\u002fUGfvSQmKBcKACAupIGAHThYV1Jg+lGz\\u002fp6cz3r02NH9PvUAUDPwu3Wul5L7F3wzA8WSWPizP+tLbLzmacrUCQIEvVb5WyVydt6qgz9SA8L\\u002fvL6q5u6ZJtzCv7OLaoVSwDAYDRuvVC4u6D0mfkz1oxz\\u002fJmVCF+v01UAZkvdzVW2dy9sD\\u002fcZqPLgPD1Xz\\u002ff1dwkMknxq3vRntuwBgJOVtYs+\\u002fWlaQszKrIyZSbEshuXKg513dzGczREuzuYYZj2D0ZhT2vDBH4fbu7pf9kcnWOiJbScH\\u002fMHL9ZI6ZWm9sqROWdagKtMa7V2a7c0KiOAnmYrsYbx4JsEyxY8pupcq+\\u002fF41nQAcLgFsYl\\u002fFOcBwI\\u002f52Temj\\u002fZi91pJ8Wl296Vqc2MSzOMId\\u002f3wAHBtUjqXyXzp2IF2ldJIUQdrKw\\u002fWVvpwuCsS025IG5XgO1A9qFPdvagEaV5Pi22\\u002fe4eqe31lEL\\u002fft0oA0NOppud45Evb7u6B1AYaPToG46YlRXT2eScJIlrY71kcZ+BwmPPnZsycnnLrXd\\u002ft3H2xz2K6NcfYKTev7tTpiuefWTZ7Zio65jS\\u002fVj\\u002fYoanRGft+W2mqsAdzE0O4icG8JD92OADtv\\u002fcr\\u002fXfejFt++WtXYSlzC3nP1PGJgf4AIFGp6zq7\\u002fi2tYJLk\\u002fdNtXG5A6zsvHZHyw6nzJS3tt\\u002f266Yk5U+enJnrzuAaKapMpztU2dCpVN40f3XOwCYMk4wL8KtvF3544lx4aLOR0L\\u002ffQGQyu+bRg\\u002fc9sWu9vpKgNZ3NvntC9iOxkVe2LOw70WeDAMMx+OJubc302HzKkYrnphm\\u002fQQG\\u002fRvfxEA5d9KYoqOVuRfSCvKr+2taZdJpYrZSqtWqdVa3Wa7lXk\\u002fWH1tQcJrabbNj\\u002f61BXj9\\u002fx4EAC2fr57yT1zhT69Sv8b3txiujFp6VjzuIm4RWK68e\\u002fG4z17nw5AdumpNnHUMz8EeAfSq\\u002fna+czTdV3WiAdmTUKjl3x28KSVDWdCvUX3z5x4\\u002f8yJp6vqfjmVc6CkgqJArdO\\u002fvOMAADi85j43NXFTdgEA\\u002fHL6wppxo7y4vSoPXx0+bboxOyXOPO5YFAUXG5pPVtaUtnQ0SKRdKrVco9Xq9Rq9QWsY6A8TAJaPTuOymP\\u002fbebBDrjRS1OGyqsNlVd487rJRqWvGjowPtPCCb+fdXQlnc3M4m\\u002fewuH4Os96mwxc\\u002f39pvsrj59Y3W\\u002fGFRFCjVWqSr8LIp6eZfOhyNd0I99JSuSVVpahRTpyyR6NrQI2hikZxwXkIEPzGSnxzJSxKxLPyJ8ph9vKtzgVlRceNCws82N7QqFQv+\\u002fOn+zAkp\\u002foE8JrO6S\\u002fJ93vlzzQ0AECwQ3jMKXV5hzl0\\u002fvMniuOSZkXEbinJ\\u002fys+pl3UBgESj\\u002fiE\\u002f+8f87AVxSc9NnBHRVz1dqdfpL20mYGp8bBsbfncDRWkupfOepj194l\\u002fa3lCuu3xdm51390wEnSKmSk\\u002fjHYAnMFDGo63dy16sEc73YZMD\\u002fcs6BEVBS2tXSPDlvw4GgyBJwvwEkjXHOND57BqjkYoI96UoymXpvFnVffW0CYvkBXPjg7mJwdzEEF6SLzuC1uS00rjo8PevWfjfv\\u002fduzyvenlfMJEmCIHSG7vVxS0f0agD198Wi\\u002fMYWqVoj02hKWtoBoKCp9Z6NW0UcjpDLEXHYj8yazLi0JIfWd2aS5Fdrlt\\u002f729aSlvYXdux\\u002fYcd+Houp0RtMFxdPjY++suAOAP+ZNuGJLbuOlldPeu+rYC+hzmAQK1SPzJ5yx6Qs9FDr\\u002fFtScbK6Tq7WyjQaU9fOeon07o1bRRy26Xe8e8q4no9k1v\\u002fMsf6+S0ekbM8rfn3PoR9PZwcI+A0SabtCmRkRel3WiHf3H+1++EtoPc8YhvWJ1gsmzuZYf9TK7rPgPe1T+sTlDzR6bMuZdc9uaCi38ZImsq\\u002f94a1\\u002fc2LPo49fODpjSkr+8WJxk+TerKfXPH113IhoDp\\u002fdWN6y+ZOdpoYk\\u002fmG+q59Yit4TQNlFb5ENsjbfIc\\u002f80EDr5ArY\\u002fcy73YTYyAmxkQWNLU9t3l3RJgaAd\\u002fYeWZSR5GXdNvVWmpEYmxUVfr62oU2muPqLX+6ZNj45JIDLYtWKJT+dzM6ubQSAIJHwzikDVR7ssa+o\\u002fL19R2s6JOiA1RakJ01PjP393MVfT19okEgBoEul\\u002fuVUzq+nc+amJj49f\\u002frAy\\u002fPtvLvL4GxuDmfzHgyrkyBmkUan77q0m92VpMp+hwZ27YyR8eEDLTu2n7V\\u002fDJ3aljplSZ2ytF5V2qSqMlAWTmkOjADCnxMWyU+K4CVF8pOCuTGkNack3I0A+Hr+8tt2bspta26US587ug85INbb95v5y+1pce4CAhbrrpFj7xw59lh99R8l+XuryjUGPQWwq7L0aH31d\\u002fNXTAxDL+7jMpgEdPeMUOnt+qeni0EQPCbLlJnUAz60UtedvYSsy+9r7by7Z6LVFk2sHWRrak62VnZp++jR2Z9YYQAaoslgMBYWNSqVGoVS294uB4CDh4v8\\u002fAR8Picy3DcgQAQARqNx7c1fpSSFJiYGB\\u002fgLFQrt6bOVjU2SW2+a2vN9rDnGmsey0qL5I3bsunDPAz+ZvmSxGCHB3ksXjV55zTjnJXcWyQvixnevYecm+nEiab3FtNnCtKTREaG\\u002fnrlwtKK6rrPLYKTCfbwifLxnJsYuSEsyP3JnQcmhsirziFStNo88MGOieSHY+u8MAGHeor\\u002fuvH5rbuHuwrKi5lapWiPksP0F\\u002fNERoctHpiEHmywdkcJns348nV3Y1NrYJRNy2MnBAfEBFk4qD+BwefVv5y+aR1Q63WGzX3Bt1kjzNVDW\\u002f8xvXz0\\u002fKzLsz5z8GrFErFBF+XnfPCHztklZFW3idwEtuNN9njEMuxLO5ubsz+bDFpfX\\u002fZqvVQ9UxzHoey3pMvfb21u\\u002ff\\u002f43020vf9HY+aPiR8UERwUIfQU8IZfD52z5ZJdpFbkz2PnoBEG89Nfjzy99q+RcRWtt+yf3r0MOiEgMfWnT417+fbzF4gq6y6NJWXEp4xN6D\\u002fYhMiXc\\u002fEv7n\\u002flhy85n3kOkhwX\\u002ffOuqhZ\\u002f+KFVrlFrdwdKqq0c58qpTgoDP1i69+9cteQ0tTV0y0zp6czH+vp+tXerDd2SVv8c3R898sL97PakPnzstISY1JCjMR+TF4wrYLC6L9fOpbNMC\\u002fIHx2azbJmfdOinrRGXN5pyC\\u002fUUVGr2eomBvYdmJipovrr96fEwEeh8zdt7dNXA2Nzdksvk\\u002fdQWPnt6MRgHeHb\\u002f86qgRaLQvpr7NaoP+98rsXfUF1XKxXKfx5woy\\u002fSOvjRk1LTgevYOZL4qOflRwCACOL3k0kCsEgNKu1o2V50+31TSrpEaKCuQKk72DpofEXxuT2Wdlv07Rub+h5Ex7TUlXa4dGoTUYhCx2KM97hF\\u002fYksj0SUGx6B3M2Pbof1blPHd+BwCk+YRsvequy9+uLwv2fFkpaweAd8ZdvTza8uVBY5IiFkxIOVNUK5bSO187gPnjkp9YMxONOprlgvuv1a87ZL9THkNoWsMewUuK4CfyGEL0iMHAj8vbsPS6ZZt\\u002fqZCIOQymgTISQPhwuan+gQtjk1Ykpg28jNpzEADTImKmRcR0qlXrC3O\\u002fzj0r02rkWu19+7YdWXunqHe3HJIgvDlciUYNAI0y6ajAEPNRZwvg8etkXQDQopSjY2Z6RgMuNYfp\\u002ftK+u3sgAZ2k3qKSKvVaWu8D3OuH8hNoaEBj\\u002fG3fVMBELtc89Ph688gHn+wx3bjlximmcjlJkmtWTTh7vmr\\u002fv4Uajc7bmx8V6XfrTVfPnH55EbQ1x1jzWNZobJK89NpWH2\\u002f+0kWjBQIOAChV2gsXar\\u002f45l8Gg7xmuY2rpy16KHmLayrsVwr1Ej151bQnr5qGDvT29drlaMgSK7+zCZvBWD1mxOoxVr3NMpmTHD8neaB3VADwxx1r0VA\\u002fXlk855XFc9DogKz8mRkkuXbsqLVjRyHx1JDAkhcfRYI2PM8YhiFwNjdnfzYftoS+3W9cJe0DfViTdfb9Nrgyr\\u002faHF3833V7+wII73rj+yvXawgE3creHQx7dO0D0zr4XHpj4bF1JI5vLMuiNBEl4+QnjRkZPvWb8nOunXfk9TXwuNUJJHpfwwCe39x60zM5nfjiz85n3HP5C\\u002fszkuG25RQBgWuruWL583k+3rlr59YbKdjGHydQbjSRBePO4KSEB89ISl41M7XM\\u002fVfuVtLR\\u002fdKD7NfymiZmPXzX1ygfy4tIo9BMETImPnhIfLVGqfzuXu+74eZlaI9doH\\u002fp9+76Hbxf17pZzJTvv7mw4m5vD2bwHj8GqlnXce+IPU2XZpEkpbVIW7KwrWBqV8fbYq5mW1ie1qmWBXOGnhUc+LzpiukbZpEYurpGLCyTNq2PHmB0OANCgkDyf\\u002fc\\u002fxlkok3qVVd2nVxV0tf1blzA5N+mjitRZ3FKD16EuiMt6+uF+qUxdKmvM6G0f4hvUMIfI6G03PiZDFmR9h1XnKlKig1+9cCABl9e2ni2pOF9Zml9arL+2aTouPkDcmKXzFtBGTM2LQMSew8BQDQInsHBqyDoNgBnOjI\\u002fhJkbzECH6SPyfMXZUaB1Lr9Tf+80eFRJweEPTDwmt7Gn8PXr5c3gNjJi6JT1m+5VeJRt2pVu2tLr82KR05LCMg+FhDDQCcba5fGJeEjDrV6OBQU8U8r615QWwiOnxJfnuL6QZyPsDOu3sgPw69WXdBXDc5yEK9z0Nkd9SebENzw8Cm9P+r+fkKDu55uufLLz6+qef2dSvHX7dyvOm2tzfP\\u002fLA+EQTcc+fMe+6ciQ6YseYYax5r5YqxK1eMRYKzZ6aa92p\\u002f76Pd4k7Fhh\\u002fv4Zl9hjQYjCvXfn74aLHzCu5D4DUcwzDMQ+Bsbm6AbI4NLDq1e\\u002fFvTUF975HLVDJVZ0sXGgUAgEO\\u002fHaeMFAAkZMbe98EtffaBcV4Lcoc8ukalfWbB63UljQmjY17b9kzPTpsWJY9P2P71PgCoyK1Gx6xg5zM\\u002fnNn5zHsUX373pe2GS\\u002f1XHUit09\\u002f206bKdnFaaNDXNywPFNHLGjb7J6\\u002fYVFxLCw16dsHMvv4uoUtlSw8HHz733ukTFmUkr\\u002fpmY5dKLVGqDxRXLB\\u002fd93WiV7Lz7k6Cs7m5IZPNrwpL3rvg\\u002fk6NslOr7NQovyo+XiOnd1JNbdDfdfy3GrlYyOKMDYgK4orkes3Ztpo2tRwAttfmGyjqownXoHfrrU0l\\u002f7Tx8KeFRwCAx2QliAL5TFaLSlavlOiNxpkhCegdAPy4goLO7rb7PAYr1Sc4SujHYTDbVPLTbdUKvRYA\\u002fm0qfSVn15tj+2i2Zo7Wo\\u002fMYrOXRI38uPwMAv1Vmj8jqt+C+pab7cu0lkek8RndjZyslRgQkRgTcODerS66e\\u002feiXpuDPz10fHtB9HncABBACHpvZVxM857FccLceSZCBnMhwXkI4LyGcnxDCjWEQjvz+nmBd3vmcliYA+GTOkiFQbe8R4+2zLCH154IcAKiVStBhgCkRUaaC+5ayosfHTRNc6njuArMi47aXFwPAzsrSJ8ZP6yvjg4GidleVmW5PjYg2H7Lz7h4oVkivz9ShltJBkdQ1Bv2LF7aj0QH5cQRpPqFodBgoLGoYNSLKvNoOAAwGyWCSckV3R1EMwzDMk+Fs3mPYZnOHSJ3YvZqkubq1trghqq\\u002fmGxcOFfa3A3ZTdavpRsaU5D7r3QCQf7wYDTmIQx5988c7i06XAcB\\u002ff33I+mo7AIyZM4JkkEaDsfhMeWNlS1hcMHrEgOx85oczO595j1LR1mG6EeLl+Mv3fzqZnVvfBADvrVzosmo7ANR3dl+0kRUd3s\\u002ffJZyvbUBDVovy81kyInn9mVwAqOukfUbKzrs7HM7mPYZSNucwmDFCv5hLG8BuqblIt+Ce3VEHAKtiM58dNa\\u002fnMggDRX1WePjzoqMAsLOuYFFE2rzwy1fDX2lTde7ehqJgnuiZkXPnh6f2rIiX6zQHmkoTvAJ6Hw4AwGOwbkucWNTVfG3M6ElBsWyS0TMk02meOrv1QGMpAGypyX04fUYIb6A6Nd1Hvz4+y1Rw\\u002f6euwPy3Nqc3Gv+p6+5GtTIms\\u002fcgDd5Cbqi\\u002fV1OHFAC8+BwfYfe5T09jV0Hc1Iq9p8Ieyo1lkW6+osfZjtZXAwCTJGO8fdGxQU6u667TeXP6mKzXpYz4+PxJtV7fqVb998iej2YvdtJWkFdampD81unDrUpFVVfn+sILN6aNRo8A+Ck\\u002fu0EmBYBAvmBJfLL5kJ1390B0O6Ntr7v4WNpVXJonD13v7fw9lbI2NDqg6cGJw3PNtVDIra3r0Gr1bLNdqg4dKe7okE+a0MeJbgzDMMzT4Gzew+3ZXCVX558qrytrlnUqdBp6Vyjf+cq1aMi1IpLCEjJjy3OqAGD965v\\u002f+8uD6BEAmz\\u002f+Bw1dwuF2fx6WtHWX2BD7fjli23am1nDIo2fvvwgATBYjPIHeVaoB4X4zr5v874ZjRoPx4\\u002fu+fX3Hf5msy4UJi+x85oczO59511BqdXy2hdfbczUNJytrTbenJsT0GnOE4xU1AMBkkNF+Lq08cC41kBErlL1Hum29UGjPZqoAINdoTTe8bdpp1s67OxbO5j3cns09zbiAqNeylpg\\u002fIwyCeDh9Zp1Csq02DwC+Lj4+cMF9T0NRME+0ec6dpl7qPYQszgCt5O9L7btRrYjF+WD8NTN2fizRqowUdaq1euD+6XQfPU4UMDEw5lRbtVKv3VabtzYuCz0C4HBzeadGCQBJXkEj\\u002ffpdBW+NpIhAU8Hdk9lYcE\\u002f3njTOb34EP4lD9lGcHcJMfY70RuPrJw\\u002fePiIrRCCy2HfJQ3yWfapW1rU0Pnl8aASnd7cmI0VtLi3YVt69fmR6X0u8\\u002fbj8BzInvnf2GABsKy\\u002fuUCmfHD9tdFCvE5htSsXJxrpicdtT463qhmwlFsl4ecqc\\u002f+zbBgAvH\\u002f\\u002fXSFE3po3uKfcbKOqn\\u002fJzXTx4yffncxJmm7Sl62Hl3D5TiHUIAQXXvYmtZl1a1sersbQmT0QFP8nXJkd+qzqJRS1bH9PEiPhysumbcV98e\\u002fM\\u002fDv8yaniIUcrukyrz8+vM51YH\\u002fZ+8u49s40gaAz4oZLDMzO04cZuY0DZWZma69e69XvF57ba\\u002fMzJi2adqGmdFJzMxsyZbFvPB+kCOv1rIk25JM8\\u002f\\u002flw+4zK2mlrLWrZ2eeCRbefMOo\\u002fo+GIAiC7ODZ3GEEz+YGrenbl\\u002f\\u002fa\\u002fe0JdEiVQMEoSLgDAK79x5UvXvc2AODIz6cik8JuemYL7fKIaZPe\\u002fOk\\u002fvi86Vk7enix1WtL+b48BAM7uvNhU3hqXGe1oInBiz1eHP3jka0fE53zy6iwuCwCA2rBP\\u002fv7dpkfWBEcF0RneXszf8eJ1Fw8Ua7q0BYdLn1jy77tevSFrjlPPG4vRUnam+tyuS0at6fHP7yU3geF98hPcMD\\u002f5APjf\\u002fuOlbfKVWSmzEmIzwkMoVQiUeuPvhWUfHjuH4QQAYHlGckKw73Pi9srpKIa\\u002fuu\\u002fYLbPzwkWCwMxInx0Ztr2gDABwuKq+VqFMDpU5mnCC+O1S6Yu7j\\u002fRt7crHx8+1qDRrstOmxUWxnae4wwniz8LynSVV9tW5SS4yD8N8eIDBs7nDCJ7NR6dbU2a6vP\\u002fwQMZ8e8K9RNXeqFPGC\\u002fv+xPp7KnclJd89HFwGc2F48p\\u002fNJQCATpPnbPVgX\\u002f26pKlnuxoBAFvrL7lMuP9xuZ7MloTJTg2DlxITfKyojhodZYaYcC\\u002fTnKnSXgjjxEVykyK4CZHcpDBOHAMZ7bfp1Baz0mTUWS06q0VrteisVrlRZ2862dqEE4SQxRayWAImW8hih\\u002fMF\\u002fWdAvSY952hLAwDgy5JLX5ZcIjdxGIwQLj83NPzqtJwFMfHkptHAYLP+UlnyS2UJm85IDZLFiaQiNpsgiG6ToUjRqTD2lke8PjM3Wer6D\\u002f6BvFlVqm57eZZTbc2ntv8g4\\u002fLiRBIeg2lAra06bZfRAADIkIX4NuEOAFiTmPrAlJkfFJxDcfzZk4feu3R2eniUhM1RW8znO1q7Tb333m\\u002fPmbohpa\\u002fOtcMwH95tMqrMJvtho7NatFarCe39Vba3obpRqxIw2SJ272ETKfD7PZggNj9NHFap8dDfh+yDyqMrIzMjeRJqw+jwYeWx9ys9XLf1lyGOmBwUQ41ODNdsmSGV8P7aVfjD1rNms43HY0VHSW+6fu6WjdOEwpHv6AFBEAR5BM\\u002fmdiN4Ntf2GP658c2G8qEXRhglFmyZNf+3mSe2nQMA\\u002fPDS73u\\u002fOpIzP4Mn4Co7VaUnKw0aY3xWTFRK+Kk\\u002fXGRPllw399t\\u002f\\u002f6pV6swGy\\u002f0znpy\\u002feWZsWiSNQZc3dl3YXyRv6uIKudc\\u002fufGb53+hPtIXfPLqq25bnL+3EACw\\u002fb0929\\u002fbQ25ic1nSMEna9KSVty6atiKX3GQXEiN77pe\\u002fPbPhfwaNsfxs9WMLnxMHC6OSIzh8ttlo6elQy5u77FXmZ65xMfJ9OJ\\u002f8iNP16NVdWoPG2PtPa+xu6y2YcOlwKY4TfBGXL+bxRFy+mCeLDBpo7tmhGeYnHwAEAUrb5aXtcgAAnUaLkYqD+FwOk2lF0U6tvk2tcdQKSgmV\\u002ffuKZeTH+sqWvOzjNY0AgG\\u002fPFnx7toDcxGEyggX8nKiwzVOy5yW7SDor9UaVyaQ3W3UWi85s0ZutZlvvD9j95bVNSrWAwxKy2Xw2S8hhR4iE5DsKV0xKf+\\u002fIabXRbLLaNn3yw8rMlMTgIDqN1qbWnqxtbFNr+WzWvQtmvnv4tOMhFAaLbdulsm2XytgMRnKoLC5IIuSwCYLo1htL2ju7dL2Zh2umTUoKCXJ+KADDfniAwbO53QiezUetmaHx1BAAAIAEoSxWIG3WqwAAhT1tbhLuYhbHfRf4IYjgie0L9nrubgzh1ZdHpodwBF1mfbm6s1TVkS116qGrtZmPdFQDAJg0uss+8oOSGhNCDY0+1ISy91DC1maqbTPV2ldpCD2EHRXBTYrgJERyEyO4CWwaz\\u002fkRI+\\u002f5U4f+qKmgRgEAAGyrLttW3VtLyO71Rau2pGWTIwCAxbGJ65PTHZ3Bycwo2qLTtOg0O+uqrkrLfnXhyoAVXfGG4+awBUNLuuQlXXLndkBHkFuy856avYgSd0AAeHfpukxZyPuXzhpsNgCA0mRUXs5Wkzbzy7v++4z54XzhK+eOG2zWLqNhd301uZXHYP5t+tw7J00jB8mG8\\u002fA7924vVPTOO0HxSRH1AvrXK6+bHu6ihqNvzQ9LHtRJ3YhaHzq39YcFt4+2wWt61PJswV9725z+9Lx0Q+IMamgiWbEse8Uy6hcUBEGjyk9Nhz6v760ncG3skruS1jm3QxMdPJuDET2bf\\u002fzU1iFn20Ojg9KnJmRMT6Q2jJAnv32IRqMd+\\u002fUMAEDZrjq6tS8Rljw5\\u002fvltTxz79YzLtK9Awn\\u002f+t8ef3fSaXmWwWWyHfzxJbg2ODnr6x0cTsmO+feFXe\\u002fbTt3zy6jNWT1l87dwjP5+iNgBgMVk7GxWdjYpjv55Zecuiv316D0Kj\\u002fk7Jnpf+3ukXX7v9I3sheE23TtPd2x+LTBbpOq835E9+xH3w2NeUD9zhwLfHDnx7jBx54ov7Vty8kBwZvmF+8v5GrieD4XijUtWoVJHaAQAAQcDGyVn\\u002fXLlA5J\\u002fCJgtSEtbmpO263JubzGxDW1WaVpVmT2n1pilZL165nJJ5uP+nP4taXZ9fvjh1gRL54Y6rp8b2\\u002fYAVcdjvX7v+\\u002fp\\u002f+0prMVhTbUeyU+ggXCd66em1qaPD7R87Y51btz1GUxoKiZe3ysvZ+mQcacuPMKf9YsYAStxvmwwMPns3BiJ7NR6cgNk\\u002fEHPCbIUUUYk+41+m6qW0kWZIInycVmZe7h3qcYmQIr86g0a5KmPJhxQkAwE\\u002f1F1+a6vTbZ3dLuRXHAABLI1Ol7OGmi1OjQ7hsJgBgsDsZSENPuFPgBCY3N8vNzYXgCAAAAYiUFR7JTYjkJsfy0qN4yaO\\u002f\\u002f7tH35UVvpF\\u002fUm0xM2i0WJEknC9w9GVGcVxntdSrVQabFQDwa1VpVnDordl5To8fUY9OnTMzIvpwU31xl7xFp1GbTRYMpdNoIhY7URI0MyJ6Y0pmosTDBQ0CwH2TZ16Xkft7ddmptuYKZZf9eTgMZjhfkCKVzYqMWZc0uJtg3rspa\\u002fLqxNSfKooPN9c1adRaq0XM5sSLJItiE69NzwnxNIftMB8+qqyKyvqs2vUl8kAqNB33nPnhw1nX8RmjZaKFI51VLxTukps9D2XqL1EYcmVsLjUKQRAEQWMHPJuP4Nm8o7HryG\\u002fnHatsLmveFVOSc+OEYh5CQ15\\u002f4GuCICLiQzY\\u002fsBwQhE5jbKpozz9YatCaAAArrpvz2Ls39z3XKMBgMZ768ZEVtyza+9XhinM1aoWWL+bFpEUuvnbu6tsWM1gMl1N62mXPS\\u002f+86I3t7+3O31vYXi9HbZgoSBCZHD5rbd4V9yznCrkAgISsmPqSZuojfWGYr77j4\\u002f1fP\\u002feLrkdPZ9AjEsOCo6SMy6k61IYaNKbWmg6TzgQA2PfN0aQp8RseWOX0eAAAANGpke+c\\u002fM+lQyWntp8vOVmp7FAZNEYWhykNFUenRWbPTZ+1Li8hO5b6MADA8D55aDifvL\\u002f938oFyzOST9c1lbbLW1Vahd5gttlQDOeymGIuJyVElhcbuTYnLVra21fU5348X\\u002fTO4dMak5lOo8UGicOEAkcndBTDdRZro1JlsFgBAL8XlGVGhN44czL54cM0LS5q1wM3f3u24HhNQ7NKg2K4mMuJl0kWpSZeN30Sn80CAKSEyqrkrnOFDy6aNT0u6mh1Q2m7vFWlUZvMVhSl02hCDjtRJp0WH71+UoabIjzDfHjgwbP5CJ7NRy0Ry13x7SB2b\\u002fZJYzU5tzhxbDZYBADFPW2n5Q1VWkW7Qa22mQ02iwVDLThqxbytoTe0V782Me+TypMYQfSfOvWPpiL7wnCmS3WICZWcfP9BanSUQTze1tjX+U2zobLdVIcSNmqb1+gIPYKbGMtLj+Glx\\u002fLSRMwBB02MWh8Xnn\\u002fl3HEAwOLYxJfmL4sUuJjPF8Xx186fsPd6zpCF7NlyC3ULCPKR645\\u002fXtTTSo16kiIKfWP6lmRhKLUhsC4pmz+oPHqmq57a4LXP59w0JqZ3hyBoIoM93CGP4Nl8pM7mP7+955uX\\u002frQvp+XFP\\u002ffd\\u002fdLQvmv7K2MespptKbmx7x78lyOoVxs\\u002f+tfWw7+eAwCsv3PxfS9f42iCRsQvr\\u002f\\u002f1+ZM\\u002fAgBmrJ7y8Pt3hMYGU7cAAEOxL5\\u002f++dc3dgAAEifFfXzxVeoWEDT6fH7ywusHTgAAFqYkPH\\u002fF0gixkLoFABiOv3nwlL27enp4yB\\u002f33UjdAgogeDYfqbO5l+bsfLPbbAAA\\u002fLLk9slBg74PeuOxb893NQEAXpuxwX0tlCMdNfec+hkAkCoO3bn8HmrzZS8U7v2+Nh8AsD425\\u002fUZGyitH1aceLvsKABgY1zuq9PXU1o92t9W+XrJoUZ9b5WwgdydNueJnKXU6LBfHQBw\\u002f+lfDrZXAQBeyFt7bWJvL+QWg2rpnvcBAOFc0dE1D4\\u002fmbuk+5LmH+8rwWwAAGIG2m+qbjRXNhsoWY5UOpY6ocg8jsFZjTauxBoAdAAAxMziWlx7DT4\\u002flpYVzEugInfqAUcaE2t69eAYAIGZz3l+2js90XcOOQaM9PHXOp0X5BAANmsF9RBA0KDckzhzCSb1Gq7j66Ge3p8y5M2Ve4IewWTD0QEfFr40X87sbqW2DsSwiY5Sf0SEIgiDIG\\u002fBsTo0GSvWlRvsCh8d+9tv7yNl2AACLw7SabWaTU3lTgYT3xAe34hh+9Pf8vz4\\u002fEpMSvu72heQNoECyGC3fv7gNACCQ8p\\u002f64WF7X\\u002fj+6Az6jU9v\\u002fu3NnQRBtNW4LhEJQaOK2YZ+cOwsAEDE5bx51Rp7d\\u002fL+6DTaA4tmfXn6AkGA\\u002fuVuoACDZ3NqdMJz35HcjPb2ZuYxfPz\\u002f\\u002fknlqTdKD9uXJSzugvDkDElYJE8sZnF5DBaXzvim5vxvjYVOj\\u002fG165Km2hPuP9dfdCTcHdOlborPnSDZduBNwt2OjjBieKkxvNS5wVcCAFRWebOxstlY2WyolJubvJ+U2U5j6y7RnCzRnAQAMGmsKG5KLC8thpcWw0vnM1z0HB9x1SqlEbUBADJkIQNl2+04DAadRkNxnEP39rOFoCFYHZX1Rc2pqsFUi7MzY7YPK49tbbh4XcL0axKmytgC6ha+ZkAtpxR1xzqrD3ZU6mxmavMghXFFz0+GvUQhCIKg8QCezUdKU2W7fWHe+rygMGpRCBabCQCwmqijexEEefB\\u002f150\\u002fUGLUmb984fd56\\u002fMkwS56nkIB0FjeajZYAABJk+IGyrbbsbksGp2GoRjLp9N+QpCf1CiUJqsNAJAeFjxQtt2OzWDQEBpG4I7Z2qCRAs\\u002fmEIXKaqSGSLotBvuC2G3lmcGq0sjfKjtiX745ecYTOUv7pyWFrAEry\\u002fvKvLAk+6yw5erOMnVHliQCAPBXcykAAAFgc\\u002fwEqj5E\\u002ffS9JGWFSVlhuZKFAAALbmo1VjcbK5sMFa3GGgvu7sDqz4ZbGw1ljYbemRlk7IhYXnoMLy2Wlx7KifXTDJyDRbu8G\\u002fYS7W7srKtEcRwAkBrkYlQjBPkKHaH9K2fVLSe\\u002fpjZ4R2nRv1955KOqY9OD45dFpM8KSUgUhlA3GioCEG1Gdbm6o6intbCnpVTdbsMx6kZDwqTR35lx9dCqiUEQBEHQEJw7UvH8PV9TowP48dTT0pBBZGDh2Xyk6NS9P1jS8+KdGgAAlxPuBp2Lyqp8MW\\u002fx5hm7vj5uMlgO\\u002fnxmy4MrqFtAAUG7PP2pUechYXT0lzMYigEA4jKjqW0QNPo4ZvY1WKn3\\u002fCj2lFZhOA4ASAkdewV7xxl4NocoNFaz0mKQDfDh1GgU9oVUkS8LCu1sKbNPZZwliXhq8kqXuVSt1cNJc\\u002fgQAK5LnPpq8UEAwLbGoqzJEUU9bU36HgDAzJD4GP4omoDB34aYcCdj07hJgtwkQS4AgACE3NzUbKi0939XWakzSnuktHQoLR0FqiMAADaNF8NLjeGlxfLTY3ipbNpw57EdsmRpEJ\\u002fJNNhsJV3y78oKb8qaTN0CAAuG\\u002flpV+tKZY\\u002fbVa9LdlXaCoOGbHhy\\u002fMXby9uZCaoPXMAI\\u002f21V\\u002ftqseABDE5qeLw9NEYXGCoEiuJIwrkrC4YhaXRXPxLUEAwoha9TaLDjVrreZui15h0snN2g6Tpl7X3ahXmjEPF4hD8\\u002fSkNZOk8McSBEEQNH7As\\u002fmIMOl7f3CKZC66E\\u002fJEXACAQWsiCALpN\\u002fB50ry0XV8fBwAUHK+ECfeREpsexRVwTHpzzaX6HR\\u002fvv+JeF\\u002f8RVrNt3zdHP\\u002f3H9\\u002fbV1bcvcW6HoNEoKUTGYzGNVltZu\\u002fzH80XXz3DRG9SCor8XlL2677h9dUtetnM7NALg2RyiOCmvd1nwvUbb1WbU2JcnywZdUN6NFoPavjA1OIZ67XLZxW7Xk5D71ub4yW+XHbVg6J6W8qdyV+5pLbfHtyRMdtpuvHPx5zocCEDCOfHhnPgZslUAAD2qtpedaTZWdpjqBzvtqgU31uoLa\\u002fWFAAAEIC\\u002fk\\u002fE7dIlC4DOa9k2e+kX8SAPDMyYPflBXMiYyNEAiZNJoFw5QmQ71adUHeprf29n9fm5i2OTXL6SkgyA+eyV1bqm6v0fbeIB2OHovhtKLutKKOEmfQ6EyEzqDRaAiC4ThK4CiBoz66Kz4o\\u002f8pZfVX8VGoUgiAIggKCwaAnpIdTo87ozKHMSwTP5oFHZ9BRGwZc1Y0BAAglPAAAgRM9nRpZhITSGhHf2\\u002fGwuQrWBB8xbB77mr+v\\u002f\\u002fq5XwAA7z305Z8f7Ju8OCskJpjBpFvNNrVC01rTUXa6yqjtHaawYMus5TctcHoKCBqVOEzGXfOmv3P4NADghV2HfzhfOCshJlwsZNBoVhRTGoyNStWl5na9pTfzsCordcPkTKengEYIPJtDZF9Vn10Xk03vd9v+w4oT9oUpsmjfdvd2FJBRXi5ZQ\\u002fFHU7HHyVR9QsLiro7O\\u002fKOpWGkxFChbDrdXAwBETM6KqAzqpuOajxPuFAKGJFM0K1M0CwCAEZjc3NRuqmsz1bab6uTmJoxwN40AxWDLxPvcg3mzrBj6UeF5FMdrVcpalZK6BQAAACGLfe\\u002fkGfdNnkFtgCA\\u002f4NCZb8+4+rpjX2htLkY9+wSKYyjAwAicxPsgAHk2d+01CdOoDRAEQRAUKGKZ4N3fH6ZGfQGezQNPIOZaTFYAgKpLS20DQHy5Mntrrbx\\u002fwp3LZ9sXNN065xYooK57cqPVgv7y2p+oDWuubGuubKNuAQAAgC\\u002fmXf3E+mv\\u002fcSW1AYJGq3sXzLRi2Gcn81EMr+vqqetynSATcth3zp121\\u002fzp1AZohMCzOURWru7827nfX8hb4yjUjhHEe+XHdrX01tO+N31e39a+kCON\\u002fL2xCABwuKO6RtuVIuorTIQTxG+NBS8U7O3b2s+uS5xqnyh1++Us\\u002f7rYrP415ce3wL1bOkKP5CZGchOngeUAAIxA5eamdlN9p7lBbm6Wm5tMmJ76mNEEAeDx6fOuSsvZXlN+rqOlXt2jtpitGMahM\\u002fgsVrRQlCINnhcVtyQ2UcByN7cJBPlWgiD487k33XHq2+HPeTI68Rns\\u002f0xZvyoKDhmBIGiM6V+JAoIGAs\\u002fmARYWG6zs1AAA6staqW0AxKb2DmUoOVOTOz\\u002fNuREYL9d2xzDcuQUKKARBbv331StvXXTohxPFx8tbqzt0PXqrxcbmsnhCblhcSGxGdN7S7Jlr8uw1giBorEAQ8MiSOZumZP1VVJHf2NqgVGlMZiuKsZkMPosVJRElhwbNSYxbmJogcDurKhR48Gw+GnSbDWqrUWez6GwWvc2ss1lMaO9otv1tFU36HgGDLWSyBUy2kMmO4IoZNBr54RqrSWkx2B+us5n1Novc1Ht\\u002f\\u002fbS8gSAIAZMtYLLtTxLGFblMIq+ISi9VdexpLT\\u002faUTM9JDacKzai1vzuJsdTbYibtDgixflBw3VFbPY7ZUfVVpMJtW08+Nmq6MxEoYyO0NqM6hPy+jaDms9g3Zcx\\u002f52yo9RH+sEUWXSGJLxC3flnU7E9siV+ivMm45+LIyMw6AgjkpsUyU1yRLQ2Zae5SW5ukpsbK7UXBjv5amDEisSPTJ0NwGxqAwSNnGxJ5Odzbrrr9Pf+u5c+UlJFYW\\u002fPuDpeACcCgsYYA2o+3V16oaeq0dDZZVEbMQtBEFw6S8wSRHNDEgURkyUp2ZIENo1JfeRl3zce+Kphj335hrhltyeucW6n+rJ+9w9NB+3LtyWsvjF+uXN7rypdy\\u002f0X3rIv\\u002fzr330Gs3l6cetS0ryP\\u002fXE95o6FTYzMwEUYQSxjMluRKkqYHpaWLYmmI05Vofwqzal9nfommvtmg0KFGM+ZhjvE8acprk++jRp3V6FpPdZcWqGoUFrXaqmPQGBImP4EfMS0ofVForojpehqigGk0dB6WX7rQU6WwqHWoUcoUhnGkc4KzloTlhbAlAAAGMojqIibMcl5ZWaypq9W1dZiVetSM4RiXwRYxePH88HRR7KLQKZFcd1+Gy48+gRN4MFu8dc5zAIB2k\\u002fKL+l2F6loMx+IFEdfGLpkl6x1yTgDiQOfF7a3Hm40KNo2ZIYq7NnZJjiTR6en6Gf4e9jcBDxs34Nk8kJInxZSfrwMAlJ+jDtgHAMRn9NZUPbY9\\u002f4a\\u002fr6XcPKsp6q1\\u002fyhNyyHFoREQkhN749GYANlMbIGiMi5GKH1g0ixqFRj14Nh9x953eWtTjeszT51VnKJGfFt06NTiGHPlP4b6\\u002fmkvIEYftTUXbm4rIkVemrd8U72KihStisx\\u002fPXnLf6V\\u002fqdd3HO6lXGhviJr087QpKcPhETM6Hc66+7\\u002fRWjdVsxTHKuwjnit6etTlNHPpe+TH73Kr+dn3i1Gcu7bLiGAAgXRyWLY2gbjHejVjC3Q4jMK2tW2WVq2wKtVWhsspVVoXa1mXFx9t3EwT5VY406pdFdz149udanQ9qxo0GdIR2bcK0x7OWc+gDZiQhaBQiCOKn5sM\\u002fNR8yotSOLTrUpENNrcaus8ryH5sO8ejshaGTH07d7HL6owDotmjsCfeD8ovvVm8zkHYYBVibydJm6i5S137buO\\u002fnOc\\u002faM8guWXH0k7q\\u002fdrSdxgifdfZsMSo+qdtxprt3xKWdFUeNqLndpDzVXfpZ\\u002fc5rY5dcF7vE450AfzBj1s\\u002fqd\\u002f7ZdoogXa12WdRdFnWppuGbhn23J67ZHL1AxPRqsvcSdf3vrSfOKMtsOLXUns5m1NmMbabuU92lX9TvXho29cGUDe5Txt0WjQE1q6y6hy+9q7H1FnAsUdeXqOsfSNmwKXoBAOCd6m072k7bm8yY9ayy\\u002fFxPxVOZNy4Odd3xxLd7aDcBDxtvwLN5wGTPTvnr86MAAHmLsrGizZFht8udl0Zn0DAUb62V\\u002f\\u002fnZkQ139022abOiO744al+OiAt2xCEIgiDIDp7NoTxZTAhH8Oeyu7Y2XNrdUt6gVxpsFhmHP0UWc3XClDmhCdQH+Mi04NjdK+77pubcsc66ZkMPiuMSFjdOELQ4IuX6pGl8BgsAkCIKrdLIqY\\u002f0g\\u002fWxOa+WHNTbLGDiTZdqF9Af+SZM32Vp7bK0dplbuiytCkurxto14sXZIWh8iOUH\\u002fbzwzmcK\\u002ftrTVkptG2vyZLHPTFqTJvYwNx0EjTY4gT9T8uVZZe887O4ZMUutvm2ksu0AAKVFA4TRPzQd\\u002fLJ+N7WNJFEQ6T7b\\u002fkThh2WaRkcEQRAxgw8QoLEZyPloOyGDG8kNjuDKpgalUpocLvRUPV\\u002f6tQmzUBtIjKj5y\\u002frdpZqGZ7Ju5tF76ykHhhmzPln8WbGa2lfFwYLbPqr9s9konxOcTW3rp9Pc81jhB\\u002f0\\u002fKJcOyS82GDrenvIgn+GuX22TofP92j8c2XaHj2r\\u002fminLLFbXObLtDgRBvFX164ygjP7P7I89nICHjffg2Twwpi3NZrIYNisKADj867nbn91EbhVIeDlzUguPVwIAPn3619Za+ZKrZohlwvZ6xc9v7mmqbLdvljXLx4PBIQiCoPEBns1H0K9LbqeGBuP1GRten7GBGvXO4oiU6i3POFbZdMbNyTNuTh7cFI\\u002f3Z8y\\u002fP2M+Neq1EI7giZylT+QspTZctmP53dQQyTBf3SUWjX5lbA41OgH48Xe+FTfLzc1yc2OnuVFhbu2ytOhRNXUjCIJ8h8dgvTF9y5ro7BeLdsvNLiYBG\\u002f2ShCH3pi1YGz0Rv46hceDHpkPkbHu2OGGKNCWCK+PS2TbcprYaGg2dlbrmen1vsmZd5EgWKFNatXs7zjuy7TK2KEecKGOL2DSW1maoN3RUaZsxAnfUIXHpg5rtjrSphCW4K3HdgpBJPAYHAGBEzSe6Sz6r26my9hYrnCXLfGnSnZcf6trFnup\\u002fFX\\u002fm6PXMo7PnheSki+LETL4VR9tN3We6y2r1vaNEzysrXir77sVJdyAgcNXSX634iZxtD+VIF4dOjuWFselMlVVXoq4\\u002fqyy34uiu9rOtxi7S41wL5wTND550vKt3dGo4JyhLnJAkiJSyhGwaU2MzVGibjncVOWqt1Ovbv2rY82DKxr6n6OfLhj1V2uY8acrSsKkogW1tPtxuUgIAcAL\\u002fpfnIGWUZAGBucM68kBwzZv25+ZDcrAIAGFDzEUVB\\u002f2PSH3s4AQ+bQYFn8wDg8tkzV046ueMSAODAT2du+dcGOsNp3MNVD66wJ9wJgtj11bFdXx0jt9qtvGEuNQRBEARBAAB4NocmvB0tJfbu7cui0h0zx04oPku4E4BQWjrk5kZ7HfZOc6PaqvBh73X6YAqhQtBEtjQifWZIwidVx3+szzd5qoc7ekyTxd2eMndheMqozYBAkHs4gf\\u002fRdtK+TENo\\u002f86+bU6w6wmFeqy6E13FJ7uKl4blUdsCKL+nMr+nCgAQxw+7J2n9TFkGZQMDaj7eVZQuiqXEHdpM3bs7ztmXuXT2O1MeiuaFOFp5DM7K8OmTxIn3XHjDXq\\u002fmrLK8wdCRwB+wfp\\u002faqn+54gdH2nRR6ORH064SMpyuz25NWHVQfvGNyq1WHAUAnFWW\\u002f95yYnPMAvI2\\u002fnO6u8yRegYArIuc\\u002fUDKRvIwhU3RCzrNPS+VfVeubSoauBc82bVxS4o1dSvDZywPn9r\\u002fw1kfNefOpLXPlnxZqe0tG72j\\u002ffTtiWvcdNAuUNXMDc7+d85t9q\\u002fTGUHpt557xYLbAAA7288AADbHLLg\\u002feYN942xx\\u002fF35r9uXL6mq+yfcga\\u002f3cAIeNkMDz+b+duXdS07tKpi7dsrVD6+kZNsBAHmLM+esnXx6VyEl7rDu9oXxGZHUKARBEASRwLM5NDERAHxfe8G+fG3iSP7mHUFDT7gbUa0jty43NykszTbcl18fNIQeyo6J5CZFcZMjuUnh3HjqFhAEDUDAYD+etfz2lLlf1Zz+qSHfgLobYj+yQjjC1VFZ62NyMyUDJlMgaEzotmgcfXKnSlMHyrYDAIJYwiuj5l4ZNcJdI092lQAApkhT\\u002fpNzO9dVbpTP4KyOmEmNkhxTFOKXs5xXRs0lp00dIriyTdELvmvcb189pihKSBjwj\\u002f3Lht2Oz3BucPbTWTe5vMpfFjbVitveqPzFvvpj08F1kbPYdJbzVn7x\\u002feU3AgCYE5z1aNqW\\u002fnsYzgl6Nfeeu\\u002fJf7zT3UJpcShPG\\u002fDLnefrAVcVlLNF\\u002fcu648exLFswKAEBxrERd3\\u002f8GCdmN8SscOxbKkc4Jzj6iKLCvsumsWxNWO7ZMFEQm8CMaDB0AgGaD60qjvt3DCXjYDBk8m\\u002ftV9qzkry68GBYz4ORvT3xw2\\u002fPqD4pPVVMbAFhw5dR7\\u002fnMVNQpB0Kixp67mu5KC8q4uvc0qYLJiROL7p85YnTxgXbIJokmjXvTdFwCAS3feL+U43ZmegAL2acCzOTQB\\u002fdlUXKmRAwAyJeGzQuKpzRODtwl3jEC7LK2d5ka5qanT0iQ3NepQFXWj4aEhtJDLGfYoblI4N4GBwAkZIGjopCze37KW3Ze+YG9b+e9NBReVTdQtRk44VzwvNGlNdPaM4AQa4iIzAkFjjhm3UUOjnoQleCbrZpfZdm+UaBocy7MHvsEwU5bhyJw6OkH3p7bpD3T29oNg0RiPpLrIZTusjpj5c9ORNlMXAEBt0x9WFLi\\u002fN+ATDYaOKl2LfZmG0O5NvnKgPeQxOLcmrHql4kdqwwDc5LLtgljCBSGTHJ9Pi1ExUDobAMBjcFKF0eRIljjekXCfIkmm9D2P44fZE+4Ky4CXdj7cw7F72CjlmtWp\\u002f0eNktzzrys23DqPGh02eDb3HzfZdgAAl89+5ffH9n5\\u002fcv9Pp5sqO8wGi0DCy5iWuOqmebNX51K3hiBo1PixtPipowcAAEw6PUIgMKNoWZecxxrVd1ihcQ+ezaGJ47Si4bmC3rKlj+cscW6cQDwn3H9tebPT3NRtacMJjNo2PDSEFsyOjuIm2ZPs4ZwEJg2eBSHIx7h01sbYyRtjJ7caVcc6q4\\u002fLa\\u002fO7G83YCGQGg9j8abK4WSEJs0IS4wXufuJC0FgUxpEyaQwbjgIAClQ1ZZrGLHE8daNRZnP0AjGTT416rcfSV48ygjPgH3U4J8ix3GMdsITlMUWhvdwHAGBOcLaMLXJud4IAZF5Iztbmw\\u002fbViz3Vg8qcDs15ZYVjOUscH8UNJjVSzQvJYVUxHO9o+FKF0Y7MsrbfhKhkMbxQSiSUI3UsJwmoRTAkLIF9YZgjnb3cw4l22PgKPJuPCISGrL55\\u002fuqb5wMACJxAaDANAUFjwLv5ZwAAG9IyXlmygk1nAAB0VguPATvzQSMPns2hcalA2fpx5ckgNh8j8Dptd4mqd8ayzfGT54clOW87gXhOuBerT1BDQ4UAJJgdZS8RE8VLjuAkMGlD7FUHQdBgRfOkNyTOvCFxpgVDi1Stpaq2EnV7qaqtzaimbuoLHDoziidJFoVmiMPTxeHp4vBQjpC6EQSNI2wac15wjr0fMUpgjxd+uCl6weaYBTKWuwzgyFoYOqxOmiasb0gsgzbgVCsM0iwsjkLb\\u002fRWr6x3L04LSSC2ukRPHZdrGvga\\u002fqda1OpanSj0MS+fS2cnC6PLLU4MOn4DBcyy7z+NLmb0JdAfyIIYQtqSvAQAAAIvWm4PACRzFMTf\\u002fle55uYdj97BhMOgJ6eHUKIkkmPrJ+wM8m48UmG2HoDGhVauVG\\u002fQAgEdnzLFn2wEAQhZMO0CjCzybQ+OJ2mo60lFDCS6KSPl33hpKcELxnHAfDgQgMnakow97BDeRReNQN4IgKLDYdMaM4PgZwfH2VRNmbTaoWgw9LQZVh0mjshjVVqPKatRYTSbMZsMx+z+cIOg0GgOhMWl0Jo3OROgsOl3A4IhYHDGTK2FxRUyulM2L5IqjeJJIniSIPfRusxA0Rt2TfEWBqkZt0wMAbDi6tfnwtpZjc4KzV0ZMnx6U7rEoR4AJGdworovy2d4TMfltpm77ssqqG6izvJLUPdnRmbo\\u002fctmQWF4YqcU1EbMvveumB7QPNRo6HctxfM97GMMN8WHC3fsRvuRPxo5Oeqyo338TjVSDBR\\u002fGdPde7uHYPWzEMsG7vz9MjY4ceDaHIAjqT2U22ReihKO3xwMEOcCzOTQOBLF5KaKQDpPWhFoFTHaGOHxj\\u002fKQrYyd5+etgvPJxwh0BiIwdEclNiuQmR3GTIriJbJofZ5+AIGj4uHRWmigsTeQ5TwFBkHshbMm7Ux96sew7R1dolMCOdxUd7yoSM\\u002flLw\\u002fJWRczsX9BjpEhYw+3YkiqMqdD2FqAsVNXG8113\\u002fi1S1zqW04QxpBYn5Oznw5feJbV4huKYEbNQSpP7nA41OpaDWWJSi2tu0sQu6VBToaqmwdDRZJCrbXq9zaRHTTYcteI2K4666TNOwaS5u7pz06ncI5\\u002fs4UQ7bAIGns0hCIIAAEZbb3UOBm10dXSAIG\\u002fAszk0FuUGRe1acS81OuG5+0nmDQQgUlZ4FDcpkpdk78nOplH7VUEQBEHQBBHFDflw6mN7O8\\u002f\\u002f3Hy41djliGtsht9bT\\u002fzeeiJHknht7JJZskzSg0YGlz7ceVOWhE35s+2kffnXlqPLw6fxGdRxbEbU\\u002fEvzUcfqwhDXRWy8T9cOxIajwM+ZUwNqdixzvPj0vJ+N9kJP1dbmw0XqOje1U7xHLsbS39BGWvhwDyfaYQNBEAT5m9ZiuX\\u002fvDrle12nQ662985EkvP+GY4OZUdE\\u002fb7zGsWpXpez+rODCmbaWLqOBy2Cky0I2p2dtycgid8k02mxZn7ybGiTbd\\u002f2t59paP7l0vlDeqbNagnn82VExD02flSDpmyXFAcPxnbVVO2qqShVyldnEotNlXF5mcMjShKTN6dTZwr3ZjSHACOKnsuJfyktqe3qYdFpOSNhdedPTZK6nnymSd\\u002f5ZXXG+vbVRozajqIjFTpIGXZGSdn12Lvm+xbxvPosSir5Yt\\u002fGxA7tPtTTHSyQvL16RGxb+9vnT3xQXEAS4KiPrybkLyXvu5TM7\\u002fFZRtrW8pFLZ5fh\\u002fdOj\\u002fn+j9RzeoTwOCIMgnhpJwl7LConr7sCdHchM5dDg4BYIgCIJ6IQiyOmLmqvAZZ5XluzvOnlNWkHOUJer6EnV9njT1ifRrwkhTWQ7T8NOgQ5AtTpgelJ7fUwkA6DT3PFbw\\u002fiOpW8hTxVZom96p3tZp7rGvLgqdnCaKdbSSob6emN0fyMVWkH6\\u002f5fpzn\\u002fi2M2GWVyp+PNlVQm0AAEEQAZ3DZ3BZNCaHztSjpnaTkrqRa573zXs+38OJdthAEARB\\u002foYTBIpjMh5PxuPprdayLgUAYGZUtGODzGDqdOI\\u002flRU\\u002fc\\u002fQgRhB0Gi2ML1CZTOfbW8+3t+6srfp87QYW3ekM3qBRb6ss+\\u002fvBvQiChAuEBCA69brtVeX7G2q3b7k+Jchpwsluo\\u002fGe3X9e6uydM5BNZ5hstiarukmjZtEZlIT7oHbDezhBPLh3x966GgCAkMUWszn5HW2n\\u002fmp+fOZc6qYAFMo7Nv76o32Zx2SG8wVdRsOFjrYLHW3Hmhu\\u002fWLeRvHFNj\\u002fLZY4dOtzYjCKjo7rp3z1\\u002f35U1\\u002f5\\u002fwZGZenNBs\\u002fL7yYEhR8dWa2feNBPTMA4P8O7\\u002fulvJRFp8+NjhWxOYXyjiaNGgCQGRw6Myp6WkQUeWPvP7pBfRoQBEG+4lXCXcIK7Z3plJsUyU3i0gc3RBqCIAiCJhoEQWYHZ80OzlJZdYfkl\\u002fZ2nG8wdDhaL6mq77\\u002f41ltTHozlUX8BDo0Jo\\u002fYDCown0q956NK7CrMKAFCnb3\\u002f40rsSliCCI0MA6DT39Fh1ji2TBVGPp1M7lznw6GwGjY7ivfnTJ9Kv6V+I3D0+ndpL2ucEdI7l8ufszQduxj1sQxDEU8VfkGunxPBCF4dOmSRJjOGFytgihJQ639F2+u3q3xyrgeGnPZxQhw00Lp3ZXShvUQIANtyzlNoGQVDASTgcR9\\u002fngs6OTb\\u002f9CADo36Xd4XRr89NHDzJotP8sWHJVRra9q\\u002fXJlqbHD+450dz48unjz81fTN7ehmF\\u002fP7j3yrSMp+YuCubxAAAXOtru2\\u002fNXt9H45rlTH61e79gSJ4i7d\\u002f9R0NnBZ7Eenzl3bXJaKJ+P4XiLVnO0qWGqc8p4sLvhve9Li\\u002fbW1dAR5KXFy7dkZNMRxIyinxbkv3HuFHVTACaHRWxKz8wKCV2dlBohEAIAbBj2ZdGlV04fP9xYf66tlXzrQmU2teu1BXc+YLBZV\\u002f74Tade9+KpY1+v37wwNv6ji+f\\u002fd+bEvvoaR8J9UM98qrX5l\\u002fJSNp3x59U32Pueozj+8P5de2qrw\\u002fj8Z50\\u002fikF9dIP6NCAIgnzFc8L9ycxvefThlnmFIAiCoIlJyhJuiVm4JWZhqabh+8YD9o69AAC1Vf9i2befTHvcm77SHmlsemooIILZ4nfzHnq5\\u002fEdHTlZt1autTjuDAGRlxPQHUza6L7EiZvKVlt563KnCmNFT7N5BwOQ6ZvJUeTHfps7WV\\u002fPdpV0dZx2fGwKQ+1Ou3Bg9n5zCJhv6ZKbD4Kc9nFCHDTQu\\u002ff7xodIzNQAm3CFobHr19AmcIJ6YNe+6rEmO4LyYuKfmLnpk\\u002f64fSov+NnOOkOV09skODXtj2WpHrZJpEVEPTZv13PHDp1qayZvtqasu6OygIcg3V2yeGtF7SqLTaPES6a39is8MYTe89GXhRQDALZPyrsnMsUc4DMbD02eXd3Xtq69x2hQAAMAby1aTV5l0+j1503fXVhcrOi91tpPT4gCA23LzWHQ6i85dmpD0c1lxXnjEwth4AMDyxOT\\u002fnTnRqFGTN\\u002fb+mffX1wIAViWlOCq9MGi0B6bO3FNbfby5EcNxOqkEzaA+usF+GhAEQT7hOeE+\\u002frLtp5tyMMLkWE0NfjVMsJnUDk1c8NiARgN4HHpp9d8\\u002fU6h9mWVePTP9xTudfhX4VrY44ZXcuw\\u002fLL71S8aO9Akydvr1AXZsnTaFu6lxoG\\u002fWiXEyTQU4NBUoIW3J93NJafau9xLmIydejJjpCEzK40bzQHHHCyogZUVzPVTLThDGnLWX25QZDxyjMnMbxwhyfc5NBPje492fbQFpIRfxd2tdx3rF8RdTsTdELSI1UetKUrQHjvz2cOIcNNC4ZdX0zOkwo97257XyFU3qR4vnbVlwxh1qiGhrImLuSCQyrDf3jZNnhSzW1bd06o4XLZkaHSGZnxV23dEqQaHDDmFzq0OuKFZ0AgCtS0ylNM6KiAQA2DCvs7JgfG09uuiYzh1IZPDs0DACgs1osGMqm9+ZV7HVLFsbGO7LtAxnabnijVau1V2K5Mo36zGtTUr1PMSdIpMWKTp3VQo2Le+8chPMFAIA0WYh9VcrhAAAM\\u002fWqv9+fymeV6PQAgRiQiB2NEYgAARhBdRkO4oDcxNaiPzlefBgRB0GB5TrhDEARBEOQrS8LySjWNjkkjK7VNLhPufAbXsawmVdhwSWMzNBtHLOG+vfXE+zXbAQAylujprJsmSZKoW3hnijT1dHdv5vRMd9mysKnO7SMvVRhzvKvYvlygqrk+bplzuxOcwGv0rdSos0ZDp2N5Wdg0UosLDaSNA8Z\\u002fezhxDhtoXDJq++6LQxDkQ509uoff2V7X3jcjiM5oqWiSVzTJfz1a9Mb966emOfW2HoKK7t7b4bO\\u002f+sS5pY\\u002fSRP0b7z\\u002fBpoDZO306hhPgcsHwamU3AGBapFPpGJeGthveaNKo7AvJUqfi8gAAl1O8AgCMNtuOmspTLc21KqXKZNLbrBYMs2EYAIDoN35NyO7tOW6vky66vGq\\u002fIUGZVcj7Zw7icgEAXUanm\\u002fcKowEAgABA7uk\\u002fqI9uCJ8GBEGQT8CEOwRBEAQFVLKwr9OTFUdJLX2CWH3Dy8q1TaQWFw7JL47IpKkAgHp9+we1f9iX\\u002fzPpjjRhjFPzYCwMyf20bocNRwEAx7uKW4yKGB8VuPeV6bL0z+t32ZeL1HVys8rNtLcXVdXuS8oQBEEuBC9h8UmNVDrUdE5ZTo36mf\\u002f2cIweNhql\\u002fuFN71Kjzl788k6RxAcdMKFRzqgfShYMgiD3cIL4x0c7yNl2Mp3R8sRHO379983BYnfnI4+0FgsAgIYg8QPnW7lMap7Ey9IuWqsVACBh93WbGMjQdsMbBpsNAIAAwGMyKU2OmwRkjnr0NARJl4VMCY8QsTlcJvNYU0ODujdbTWYvmO5AH7g04qCeeUl84k9lxbtrqx+ZMdte8B1crgYzMyqGz+rb80F9dIP9NCAIgnxlKN\\u002fgXiIAYcYMKG61EVYEIAyEyaSx2XTeQKU\\u002fIQiCIGjssuIoi+bVWbVK2+JYDmFL+hpIMkRxjuUWo+JiT\\u002fXUoFRSex+lVftt435qNFCOdRURBAEAYNDoyQLP\\u002fbnckLFFayJm2fv+4wT+fOnXb015QMT0\\u002fKPa+09+mJIFUcmCqFp9GwAAI\\u002fCPa\\u002f98LvtW6kYAAAAwAv+ifjc16gxBEAGT60jKtxq7o7i9g7IpCEC8X\\u002f27vfRKIPlvD8foYYOiWE1pGzXqDLNh1BA0HhlgD3cI8oMTxfVlje5G7GkN5p8PFz64cS61YTAELBYAAAHgwPW3UqrEuOHldnwmEwDQvwxLf0PbDW\\u002fYM8sEAGYU5TCcznRmjNrPw4Ta7t39l9JknBUV886KtaH8vhPofXpd\\u002f7S49wb7zEsTkpYnJB9oqF3x49fLEpJELHaBvKNEIRezOf9esIS85aA+ukF9GhAEQT40uF8a7ulRdb2+pN1U12GuV1kVWpsSI6hfYXSEIWQGSZmh4dz4CE5ioiBbzHT94w2CIAiCxpCvG\\u002fZUaptXRsyYGZQhYQmozQAAAAhA7G4\\u002ft7vjnH0VQZAZMmpBSTsZW5QmjKnS9abmX6388ZVJdyf2q09drWt9sexb9z2p\\u002fcrRsx7Fsf9V\\u002fLQpen40L5THYA\\u002ft5votCSvPKsvkZhUAoNHQeXf+G\\u002fcmr58fMolc0d5BZdUVqGqOdRXpUdMbk++nNvvHjfHLny\\u002f92r58vKv4vZrf701az3TO2+psxtcqt9boWslBl7JE8Wcv9wr\\u002foelAnjSF8lQAAD1qeqd622H5JUo8MPy0hxPtsIHGGavZhqEjM6gIgsa344X11FA\\u002fx4vqhplwtxeHwQiivFuRHRJGbR6eZKmsTtVzqXOlKtgAAQAASURBVLOd2tCP\\u002f3YjViyxL9SperJCnEZ9tWqpU76fb29TmowAgDeXrybnxAEAXUYDeXWwBvvMCAAfrr7ihj9+Pd\\u002feuqe2BiPwML7g2qxJD06bGSV0Kuw+qI9uUJ8GBEGQD1F\\u002fNQ2BAdUUqI8Uq090mhoI0K\\u002fElzOMQNVWhdqqaDCU2iPB7Khs8Zwp0sVBrAjnbSEIgiBozMAJokhdV6SuQwCSJIhMFkbF8cIkLAGXzsYIXG3VNxsV+T0V7aa+gdLrImcP1MMdAHBd3FJHbldp0d574c15ITm5kmQpS2DGbF0W9SVVdZGqjgAEApClYXkH5RedHh8Qc4Ozf246bD\\u002f7H5Rf7L8PCEC4DLaUKUwRRs0LyZkfPIlBu1zotB8xk\\u002f+fnDseufSeCbMAALos6v+UfSti8jNFcRFcGZ\\u002fBseGoEbPIzapmg7zT3GN\\u002fVLY4welZ\\u002fGl+yKS5wTmnukvsq3+0njzTXbYodEocP4xNY2pshkpt86nuEntf70Whk48qCskPp1gTOcuRzi7TNN574c0NUfNShdE8BseEWdtN3YXq2kPyS0bUDABYFTFjL2kK08Dw0x6OrcNm5uKMPdWvUqPQBAa7t0OQn9S09tbmdqOhvQfDcbpzVZNBiRGJc0LDShTyjy\\u002fmv79qHbV5eJYnJu2rrznUWF\\u002faJXefC\\u002fbfbsSKxNEiUatW+2d1BSXFbJ\\u002fTlcxotQIAaAgSwnPKiTeqVYXyQUzN0t8QnvmlU8fOt7e+tXzNhrQMahvJoD66QX0aY87G976v6ugCAHx485WL0hOpzaPbmN55CPLGsBLuKqv8qOLXIvVRjBj6+NluS9tRxa9HFb+mCqcuDr06mpdK3QKCIAiCxg4CELX6NnvhETemB6Xfl7SeGiWZHzJpbeSsXe1n7asYgR9TFB1TFDlvBQAAtyasWhw2pX\\u002fWMgAyRHFPpF\\u002fzQc12I+Z69DQBCCNqNqLmNlPXUUVhNC\\u002fkX5k3uqnZnSSIfG\\u002fqw8+VfNVm6rZHtDaDI+c7GjyZef0\\u002fiz4t1TTYV+Vm1dbmw86bAADAdXFLr4ya6z7hPjc4m5yUbzR0vl39m9MWl22Mnv9gysZLqhqFmToE26\\u002f8tIcT8LCBAu+56z+ghnzEYu6b2wCCIB\\u002fSGFyfFMhwgtAZLRKB5yLpbjw9b9H1f\\u002fy6q7aKeYB239SZqUEyAIDKbGrRag421DFotIenz6Y+xjtXpmZ8XnCxUtl105+\\u002f\\u002fd\\u002fs+auSUiUcDkYQXQb9+fY2ldl0y6Qpjo39txu35eb958TRL4supQYFb0rPpCGIDcN+Kiv+o4p6ZrT3FscJ4ruSwtty8+zBU63NTx05YC\\u002f+NmRDeOatZSU0BIkWiVAcp1SKpxjUR+f9pwFBEORDQ0y4o4TtiPznU91\\u002fDifVTlGtu1itu5gjnrcm8g4BQ0JthiAIgiCSm1dNa+vSqPUmtd6k0pnsC2YrtZRZwGSK46Ryocqqozb0w2dwro9bdlXMIpclL8geS71KxhL\\u002f2HQQHeBsy2dwHkzZuCJ8OkEQLBpjoClY\\u002fadC21SorqV5eiMOrcauvxd+9E7eQwn8AYe1JfAjPpr2t5+bD\\u002f3RdsreddqNGF7oqogZ1Kg\\u002fcensV3Lv\\u002fqRux872My5\\u002fMXLorPuTN6yNnAUA4DE47t\\u002fCkxk3iJn8P9tOURsu49LZdyWtuzJqLgAgUxTnTTrbt\\u002fyxhxPwsIEC7\\u002fyB3pEoEDRqjbYrmbGCRhtK\\u002fTGyGZHR76xY849D+\\u002f6oqvijqoJBoyEIYsN6r7Xcd692j0GjfbFuwx07\\u002f6hUdj155MCTRw5wGUwLhuIEAQBYEBtPTrj7bzdumZR3qqX5cGP93w\\u002ftfeHEkSAuV27QW1D05SUrXjhxxGizObZMlAZtSMv4o6rihRNHviy6GMzlt+o03UZjXnjk9VmTXj59nPSsgzOEZ16bkvpbRdlV2352RLgMZpRQuDAu4b6pM2TcvjnJB\\u002fXRef9pQBAE+dBQEu5yc\\u002fPPza92WzwXJhuCEs3JGn3B5uhH0kXTqW0QBEEQdNl1S\\u002ft+sThYbajjJ6tKb7YvqHUmtd5U3ihv69ZQH+A7C0Jy58iyi9R1ReraekNHp6mnx6o14zYrbmMhDC6DHcYJSuRHTA1KnS3L4tBZ1Me7giDILQkr10XO3tNxrkhd12Ts1NtMAAApSxjJDZ4XkrModLKYybdvGc0Lrdf75dTsEkpg71Rt293R2wFfyhJOkaaEsCVsGtOxDQEIK25TWXX1+g5Hl38Dan63+ve3pjzg2Kw\\u002fPoNzR+Laa2KXnOkuu6SqqdG1qm16nc1IQxAenRPMFkfzQjJEcXnSlP517QOAS2c\\u002fmrrlyqi5h+SXLvRUdZnVeswkYQrCOUFzg7OXhU8LYgntW8byQiu1zc6PdsKg0R9O3XxF1Jw9HedK1PUdJqUBs9ARmoQpiOOH5UlTV4ZPd0wJkCmKd99l3h98u4cT+bCBIAiiGG1XMiNOLOB4LCrDoNOEXA41Onhrk9PywiO\\u002fLi443tTYrFVjOBEtEsWIxIvjEtcmD2vMfaRQ9OfVN\\u002fxeWbartrq8W6G1WAQsVjCPPyUsYlN6JmVjP+0GHUE+XXPl96VFv1aU1ql6lCbj1PDI+6bNnBsd+31JUWmXnLzx60tXTY2I2lpW0qhR9ZhMsSLJbbl5d06eVqtSgtPkDQdtUM9cq+rBcIKOIAQAfCYLAEAAwoSitaqeWlXP7trqPdfdLGb3\\u002fdd7\\u002f9EN6tOAIAjyFcRl5yw3yrVnt7W8Y8U9dB0aJgQgS8KuWxR6FbXBF0435WBEX+3F1OBXwwSbSe3QxAWPDWg0gMehn7y37eTXe\\u002fOpUZLVM9NfvHM1NQoN4OXyHxxFbG5PXHNN7GIGMmCVbQBAhbbpH0WfOLoe\\u002fzD76XBOkPMm0PgHDxsoYFaH3EsN+cGero+poXHtvje3na9wdxPx+dtWXDEnixqFfGTcX8m8+O3B7Sc8jE3JTYr88p\\u002fXUKPQGFet7N74248MGu3tFWvnx8Q56skQAFQru2\\u002ffub1dp31u\\u002fuJbL5emgezGdBn0Mb3zUAC8t+0kNeQHD22eRw35zuB6uBepj\\u002f3e+i5O4NQGXyMAcUj+oxU3rwi\\u002fidoGQRAEQdCIKtU0ONKmayNn3RC3zLndhQxR3LrI2b80H7GvVmqbYeZ0ooGHDTQiHn37JpGUT40Og6ZH\\u002f85j31OjEAQN2+IpSR4T7mtmpVND0Nj3ZdElo832+My5i+OcpjRHAEiTBS+LT\\u002fy2pLBNpyU3QRA0vrm\\u002fwewroyXhXqE9v63lHQJ41SNeyJAKmFIeXciksRkIgwAERqBW3GxEdTpUZUC9Ggp3out3Hl04L2QDtQGCIAiCoJFzjFQ2ZF3knL4GtxL44Y5lrc1IaoEmBHjYQCNiwYZpXD6bGh0GncoAE+4Q5A+zs+NzEiNK6juoDZdlxIWtn5dNjUJjX4deBwAQc1wXC2rQqAEAofze+nUQBEFjgrcJd7m56beWt9xk2zl0XpJgcpJgUhQ3OYQdzaS5u6614maFubnNVFenL6rTF7kpULO\\u002f89twbnyyYDK1AYIgCIKgEdJsVDiWY3ghpBZ3jKjFsSxguP5NBY1j8LCBAo\\u002fFZvo22w4A4Am51BAEQb5AQ5BX71330Du\\u002f17UpqW0A5CZHvn7fFSyGu0Jk0BiVHhxyvLnxm+KC+TFx8RKpI95p0H908fyJ5kY2nbE6aehF7cer4U4fPKLG9M5D4wCfy4oP9+\\u002fAWa8S7hiB\\u002fdby9kBp8Qhu4rzgDZniWQykb8Ir91g0TjQvNZqXOlO22oZby7SnT3X92WlupG4HAAGI7a3vP5z6LpvWNyd1fwSBKk0He4xH9NZSM9qG4yYawmIxQnnMNCl3bgh\\u002fHYMmdmyMICxAqo88BCiuURoPasznDNYqC9aO4noACAYiYDNjBKwsKWdeEG8xDfHNxX0gX6s\\u002fAmBq00ml8bDeWmpGWzBcTwCCgfBZjAgeM1HIniLhzOaz0qgPG00CeWwYbbUa83mDtUJvrbBh3SiuwXAjjcalI3wmXcxjJvNZGWL2dBFnGvWRg2S01bSoP1KbT9twNZMWJOZMjxLdLmTnUjbrMR5u132rsxTjhIVFDxGxp4YLrxZzZlI28yhg7wtcPuQ05nytpcCCtqO4FiMMTFoQmxEu4cwNE2zkMn1c301vKVGajugsl0y2RhumxAgLHWEz6cFcZoKIPVXGW85n+eDaLpDHIQQFAPkGPIpjwLsfv0XqWsdyGCwMMvHAwwYKPJHM9z0i6Qwah8c2G\\u002ftuBUEQ5CthUsH3T13\\u002fx8myQ5dqalu7dUYLn8vKiA1dNydz1Yx0Gg3m6Mane\\u002fOm76urqVP1LP7+y3iJVMbl4gQhNxjadVoAAJfBfH3ZqmiRiPqwieF0bdPvF8oKmtuVeqOAzUoICVo9KW3LtGwWg+7y\\u002ftMPZwpf2nEkJUz25yM3K7T6\\u002f+48erqmic9mLc9OeXTFXB6Lad\\u002fm21MFCq0+Llhyy7y8jXkDzr1xurbpz0sVxS0dXTqD0WqjNl\\u002f29Z1XzUiMpkYHv\\u002fMQ5PDQpoGLvSCASaeL+RyJgPvtvgsXq1vtYRaTkR4bEhcWJBVyeWwmk0E3WWwag7lZrqpoVmgNvTntFdPTHr1qQZjU95eIFF4l3E91u86G8+jC1ZG3T5YsojYMBpPGmixZlCtZWKg6sqfjKxOmp2ygtSkPy7eujriNEneQ67c1qt6wYn3dpgAAGGEy2ZpMtialcX99z8uRoltiJQ\\u002fSES4AgEETorhXNW36M6OtzeoPFIY\\u002fCIL6XWMj1DaLWm8p6dT9zKCJIkW3RIvupNOGXjIyAK+lNp0qkd\\u002fiWJ0efYTDiHGsdhv2Nqj+Z0apMyPZCLXNqjZYK7oMuwAAAlbmlMi\\u002fKNuMEoE5Noy2Wrn+N6XxgMnWRG0DAMP1GNBbMbnBWt1l2A0A4DBiI0U3RYpuRjwlG3qMh8sUd9uXY8T3x0v\\u002fBgDoNuyp6n4cJ6z2uBVTdBl2dRn2JEifiBb3bgwAXqN8plO39fIqMKOtZrRVYfgzUnRLUtAzjrgb\\u002fntfLhEAa9d+16792oz2fl06WDGFFVPoLMUtmo8oTRQIoM+Lr6JGB9Bt3Nesfs9graTEMcKEoS1mtEVlOt6kfkvEzouXPj6EGxUOgTkOISiQQtl9nY8uqKoWh04hNbp2SVVzsrvUvixhCTJEsc7t0PgHDxso8ERBfvk1xRdxYMIdgvyExWRcvTj36sXUvkTQOCblcHdec9M3xQWHGuvqVD0tGjWdRpNwOLOiYuZGx16dmRPKH3SuYxxAMfzp3\\u002ff\\u002fVVDhiPSgph5D28XGtp\\u002fPFb1\\u002f43oxd8CRf43daqPVdtdXv9fIlQAAvcX6\\u002femCdrX2\\u002fRvXf3T43HsHT9s3q+7sfuq3\\u002fQhANuRlOj0eAJPV9vdf9hwur6PE+0MQwOh3M2w4Ow9BAIBbV0+nhpzhBPHf7w7Zs+2RMtG9V85ZnJfMY7vuCI7jxNnypg+2n6psVuzPr4oMFrlL6PuI54S7GTOc6PqdGgUglB1zU\\u002fzTElYotWFIEIBMkS5J4Gd\\u002f1\\u002fSSwkzN8Ob37J0fslHAkFDiOGGu6nqi27iXEqfACXOr5pMe48HM0E+4zHgGTQIANaPnjXbd9w09r+Je9DxFcW2z+j25flta8OtizgxqsxcC+VoOFrTdnnAnCKy255lO3S\\u002fULVwh5+hHj8AcG2rzmRbNx2rTKWqDW2a0ub7npS7DzoyQD9iMvtK07umtpQAAvaWksvtv\\u002fe\\u002fBAIA3qP7HYcYG81YBAOp7XiZn28natd+w6aHR4nuoDSSBfF92FrSzqvsxjTkQ02IAAGyYqrr7Hz2m3ln43NNaLhV33hAhvC4x6BkawqI2uxWY4xCCAm+GLGNPxzn78kc1f8bzwxP4Ec6b9LHi6I6205\\u002fX73RMun5l1FwaQnPeChr\\u002f4GEDBZ7YDz3cAQA8EVfZCW+NQxAE+YyAxXpg2swHpg29k9P486\\u002ff9u0s6u0cFioSTIuP4rGZnWrdhca2Wrnyji+3RUiEzo\\u002foY8OwV3YdbVZqVuWkWlDsSEUdAOBwed2uoqoPD58NEwnmJMeVt8urOrsBAO8fPNM\\u002f4f6Py9l2Ppt105wp6REhbCajoavnxzNFrSoNAICGIC9ftTIjIjRGJmYzqKnF4ew85N5HZWf+V3DUvnxv1qz\\u002fm7LYqXlgBADPnt\\u002f3V2M5i0Z\\u002fKGfuzWlTqVuMKT8dLLDPs52TGPHBo5v4XHe5GhoNmZMdPzMz9h8f7zxaUPf1nvzoEPHG+TnU7XyK+lfRX37PPjNmoASlrLDbEl\\u002fonwEfJgkr9PaEFz6t+2ePtZMct+HW88o9S8KuIwcJwlYmv1NtPksOumG01RV3Xj8p\\u002fCcmfQjjkfEa5bOdup+pYbcsaHuJ\\u002fOYU2Uthgs3UNncC+VpOLGg7AIAAWEXXA0rjQWrzAIJ4i6ihkRawY0NvLR9sVtpBZykqU9w+KXwrg+bVmUZvLQWAqFE+5Srb3qu+5yUZd5nWcrFN+xW1jaRZ\\u002fX648BoGTUJtuCyQ7wsAYMPVJfIbTbZGShwBdCZdSkO4KK5DcTWldchMtqZS+a1mtIXa4FaH7iejrTYr9Au62\\u002fJWZAE7DiEo8OYGZycLomr1bQAApVV7T\\u002f4bC0MnTw9Kj+aFCBhcAAgLZlPb9O2m7jJNU35PpdbWdyGRKoy+Pm5Z33NBEwY8bKBAevfAkwAAgcTbs\\u002fag8EVcagiCIAiCfOdweZ0jYX33ohkPLZtDv9yLXG00P7f9wIGy2jaVtu8B\\u002fWy7UPrVHb2VXt7cd\\u002fLzY\\u002fkAgH\\u002f+uidOJv3pvmuFHLYNw67+8Keqjq52tbZOoUwKlTkem9\\u002fQeqi8DgDAZTG3P3xjtLS3\\u002fOnCtIRrZ+Ze\\u002f\\u002fHPlR1dOEG0qbRXTM5wPMph+DsP+cOpjobvqy\\u002fZl1+4cHBdXEYQxy+XSQEgV+k\\u002f2H4KAMBiMv579xr32XYHOo323C0rLlR+qTdZ3th6bMGkRJnYj6NnPCfcC1SHKREaQrsm9gmfZ9vt+AzxNbF\\u002f\\u002f6TuHziBkeOF6mOUhHtdzwv9M1kIwgziLuAyk1l0GYprLahcbT5pQTvsrVZMUa64ewjJrFrlv\\u002ftnwBGEIeHM4jFTWPQQAmBWrMtgrdCaLxGgb88JAq3ufhJBGKH8K0kPdSeQr0VhwToAAPXK\\u002f1Cy7Sx6MJsRzaQHIYBuw1Uma62tL\\u002fuJSLmL+jYdHQJ2bEQIrm1Rv4\\u002fiOnKQTuPzWRl8ZhqTHsSgiXDCZEHlWsslg7VvOJWdwVrdoHolRfYSJe6SDetp136vt5YDACSc2WLOTAAIhWGHyVbv2MaCdiiNB1u0n9hXecwUGW8ZgyZUm8+qTMcdm2GESa7\\u002fPUp0uyNCEcj3BQCoVDxEybaLOdOjxXeLObPsJVYAAFZMoTQeaNF8Yr8tRMagiTiMWC4zlsOI4zLjKa0UVkxRIr+p\\u002f5PwmElizgwWPZROE9gwlRltUZtP2bAe8jYac35l96NZoZ+Sg24E7DiEoMCjI7Rns29+ovBjhVkFAMAI\\u002fLD80mF57wWcG3nS1Oezb2UgQyk8BY118LCBAillchw15DtRiaE9ctjDHYIgCPKXT46ety+szEl9dMVccpOEx3nzurVb3v\\u002fB3j99ILkxEY666lfPyLEn3DGcuGPhNCGHDQBg0ukrs1OqOroAAHWKHnLC\\u002fUBZ7ww6183MdWTb7ThMxkPLZj\\u002fw3V\\u002f2ze5d7GJQwvB3HvKHZr3asYwReLtRO3YT7nvPVVpsKABgfk5CpGwQEzyI+JxFU5J2ni43WWy7z1betNKP3fw9JNzl5uYuSxslmCddGsVNpgR9KJKbOFW6LL9nHzmosso7zY3hnHj7qtZS0NEvKx0uvCZB+vf+\\u002fXbV5rN1yueNtloAgNFWB2yeq1CRdRv3duh+IEcQhBkjvjdKdHv\\u002fPrw2rKdF81G79ltSKhyv6X6Sx0gUsD2PVgjka\\u002fVnRtu7DLvbdd\\u002fbVxk0YaTo1hD+Wh6T+t9tRlt6TMe69DsIgLLoIZTWkRXIY4NO44cLr2\\u002fVfAIA4DITgnkrZbxlQnYucDXnts5SXNvznN5SQg7K9dtixPdxGL0nQvfqev4NAEgJfjlccJU9Ei2+q6TzJq2lwLFNveq\\u002f9mxytOiOhKB\\u002f2vckWnx3fc+LbdqvHZupTCfcJNwD+b56TEfV5jPkSLT47gTp3ymvxaKHRghvCOGvK5PfrbVcdMS9L0lvV939T0q2XcpdmCB9gs\\u002fqf2cel+v\\u002faFS9ZsW6HKEe4+F27TeRor6ZDwYSyOMQgkZEFDfkg6mPflL71yH5JfJkmANJ4EfcGL98YWgu4uqbBJog4GEDjQ9PfHAbNQRBEARBPtLSoy5p7bQvu8xo02m02+ZP++eve6kNJNnR4Y7laKmYz2YZLFYAwPSEvh\\u002fpUdLeTGW33ugIAgAau3p7nqVHuMj2pF0ONnarnFsA8NHOQ\\u002f4QJ5A6ltl0RoxA0tc21hwv6u14OiU1yrnFs+yE8J2nywEARwprRzLhXm9wSqLZzQkeYgdq780NvpKScAcANBjKLifc8Trlc8D5d1pi0NNRolvJEQcJZ9aUyL\\u002fK5Heqzb1TQ3jPiilqu58mRxg0UU749wIWtcSVHZMelBj0lIy3vFR+h6MCO05Yq7qfmBK5w30B6EC+lktac363YY99OYi3JC34NQbN6WamA4cREym8MVJ4I+ZFlfnACtyxYRclusWCtocLr5FwZlHbnAnZk3LDfy6R36I1X3AECQLtNuwmTXbqQQh\\u002frSPbDgCgIZwk2XMF7RscEXs2WcSe4si228VJHu3Q\\u002feiYapWcs3YpYO+rVfMZeVXMmdk\\u002f2+7AoIkzQt+70LYcw3vrDHRov48QXs9jJjlv6FqH7gdyT38AQKzkwTjJo+QICS1MsEnMmVHSeRO5\\u002fkyD6tUg3lJP9xICfRxC0IgIYgmfzLzhjsQ1x7uKyzQNjQa5yqYzYRZAAA6dxaGzgtniWF5YkiBypiwjhhdKfTw0IcHDBoIgCIIgyI3C5t4B0OFiYVp4sHNjr7kpcdSQswixU5dNMZdjsFjpNCRS0tcdmM\\u002fqzRrZOws72LDeGXQ4TBc5Q87liu047qLzhE92HvKHuRHxN6Xm\\u002fdlYzmewnsxbLGZxqFuMHR3K3oGGQaJBd9KXCHrrKHR0+7eukYs\\u002fHrI2YzUlEs6JD2EP+gbCYMnYEZHcxHZTX60MAECbsQbIAACgx3TcXljDIZR\\u002f5UCZLDsawsoM\\u002ffhi+6r+pSTca1K\\u002fSyqfAgCgZYV+OlAG3EHMmZER+m6Z\\u002fC5HxGira9d+Ey3ui\\u002fQXyNdyyXi5X22YYFNq8CsAeJ6azFHxY5QI5LFhx6KHpoe8RY0OgIaw04PfzG9bQhB95zON+bw3iWm7SNHNlIiAlc1jJjn+7+yixHdSctZ0mkDInqIxn7OvYrjRinWz6K7PfyBQ7wvDjZTUf4z43oGy7XYsemgYf6NjHAYBsHbt18my\\u002fzhv5QKK6xpU\\u002fyNHQgUbB8629+IwotND3i7suAqA3msOnLC2ab9y360+8MchBI2gUI50S8zCLTELqQ0QNDB42EAQBEEQBLlUK1faF1LC+sq8UMgEPBGXozWZqQ2XiXlO6VQ6jQYAEHE5NKTv5zbtcml1FO\\u002f9tWvn6Plef7mrO1l9V2\\u002fHdsdmZD7ZechPXpix8oUZK6nRMahH19v312J1ulfkDbO1d1pEld6\\u002fHYg9ZFSV1t57Uw7x\\u002fCxKxE\\u002fi+dmUiGNnFPo\\u002fyXEEYcRLHydHXKLTeHGSh6hRt1BcQ3mtcOEWEWcaOTKQIO7iYN4qcqRV+xlOWMgRskC+lnt8Vlqy7D8ej43RKWDHxpCxGZEy3nJyhJKZdYOGcISsydQoAEJ2HnkVQZhB3EXkiB2flUpetaBt5NVhGtr70loukXP0CML02KEeACDhziev9jh3Wh+IXP+bo188AIBBEyUHPUdqH5CQnRvCX0OOyHW\\u002foHjvDVWXRv9xCEEQBEEQBEEQBI1CmsuZ6GCBuxkdJc4pdQoWnU4NAcB0FexveXaKfeGHM4U6MzWz9MnR3m58izMSnVsA8NHOQ5B7Qh7bvtDQ4eKekHuOhwi8m2p1yDwkVdXWvuLFdiFs94UUfKb\\u002fC2msXQAAjDBRpvQM4a1hMyLJkYGE8jfSae7+5ik6db84SrUAAACgxUkeIa16ECd9jLxqw3q6jdQ6OQ6BfC33EqRP0pDeY3dsCeSxMRwSzhzyKuo0rMEdHjMRcTVlHJcZS17lM1Nc\\u002fg9Squ17\\u002f7peGsL7smIK8iqXEYcgTHLEJUoBGQvahuF6csQVgjI7QoTwejpNQI64ESbYTF7FCJPKdIocIRsrxyEEQRAEQRAEQRA02hgv98Blu6ro4sB12+rovU7mIuTKgtSEvPgoAECXzrDh3e+2nisuaG6v7OjaX1pz4ydbT9U0AQBCRYI7FkynPtJHOw9B7oUH9VZM2p9fZXUuiOSeFcX25\\u002feWcgmTOpVd8jkPCXcrTh3iIWRKKRE\\u002fETCo1cMtuAkAoLMUOCemQRBvCXnVDQRhSDnzqNGBUbJmYs5UFj2MHHGPx0zis9LJkW7DbvIqWSBfyw0eM0nKHcRHNKoE8tgYDsoktDhhpez2QNgD1A2nVIbpP8mtHaUiv8\\u002fr7w\\u002fhfVGS8gyaiyFp\\u002ffXfzLkWkwt6a5nJ1kiOBDt3WndPzJmOAKdbHVpLPnmVbKwchxAEQRAEQRAEQdBow2X29kKzoO4yiairEuo+gSDgvRuuyI4OAwB0qHX\\u002f\\u002fvPQDR9v3fTe94\\u002f+uPNSUzsAID5Y+sXtm1z2Uh\\u002fxnYcmgtlZ8faFzh7d61uPEV4fTW\\u002f\\u002ferxD2Vu6fVaWf+cS8JBwRy9PsejAHPxUnEPDoFFfyIZbAAB6awUlLhlMgljMmUkNDYAgbHprKTkSxFtGXvWGzPkhavMZAmDkiF0gX8s9l6VIxoqAHRvD1D9fjBO9N4HdG6jkOg1xOs9RerI7IMCp8ziOU4eGDdMQ3hdBOJWKw4GH7e1wgnojkPIJ9Kc1XyKvMulSASuDHHGPhnC4zHhyREOaIZZirByHEARBEARBEARB0Ggj4rLtCyqDux5sjuIt\\u002fiDlc7++86rEkCAAAJvBoNNoTDo9WMiflxL3743Lfn\\u002foxqRQ1yXaR8POj2+ItwMVxrOVM9IcsxFsO1b8+Ad\\u002ftSjUTlv006HU\\u002fuOjnVsPF9pXaQiyZuYgkkJD4GEQBwNhWZ1zWzZPGTRfQXFqrp+OMAAABufC0Cx6CJMmIUfco2TN3NBbyyhl0AVMpy7k3uA7J\\u002fUw3GCy1fOYvfWwHAL5Wu4JOU7VwMeWgB0bw4QgHv7uBtI\\u002fo21HyaQz6a7PfJRyNARwd8N5CIbwvii3EKyonLw6EAvW6RygMekeRt5oLQXkVR4z1evhdL1Y9BDyzLQWtIPU6GSsHIcQBEFuvFd45vVLJwAAF657IITLBwBUqrp+qCw609HUYdARAIRw+elBIYuiEq5OnUQnzb51sr3phr1bAQCJ4qAjm+90xCkWb\\u002fu8XtMDAPhh1TXzIvt6lzge\\u002fvGSDavjU1Ec\\u002f72ubEd9RY1a2W0ychmMaIF4bmTcLRl5MULqUEgIgiAIgqBxwJHLdkxA2p\\u002fBYlXq+6Yo8zmzDb3ji231XT0ZkaEf37IhROht4dPRsPP9lSg71u\\u002f52r58fvPD9otbAIDWav6tvuRIW12NuqvHYmLS6CFcfgRPODMsdkFEYm5wJPkqtz8jaj3aVn9O0VzeI2\\u002fRq7U2iw3HBAy2mM1JlQTnyiLXxWXECT0kKyiq1d07GsuOdTR0GLQaq1nG4UULxMuiUq5MyArnCQEATJqHntN2GIEn\\u002f\\u002fAqNXpZOE94ZtOD1Kh3zBh6qLXmeHtDkbK922zQWM1iFieEK8gLjloanbwwMsn9h+YTSZGyqxbnOrLnx4rqTpTU5yZFzsqMi48ICpUIuGwmDUGMFqtSY6zvUOZXtlysasVIkwNvWpCTFOU6deYrHhJkLBqHUlXGiPX2vfc3A0qdk5BN5wIAKHUhBpuc8n57g7WKEuGxBpe8BgDw++W7Ddaa\\u002fknwQL6We1yGUzXwsSVgx8ZIodN41JArLgu4j04CdhZ51YopzGgLhxFDDvande5dzmelUuq99EcZQcIZ\\u002fHFOpzmV90JxLQC4y0FC4\\u002f44hCBoQpEb9SFc\\u002ftsFp94pPI2ThmsabNZGraq0W35dWi5pc5+pVSvlRv3dh7YXdvXd4LRZsfIeRXmP4tuKgtfmrboyKZP0CAiCIAiCoPEgNzbCvtCq0tQrehJDg5zbAQDgfH2L92U0huDbU5eKWjoAAK9ds9r7bDsYHTvvXqdJZ0+4\\u002f9FQ+uz5\\u002fTpbX+dXG4416axNOtVZefM7xSdPb3oggue612O+ouWryguH2mqsGLWwhNpqUltNTTrVgZaa1wuPbUjIenbacimbS9msPyNqe7XgyPfVl8iX3J1GXadRd0HR+nbxiScmL7wtfbrEi6fyEwKAX2qL3i4+0WnUkeNKs1FpNlaqFD\\u002fWFKSIg\\u002f81dcmiSKeJ9\\u002fzhoU3zalq7LlW32VdxnCioaSuo6V11Lzc58pGrFlCjvuYh4S5mButRNTmitLSTV\\u002f2nu98LiRgy0Jvn6jOoQucAAAbN25tL\\u002faZ8pLHooc4Rz1iM3u8aBwvmomNsIF\\u002fLPUqZ77ElYMeGGwRhM9pqDdZKo63OiilRXGXD1RhuwAnz5X+WIddyQQC1zpJLiB\\u002fqPvnpffGYKRxGjBltcUTatd8lBv2LtAkVQWCd+q3kSBB3MXnVJRvWOw+1nVz\\u002fq1z\\u002fKzkyeDiKaxmuuq6PhuPQSzhOVLd2Vbd0Vbd0tXdruzT6brXBZLVZbBiKYnQ6jUmn8TgsiYArE\\u002fGiQyWxYZKMuLDMuDAu22lQBTRSOnt0ta3djZ09DZ09XSp9t8bQozOZLDarDUUxnEGnMZl0FoMu5LKlQp5UyA2XiWJDJXFh0oy4MLHAQyGm0UOtN5U1yhs6lC0KTUe3pkdnUutNepPFasNsGAYAYNLpLCadx2aK+ByxgBss5kfIRJEyUVy4NDkyePS\\u002f01H+BhVG\\u002fVvNtW8XnAIA8BjMFEkwl8mUG3Qteg2K40ti\\u002fHUxXdzdeaC5tqi7AwEgOzg8RSJj0uj1mp4L8lYCAAuG\\u002fu3E7gRx0KTgcOojId+ZIF8yZDYUK67rqGiSN8lVTZ0qhVpvtNhMZqvZhjLodA6TweUwQ8T8UKkgJlSSFhuaERcWGyqhPsuoodGbz1c2VzYrmjpVzQqV1mAxWqwmi43DZPA4LD6XFR0sToiQJUXJpqfHRMhc5xTI6K6m4BtBE\\u002fAQhcaiUX6iH6zOHt3ec5XnK5ob5SqVzkSnIWI+JzxINDklclZm3PR0D32nKAgCnC1vOlXSUFjbrlDrNXoTg04T8jjRIeLUmJAZGbFzsuNZDA+dq8afhGBpZmRoebsCAPDRkbOvXeNi+rFvTjkVTfW507VNAAAGnRYnG9zv09Gw8+4pjDoQFP5+6ek3Co9R20jSpaEDZdtb9ZprD\\u002fxATou78UdDWaW665cVNwqZ7npGGlHbbYe3nlf05UYozBj64sVDtRrl8hivutUiAJkXkaCyGHvMJrXVZEKHW63EimFPnNm5o9FpQH9\\u002fNZru2w7\\u002f8uik+Y9MGkRl3SHgspnvPrzx+a\\u002f2HbxYQ21za9GUpP\\u002fcsZrn\\u002f5yGh4R7ECu8zVRLjjQZqOWJ\\u002faTRWEaJyNiRAAAM15ODdGRw93boiFd9hAEANuesmZediynoCBcAGgB9wxZQTEVq7xXI13KPThvErcvRJmDHRn8Yru827u827lWbTuL9Zj7wFUpNmIF47O7tvQC8rwjRjQ09LztW27XfSLnzpNwBbzbWq14y2ZocqzSEHSG8ltTuEoHiTjdgfQLDzQwXHdxH8jj0ksliO3Sp5lhhXX5li8444G0SHMVsKGa02Lo1hto2cK6imbrFMMzOinv\\u002f0U3UKOSFho6eM2WNl6rbius7lBp3IzGtKGZFMQMAKp2puV9Ju9hQSV5q9ILJibMz41hMDxcDgWe1oWfKm06VNJ4ta2rrpo54o7DgqMWG6owWucrpr88uWMzPSYzITYqckhqVGRdGGx3ZojH0Bn+pKdnbWB3OEzw1Y\\u002fGa+DTG5UGsepv1QHNNisSpMpgP7W+uAQAkS2TvLboiM6ivF0JRd8dt+7cpzUYUx98pPP3FsqF\\u002fk1z\\u002fwvdVLV3UKMmyaamv3rOWGg2Uzc9809jpdLeYYkFu4lsPXkmNDtsY\\u002fZIprG2749VfqFEAAADJUcFbn7+JGnVmRbED+dX7L1RdrGo1WVz\\u002fIrXiqNWGao1meY8ONPTFI2WiOTkJa2al5yZF9kVHlNZg3nmmfPfZyqpmhct0gNFis5\\u002ffmzpVp0ob7cH48KAlecmbF04KD3Ia1UfGY\\u002fu+V8dgjdFDdGL640Tpf749QI0Oz45X7oj04ubQiBsTJ\\u002fqB\\u002foMeu3rBjcunUqMAaPTmN389tudsJbksAwDAZLF19ugKa9u+3pOfFCm7c93MFdPTyBsM5PCl2o\\u002f\\u002fPF3X7lR4BMVws1XfpdYX1LRtPVwoEXCvXpx7y6rpHNbE+ju9a+H0x37aBQDYVVQVK5Pcv2S245an0Wr73+7j5+tbnR7ga2wGAwCAYvj\\u002fdh+7eW5euFhA966SCRgFO++e3KT\\u002fta7YkW0P4wqmhcaEcQVcBrPHYqxSdxV1d2AEvjQq2flxfaIF4pUxaXuaKx2r00KiM6ShwRw+h87ssRgLu9v3NFcaL+e4K1WKNwqPPz99ed9T9PPE6Z3kbHskX7QuLjNZLOPQGd1mQ76i5XBbnQVDf64tbNC5uz50oCHId0v7UiUWDK1Wdznq6gwWAcB9x38\\u002f3NaXH44VSFbEpCaJZSImR2UxVakVB1prHD3f3y4+YcJs\\u002f5yy2LG9P3DZzFfvXXesqO7znefKG+XU5n7SYkLuXDdrSd6A\\u002f7O+5eE7K4KbUKI5SY60GKu0NqWI6d9KN2qros3olOgHAERxkwEAqHMyi0YbXDILQegIwiAIz9WrMUoSfKhZMDqNR07AUWq12wXytTzx2fk78AJ2bJDhhLVD932L+kMbdZjC2Baw9xUpvKFD+4MZ7U3mEgArU9wVLborUnQrpcK7wVrRqH6rx3iYHIwS3cFmRJEj\\u002faG4jnwjyt9G5Dj0UodS+\\u002fXeC7vPVhjNfrl9AvkJQYDiuvZ9+VXHCus6e3xz96hZoW5WqP84WcpjM9fMzrxmcW5ipH\\u002fP7F6qa1f+cqRo3\\u002flKN3eDBqVbYzhSUHukoBYAIOJzZmXGLcxNXDA5KQCdGlwac29wT2N1OE+wY\\u002f3NoTwBOS5gsjYmOZUF8zkxm\\u002fPTqmsor5sbHPHczCUPH9sJADje1mDDMSZtiLeZ18\\u002fNeu3no9QoyfHCOp3RIuS5647kJ2WNcvfZdgDA+rk++\\u002fzH95dMY2ePvbMztQEAAIDOaPlm34Xtx0vUendzu7nRrtT+drTot6NFSZGyW1ZNXz0r3TGLV+B1awyf\\u002fHVm55kKq23QFxKNnT1f7j7\\u002f9d78BbmJ918512VhUyF\\u002fBP4cwHg\\u002fRKFxZsyd6Purae2mhgAoqGn75ye7ut3e5QIA1LUrn\\u002fx09+FLtU\\u002ffvExwefLM\\u002foxm63+\\u002fP7TnXG++0g213vTpjrM7Tpe\\u002feu+6rPjBDR0e01bmpK4oqd5fWgMA+OjwuW0XyqbFR\\u002fHZrC6d4WJjm85sSQmTxQVLD5ZR82a+snla9onqRgDAd6cLvjtdQG7iMBnBAn52dNimadnzUuLITXYjvvPuHW+vP97RAABIEQc\\u002fmbdkcVQSZQOdzbK7qXJKsLv76PdlzTqvaN6SNGljQnaaJITSemNq3j+mLLr76LYiZW\\u002fpjh9rCv4+eSGf6fq+9cHWGkf6HgBwfcqUZ6ctY9P7Era3pU9v1WsePvlnQXfbOflQesKx6YwQrtNF9aB8WnbWkW2nI8i\\u002fpi69JW0apVb701OXfVx+5u2iE\\u002fZb\\u002fZ+UnZ0aHO1lf\\u002fzhWJibtDA3qa5NebKkoaJJXt+u1BotepOFIAgBly3icxLCgzLjw2Znx6fFUP+n\\u002fMpDwj2eT72UJwBxVrl7RbiHfiLDdEa5kwDU7hiJgkmUCAAAcVVD2T0aYGJeTBfpu4SXU6YPQVycFwP5WhOH\\u002f44NByumKJPfpbeWURtIEITJpEkYNBEN4dJpXBrCIwiL2nyWut1oEsj3RUM46SFvFXfeiBO9P3QJAmvRfNyi+YTHTGQzoukIFyP0RlutBaXMlQpkvOVx0kcowf5w55mfAywAx6E3DCbrB3+c2nasGMUCd+8BGr52pXb78ZJdZyvkPsov9Ge02H47WrTtWNHK6WkPbZ7vpmOjvzV1qj7YfupwQY2r7pi+oTWY9+dX7c+vYjMZ83IS1s\\u002fNmpMTH7DU2Nh9g8\\u002fNWkrJegfGnVnTXL7u6vg0xondKI5bMaxeo0qTOt2d9d7qmRlv\\u002f3bChlIrbzpYUWx\\u002fftXmhS6uP\\u002f1t1xkPw3UlAu78SYnU6OBNhC8ZFMMbOnpSoqnHCU4Qf50se3\\u002f7SZVuiKl2irp25bNf7v3+wMWnb1qWlRDoYkcYjn+1O\\u002f\\u002frvfkD9dD3Eo4TRwvqjhfVb1k46f4Ncyk3nMT8QJe2mAiHKDRujN0TPUX\\u002fhPu5iubH3vvT4vWdvAMXqjt7dB8+tonHcZFe1BktD72zvaR+EPVvO5TaO1\\u002fd+taDV87KcpHeHa9eu2Y1DUH2llQDABRa\\u002fe7iKkdTRmTo+zeu31NS5b+c9YK0hLW5abuK+l7UwWxDW1WaVpVmb0n1xqlZ\\u002f9m0vP9BOLI7796+lmoAwJzwuM8WbeExXByiQib7muRcatRZjizi3OaH6MiAP\\u002fZDuYLPFm1Z+OdH9louNhw7r2jpn9y3e7fklGN5WXTKizNXUT9QAKIF4m+XXrN61xeteg+DZnyux2x8q\\u002fiEY\\u002fWVWWu2JLm4PGbR6Q\\u002fnzOMxWC9dPGSPPHV+z8LIRBZ9iJ1jBiUpSuayr8AI8pBwj+Im8xkiA+rU\\u002f\\u002fqscue0oGVBrAhy0IcUlpbzyr2UoIQVGsFNAAAwaHwr1ndZjF1O0nmPAAP+siKj9EvFCA\\u002f3cgdAYLjTHjJoLkbABfK1xrGAHRt2Vqy7sGOLBaVONsCky4K4i8ScmTxmMpeZwHCebBMAoLMUFnZsoQRHj8C\\u002fLyE7Nzvs84quB21ORZAIo63OaKsjRZyEC65Kkj3vTf2c\\u002frPIynjLJZw5lOBgMehiaggAEPDj0Bvnypv+\\u002ffV+l2NUoVHrVEnDT4cKzpU3u6wG4HMEAfaerzpaWPf36xZvmJdNbfYzFMM\\u002f33Xu6z35blKfvmWxoYcu1Ry6VBMWJHzu1hUzM2KpW\\u002fjUmH6DYjZnVVwqNRoQa+LTqCEAAAAsOj2MJ2jTawEAasugv2MdxALOgkmJhy7VUBtIdp2pCHzCHcXwfedd\\u002fL4lWzMrY6Au216aUF8yNa1dlIS71mj+16e7z5Q1kYM+Ud3SddsrW+9eP+uONTP75R\\u002f8pa1L86\\u002fPdpc2ULsmDBmOE78cKTpZ3PDqvesySV1KJYLBjdsbjgl1iEJj3Zg+0ffX0K7EcNxRP6S+Xfn4B395n223K6nveOLDHR88tpnyTWi1oQ+8\\u002fXvZ4L+vrCj2+Id\\u002fffXPa1MD20d1BDHp9DevW7tpatZvF0qLWjp69CYhh5UQErQ2N23ztGwmnZ4YEkR9jI\\u002f8dLbo3QOnNSYznUaLDRKHigXMy8eDDcf1Zmtjt8pgsQIAtl8sy4wMvWH2ZPLDwYjuvDdkHN778ze6zLZ7z0223S6Ey18Vm7a9vtS+WqdVuky4V6u7SpS995\\u002foCPL01KUDXT4ImOy\\u002fTVrwt9M7qA1+9m31RQvW+w0wNzzeZbbd4Y6MGTsay4uVHQCALpPht\\u002fri61OmUDeaGDwk3GkIPUc8\\u002f6xyFzlow61bm1+\\u002fI\\u002fElFs33fRzMmGFr8+soQe2akSddggAEAECnCQDWd8cVd04xe4QTVi9LUTOdpy7EcBMAxGArrlx+VB+Xs5IG8rXGsYAdG3a1yqcpWWkWPSRe+ngof6OXxdZHpxF5X2LOzLzIXY2qt+T63yiHcX8CVnas5CEZbym1YQB0mgABdHIWm8dMjhT5a5hOgI9Dj77ff\\u002fGdbSdw3MOnCo0eu85UfLMvv65NSW3wP7MV\\u002fc83B0rrO\\u002f9109L+HVX8pEut\\u002f7+PdxXVUW\\u002fyBYZCpfN3Kdix\\u002fgZzZGEBOxjI2HRGonjAX2KOMbY25zKyg7V+bpb7hHtRXXuLQh0T2FkxT5Y0eCxvcsWcTGrIaxPtSwb0663Z0NHz6Ht\\u002ftnapyUEfwnD8oz9ON8tVz926wvuKt0N2qbrtsff\\u002f1Jt8U7yCrF2pvePVrf+6aekVc3oHPQcm4T4BD1FoTBvrJ\\u002fr+rCjW1KmyF1kyW9F\\u002ffLxzaENnzlU0\\u002f3y44LqlTrm2V388MoRsu53Ziv7rs90\\u002fPXsjcyJNozovNX5eajw1CgAAYFF6Yvl\\u002fH6MEb5g9uX\\u002f6GwCw74nbqKEBnuGL4xfe2HsCALAgLeG5K5dGSKg97QAAGI6\\u002fte\\u002fUlycuAAB+u1Dq8hXB4Hc+YG5Lny5lB+KMlhMU7ki4awboJnKkra+XYV5IdJzQKT1IsTI2lX2O4Uh\\u002fB8ZfpIlSb0qbSmpxAbFX1DnTm0b+ra5kwibcPV8CzpCttme6ydpN9d82\\u002fseE+bi\\u002fpB5Vf93wvMLcTImzaJwZQavty3TnfrVWrIu86hHqXC3dDTaD0oUf71\\u002fRwiML1kaJUHLrdoF8rXEsYMcGAEBjzlcaD5IjHEb05IhtYYItHrPS\\u002fcsljR4j+L5oCBsn+m4a0Wk8Fj0YQRgIoDNoEg4jVspdGCd5dHLEtimRf3ifbQcAIIDOdC4Hb8N7yKu+Fcjj0KP3fj\\u002f51q\\u002fHYbZ9bNmXXzUiWQaH7SdKnv9qX0A6FIIWhfrWl38eqd+oAIAZ6bF+zaWOgzco4wxxXplhknG4Ach2zcmODxbzqVFnOz1Vd\\u002fG53WcqqCFn6bGhw+nfN6G+ZOxqWvvOxS0K9d2v\\u002f+q\\u002fbLvDrjMVz3y+199v80Rx\\u002fYNv\\u002f+6PbLudFcX+\\u002ffX+344W2VeDhIH4QpiAhyg0do2DE71LNW299ym\\u002f3H2+oWPoP53e23aSXPb9wIXqP072Jh+HpqGj55cjvd9IkD+YbeiHh88CAERczhvXrnGZbQcA0Gm0+5fOsl+pNXWrqM2j3tq4dGrIP8Ssvm7KFsz1CJiSnr7s3\\u002fyIBFKLCzwGKyuob+RZACjNxgZt75cAHUEWRiY6t7uwLLqvbnuxssMxeexE4znhHsKOypbMpUYBaDKUf1T7RL2+hNowVJXa\\u002fA9rH28zuajiNCf4Cj6j97Ytl+E0WsqENpBXPbJi3iayecy+Q8TOaHOxb+4ZrdSH8FkZlAgI7GuNYwE7NgAACsMflEhq8GtshrtZNRxQXEMNjRoj9b6smKKgfWOXYad9VcqdPy3q4MyYs\\u002fPiKufFV82OvTA9+nB22BexkgeF7Fznh3qFx0omr5ps9eRV3wrkcejel7vPf70nnxodHdgsD+OrJrJbVk2jhgJu15mKr\\u002facp0Z9rUutv\\u002fu1X301\\u002fd3QbPJntZDx8QaZAam62B+HEYh5aGg0ZO1sDxdLe85WBjLvpjWaTxR7OEkNc7rUifMl41B7OXurUOvve3Nbj9bo3O4v+\\u002fKrPv7rNDXqOxerWp\\u002f4cMdg6zwMFkGAl384vO1YMQBAKgpEwn0CHqLQGDU+TvQu2QcGtXVrvtt\\u002fkdo2GBYb6ngGo8X25tZjzu1DEcjSPRNQrVxpstoAAOkRwXy2u4orbAaDhtDsC9S20U3M4sQLBxxG6VvedB+p0fR1C0gWy0gtriWKPG\\u002fjQ\\u002fbiMHYJoiAOaSrXgUjZ3HBe760ajMDLSHcUJhTPnxQAYGX4LdXai5Z+hRFUVvlXDc+mCqfOC9kQz8\\u002fq3xHeGziB1+oLT3ZtbzC4vtUZzI5aGHqVY1XAyuwy9JW4saByDDfQaR56JzkYre4GDpPxWek0hI0TfR1G9NYyKXc+aRPP9FanXlFMuozLjCNH7AL5WuNYwI4NAIDGfI68ymeliTnTyRE3bNhI9tlxb0TeFwGwUvkdZrR3aIuUuyAr9FME8erbyUtCVo7adMqxqrMUE4TNT7MKB\\u002fI4dONMWdOHf\\u002fS9ZfeSImV5qdHT0qJjQiViAVfE5zDoNK3BrDGYu1T6gtq2guq24vqOIVzaIgiQifjRIZKYUEl0qDg6RBITIo4OkYgFvq9INm5MTY3OSggf8mBbX\\u002fnoj9PT0mImJVEGYPkMiuFPfLhDofbxULlBCRLxFk1OokZ9ZNy\\u002fwWEyj5quLuvnZn2z9wI1StLWrSmoactLjaI2+Mf+\\u002fGqr2y9bJoO+euawumVNkC8Zsi61XqM3C\\u002fnsf326u0Ppy5FkHn256\\u002fycrPjcZK\\u002f6LgxKY2fPEx\\u002fuCNhc6K\\u002f+eCQuTBoVIqY2+MEEPETHq6yE8NvWzFDrTGp93z+NwTw+Bl+O7xO9fWDQ13vyrcO+pbftWPHd62bxuawvd5\\u002f3ycfVozMeulS7akYatQHyBUd+2OCpjtCekioMxwEAyWEBzf8On4zj7W9zb2is5jOdTVXqrlpNt9Js1FjNOpvFgqGOf9QH9KOxmB3LYVzXQwrIgtiBuPntoLL0dVMIu5xG9yiMK+w09t6M7DYHqKODg9FstV9O8zmsEaxA5VVKS8wMXhlxy19tH1MbAAAAVOsuVusuSlihacJpiYKcKG6ymBlM3cgZAQi1VdFqqqnXF1fpLupsA45RYiDMzdGPMEh5MT6LUrOSUJtPyXgrnIMDomSl3aAhbDFnusp00hFRGg\\u002fGiO8lbeKZ0niAvCpiuy5dFMjXGscCdmwAAKyonLwqHMynrbUUUEOjxoi8L4X+D4O1b\\u002fh8UtBzvs22AwCk3AUtmr5vMJywqM1npNwFpE18JpDH4UC0BvPTn+\\u002fxpkvm3Oz4O9fNcvljMljMDxbzkyJls7LiAAAKtf67fRe3nyjxWMORx2bec+Xs6BBJTIgkKkTMgZ3ZB+\\u002fmldP+7+Od1OgAWAx6XLg0IUIWGyYJkQiCRXwuh8lhMiw2VGu06I2Wbo2huL6jpL5Da+i7mPMIJ4hXfzz83dPXe9MvYwi+P3BxUPP7RQWLM+JCI4PFUcEiqYjHYTLYTAaTSbfaMIsVVWqNPVpDa5emsVNV36H08p1eOS97mNNOujHu3+AwqUk\\u002fLUZWfHhQTmJESX1f553+dp0pD1jCfZenCjYLJyeJ+MO9ZzkRvmQoalq7Suo7CmraqA3OmAx6ZlxYZnxYdIg4PEhkf6d6s1WjN2kN5oaOnvyqlqbOQYydxwni31\\u002fv\\u002f+XfN\\u002fv2j9FiQ\\u002f\\u002f2wV9a4yA+cLGAMyM9NjUmJDZUIuSxOSymxYaarDaFSt\\u002fWrSlvlJfWd7rpLI\\u002fh+P99suvZW5dTG\\u002fxjAh6i41JKdDBlvmIAAEEAndGscqTgdWZyOv5Ecb03V7Cjwfg+0de0dqt0pp3OJc4kAu6yaamTkyNDxHwrijXLVbvOVpQ3Ov1+7M9ksR0trFuQm7j1cCG1DYDJyVHzcxOSImUCLlupMVS3dm8\\u002fUeJxHNLec5Uw4e4nSaEyHotptNrK2uQ\\u002fnS26bpaL8eUWFN1+sex\\u002fu4\\u002fbV7dMz3ZuH+34PhpGeaKj4ZOys2flzRgxrJvfOltf11ueF\\u002fvGZ3rexoc01r6vIyGTTWpxR8jq21I9QPF6HzJb0b3nK8+XNxfVtSu1RkdPwTfuX79oil\\u002fuSnrD2zzI9KCVbcbaiyqn4s5kaqvinHL3OeVuAACbxpWywgRMKY8uZNLYdIQOAMBw1EqYjahOh6pU1k4b7tWsgBuiH4jmpZAjQvZkGsIiTyqoNB72PplFyUq7F8xfS06C6yxFZrSVw4gmbeKOwVpNqQwTzFtJXiUL5GuNVwE7NgjChhFOXxnel8snAKYy9Z6ZRpuRel8qU9\\u002fQQgZN7I+RGSLOVBY91IopHJF27bd+SrgH7Dh04\\u002f3tpzxOuMfjsP571+r5kzyXYLMLlQgev2bh9cumPP7BX1UtXdRmEqPFFiT0V3ebCWJJXnJMqKRFoaY2XMZhMaamRs\\u002fIjM1NikyPDfXmvj1BgKoWxU+HCvadr\\u002fJysEJls+LAheqV033\\u002fe0ajN3++02k8zUCSo4KvXpw7NychPMjb\\u002fhQAAHvy6GJV68Xq1vp214NvEARsnO+vnwfj\\u002fg26wbw8RaR1gDqVAIB2vXZUFXNcPzfLfcL9wMXqf1y\\u002fmM309rJ5yFoU6uI6d3sChl1Pxm7cf8n09\\u002fIPh93XbZ+bHb9uTubCyUke\\u002f6OVGsNfp8q+O3BRo\\u002fcqI9YkV\\u002f15snSzT8s+vPvbCe\\u002fz\\u002fvMmJVy\\u002fdMr0jFj3iWMbip0sadh5uvxYUZ3LjKdab\\u002frb+39Ro\\u002f4xAQ\\u002fRiQNBgIjPEfE5cWEufmhMv+dtwuXxN8qM+xO9vEd3+ys\\u002fO7q3Iwi4ccXUe66YzWX3ZfrmZMdfu3TKrjMV\\u002f\\u002f56v72n80AOXKju6NEazU4poISIoOduXZGT6NTvZ9m01DvXznjmy30HL1ST4xTnK5rNVhR26\\u002fEHDpNx58Lp7x44DQD4z1+HfzhbOCsxJlwsZNBpVhRT6o2N3aqCpna9pfd\\u002fc2VO6pVTKB3Oxj8jav3bqR37WlwcpTQEETLZQhabQ2dw6UytzdKk83C+Jn\\u002fluT9T2zFpnk95PkSuZeL99zN5Sy\\u002fe09ARBPjp0KUvd59X6TwkQAAAVhv60Lt\\u002f2K8B1s\\u002fN2jDPX1+hdoP4hroi6h4Dpq3Uei5mZ8FNneZGYG6kNgwGApC1kXfmShZS4gyaMIi7uNu4zxHpMuyIl\\u002f6NRQ8lbeWaxpxvRlup0YGF8NY00P6L4o6ibEST+u204NfJ27jRpH6LvMqiBwfz15IjZIF8rfEqYMcGgjBpCAcn+n5lYYSB1O6OQv+nBW2nRkeHkXpfFlIeHMV1JlsDl5lAavcBBNDDhVc3q993RHpMx1Smk1LuPNJWvhGw43AgzQr19uMl1KizUIng\\u002fcc2JUUOevRfhEz05T+v\\u002fb9Pdp4sbqC2kbz1y\\u002fF5kxJEvOH2wZywaAhy44qpL39\\u002fiBIX8TmLpyQtm5o6LT2G5UVygQxBQHps6L9vW\\u002fnwpnn\\u002f\\u002feHQ0YI66hau\\u002fHSwwB+Jht+OFXkcKhEk4j1987KFuUO5cxMVLI4KFi+flgoAkKv0J4vrTxQ3nC1vImdYZmXGRQWL+x7jU+P+DbohZvf+4cuNeiuGsVyVgD\\u002fc6qFGeYCtmJ76+s9H3fTtNZisRwrqAtCZbtfZCmrIWYhEMDvTB7elx\\u002f2XTH+NnQMOqJ2REfvolvlpsZ5P03YyMf+2NTOuWTrl6z35X+4+581vzy92n984P4dG880vzkvVbVuPFFKjrsSGSp66adm09BhqgytMBn3xlOTFU5KrWrpe\\u002f\\u002fnIpWoPowH8agIeotDYMhFO9M2kO15P3bRs4\\u002fycvjaStbMz2Ez6\\u002f33SV1Gzv5Ml9adLG8mRyclR7z26kUdK3zuwmIyX716jNZjPV\\u002fRWHO3PYkOL69pnZDhNnQX5yj2LZlpR7PPj+SiG1yt66hWuT6BCDvuOBdPuXDid2jDe4QRxx5Ffz8r7js9EkeyK+IwZobGJoqAwnpB8sv+huuDp83tJAReETLbpck8UA+q5a3KAu61I2FzHst6L3bPT2\\u002fq2FLP6nsG3jGbrk5\\u002ftdp+aIGMxGUIu+0hBLQBArTf5O+E+iPFHdIRxbew\\u002fJknmUxv8gI4wNkQ\\u002fMFO2htoAAAAgVHAleRUnLI2qN8mRARANqlepMbfoNH6k6DZyRKH\\u002fU20+TY4MRGk8ROmpGi68joawyBGyQL7WOBawY4NFDyGv6izF5NWBWNCORtVr1OhoMiLvi0OPJK3hxZ03tGm\\u002fNtpqMMLkfLt3WKJEtzFoIlKAqO7++5BvErgXsOPQpR8OXMLd\\u002fvqn0ZD\\u002f3r1mCNl2Ow6L8d8710SHSKgNJD06o5e9fqCBrJ+TGSTk2ZfpNNqC3MQ37l9\\u002f4I17nr1lxZzs+MFmGchkYv4b96\\u002f\\u002f29UL6Zd7IrtRUt9RN0C\\u002fquHYebqcGnKWFCn75fmbh\\u002fYblSJMKti8cNLbD1158M17\\u002fn3bytlZcfaeI77tbUox7t+gG4miIHuS3YZj+5prqM0AmDH00xLPvTcCScBlL8lLpkadeaz0MnwEAXZ7SrivnZ3hq6Tt+P6S8RKTQf\\u002fnDUs+fGyz99l2Bx6bef+GOa\\u002fcs85jj3gAgLxHd6rU21+D7hEEePOXY27P873mTUr4\\u002fukbvMy2k6XFhHzyxFUPbpzr1y5pHsFDFBrNJtSJ\\u002fqpFuQNl2+2WTUtdNtWpLAEFQQByF\\u002fiwIOFbD613mW23oyHIP29Y4v4vtNjt0DRoOBAEPLx8zq7Hbn1w2eyZSTFhIgGHyaAhCJfFDBbyc2MiNk3Neu2aNYf+7867F83wpkf2OLO1tsiRbUcAeHbasoPr73500vw54XHhztl2AADhRUJDzOrrptZl8tzlUeP\\u002fCi1kIaSS9+0GLanFnQ5j35bBnN6zuW\\u002fhOPHPT3Z5n223WzYt1b7Q1Kmyzw7tP54vEMnoCP2qmL+FcxIOyr\\u002fHh1elyA0RU3Zt7N9jeAN2JQjiLuUxk4y2vl4Jcv1vAnZ2pPBG0lZUdT0v6CyF1Kgn0eK7FPrfSX1OiXLFfTlh3wrZueTNKDTm85Vdj5AjHEZ0tOhOcqS\\u002fQL7WeBWwY0PMmW7WtzhWdZZCnaVYyHZ30WNB28sUd1kxd7U4RtyIvK8Q\\u002fjqFoW+EshVT1Pe8SGp3giBMBk3EpEn5rAwRe0oIfy2T7lXimEETJ0j\\u002fUaN82hGxYl2FHZszQj\\u002f0fsIDg7VCrt8m4c4N4i6mtpEE7Djsz2JDPWaFbl8zY0pKFDU6GHwu6793r7nlvz+6+cG\\u002f41T5AxvnepOGgFxiMRnXLJm89UjhpgU5Vy3KDRb3Xej4xA3L8wAAb\\u002f5yjNrQz\\u002fHCuiHfnnGpRaEm95nqj89lvfXglVKhj7tCCLjsdXMy183JVKj1+\\u002fOrFuQmUrfwkXH\\u002fBt1j0ekLohIONtcCAJ4\\u002feyhBJM2WhTlaW\\u002fWax0\\u002fsadKpHZFRYv3crD3nKqlRkrPlTUqNQebrv0Sywtq2ti4NNeps\\u002fZwsamioxvGXjJf4XNab968fQj6abNnUFCGX9cDbv7s5IdrtOFXufRk3N\\u002fblV1U0yanRfuZPSnz9\\u002fiuGXNyZhiC3rZkRFiR87st97u\\u002fi+w88RKFRa0Kd6MUCzkObPI8Jfnjz\\u002fEOXarz8tvjHdYs9DoSNC5POz01wMxLFm29CaDhigsT3L5lFjUIA\\u002fFpX7Fi+ITXvtnR3ffzJBdAHkiyW1Wh6M781mu4VMb0Z4YHU61yPOfCTScERdATBCAIA0KRTGVGbx0LzPWajwqS3L9MRWlZQ328BH\\u002fpqz\\u002flTpKEzsaGSxXnJqdEhIj7noXe2923nbAbpwu9cRVP\\u002fiUZ8aCgXYfNDNt6T9FoU10NXoCFAADI9aOVDKe+6ybYDABCEniR7nhKsUz5fq3wGxV38UDHaakvlt7drv6M2eIGOcFODX0OQvj4UGG4o6rymUfWGy9eyYar6nv+WdN5ErsuBAHpayBt0mofLxEC+1ngVsGMjiLeUEqnoeohSRt+BIGyd+l8vta83WKuobaPMiLyvIN6ScMFV1OgACMJmw5RGW22XYUddzwvnWudWdz\\u002fp8j+3v3DhtZS5DaxYV1HH1eWK+9XmMzhhITddRljQtm7jvjrl8xfaVlxqv6JN+zWKebivG7DjsL9TJY3ux7fyOKybV0yjRgcvKz5sbk4CNUqiNZoPuK29CHl044qpu1+9874r5\\u002fg8y2B3w\\u002fI8b0pkkK9jfKLM0+RaG+ZlR4WIqVHfCZUIblw+1X23qeEY92\\u002fQo4cnz2bQaACAbpPhir++vXr3T\\u002f88te+JE7s37\\u002fph4W+fne1onhQcvizW95eRwzE9PTZCRh4FRYXjhPuM\\u002fPDt9HS7NDcpMi5cSo0Ow3j9kvEGk0EffrbdbmZm3A3Lp1Kj\\u002fZwpa0Sx4XZXIgjw2Y6z1Gg\\u002fKdHB\\u002f7t37ZCz7Q5rZmU8vDkQg5sHMpEPUWg0m1An+s0LJvG5nofOR4WIvezQkxEX5uVUT4unuLtUaOxQUUMQFBDVmr6+hhsSPPSEqFZ77piYI4twLJ\\u002fpbCK1uIARRJnSw1eQbwmZ7Expb8YcJ4gjba6zQ2QHWmscy1lBYTyG5++QwdLozd\\u002fsu2BfZjEZz9y8\\u002fPcXb3t48\\u002fxVM9PnZMc7beosSMRzFOMqa\\u002fDvJznEvoeR3MR7kv9XrD5+TPFrl8UHBf4QgGSIZi4OuyacE09tc0XCmR0q2KDQ\\u002f0EOduh+6tT\\u002fFsRdwGUms+gyDDdYsW6tJd9g7Uv6SDhzGHRRt8FDESUyMWd6UtDztcpnHBGCQFs0H7VqP5NwZvGYKSx6MADAinUbrJUa83kCYH0PBgAAECd9TMT2fCEOAvta41Vgjo1g3nI+K5X8cAvaVtC+Ppi\\u002fWsqZx2HE0BA2imstmFxrudBjPEqerjNafLfOUqAx5zsio8dIva+U4P8y6cEtmo+oDZ4QBCrX\\u002f6oyHcsJ\\u002f47H9HzplhbyhlWu1Jp7v5oBAAAQSuN+pXE\\u002fDWHxmMlMehCdJsBxC0YYUFxjsjWRb2h5LzDHYX\\u002fHi+qpIWdXzMn05qLZG9ctmeJ+ANfO0+XrZmdSo5DXAjAT1OPXLjpcUOeYEculymYFThA+HC7qppKy3aoZ6dTQmDLu36BHucERby5Y+\\u002fcTeywYihPEuc6Wc50tjtbJIRGfLt24tbrY3gt+lEAQsG5OpvtU5o7T5Teu8NclltWGHrzQ9+PEJZ9Ml0o2Xr9kvPH3axf5JNtu9+DGuUcL6txPymq02Irq2qemRlMbBuNceZPHbxgWg\\u002f7KPWtZPhphdtPKqflVLadK3J3u\\u002fWciH6LQaObxz3A8nejXzs6ghgawcka6N3M\\u002fbFnkbvA0mfsMfotCjeOEr8qsQZCXcIIwkqqTy9wWS9FYzUfaBhyl4bAwMvF\\u002fBUfty2flzW0GTRR\\u002fwDt2Jzsa1NaAlpQBAGxJmlTS02lf\\u002frbq4to4d18LOEH8UH3JsboxIZvU6DMHLlQZTL3\\u002fEc\\u002fcvGzNLHe7RBEfLm3r1gAAmjx9mQ\\u002fT0C9iEIDkShZOkiyo1xcXqA5XavMt+FD+16WssEmS+VOly6SswY0ySJG9aLY1aS0F5CBB2JTGQwAcIgcduMzE9JC3FYa\\u002fBpvMihBeRwCsTvkCAH09UwgCVZlOqkwnSRu6ECd5OEZ8LzU6sEC+1ngVkGODliJ7ubjzenK3aJywKvR\\u002fKvR\\u002fkjajChdenSD9e4vmk6Elpv0v0O+LIFCF4Y927Xd6axm1zWtWTFEqv2VKxF9MehC1zRkN4eSEfVXR9UiP8TClCSesequH3oWDEpDjkKq4zkNV+rW+y4DPzIyTCLhq\\u002fYDf\\u002fKUNnfA6eJQLEvLWz8367WgRtYHEZLE1dvQk+m40vdrTDPLRoQNeYo4J4\\u002f4NeuPKxIy8kMhvKi6dam9q0WtMqE3G4aVKg9cnZmxKymLQaMkSnx1RvnLFnMzPd551Mxy+tq27qqUrLcZpshNfOVpYpze5HGjVi8Ni2GfPG1tG5EvGo7nZ8b6tfcxk0K9aPOmtX45TG5wV1rQNM+HuzVypN66YGh\\u002fu4XJoUJ68YcmmZ75xn5Ieu0bnIQqNchPnRB8WJPT++2RyciQ11A+Nhixx22+dLCpYLOCyBzo5YjjerTWESgTUBgjyJxqCiFlcR8q7QauKF7r+GyEAeD7\\u002fgM7m+gAmy5SGZUrDylVyAABG4C9dPPThgk3UjQAAAGAE\\u002fnrhMWrU\\u002f65Ozn2n5GSP2QgAOK9o+aG64IbUKdSNLvui4rwjOy9mca5OznVu940zZb1DAdJiQweVbQcAOEa1KtR65xYfG3rC3Q4BSJIgN0mQixNYi7GqwVDWaW6Qm5vU1i6UcF3WgE3jydjh4ZyEKG5SoiA3mO35e9klGsLJDvumsuuRHtMRapsrQvakzNCPmfQgITuH2uaFSOGNXEZcdff\\u002fkbv0usekSZJlLwbzV1EbPAnka41LgTk2hOzc9JB3qroewwgPl1yXIbGS++MkjwEApNz5jarXqe2jQyDfl9FWU9n1N4O1ghxk0cN4zCQGTUQurwQAIABOEFYMN1gwhRltIgin4R0WtLNJ\\u002fXay7AVy0CUaws0K\\u002faRN+1WT6i2v3yMZ4mXJpsAch2Q6o6VZoaJGSVhMhg9TRQgCchIjThQP2KfeZLHVtnWn+u4VIX+4dslk94kGAECzQu3DRIPJ6vrywIHH9s0gjJEyPt7gQ5NnPzR5NjU6GDFC8dMzFlOjl62JT2u6\\u002fR\\u002fUKADzIuNcximObL6TGhq2qGBxXmr0xapWagPJrjPlaTELqVFf2OVputSlU1N8NT4pwAL\\u002fJeMenUZ74roBj8whWz8368Ptpy1us9LVw5ubS603nfTU01zE49y2ZgY1OjwRMpHHlPSYNtoOUWj0Gx8nem9M8SKH7pAYKeNxWEZzX+ff\\u002fnISIkR8DjU6sLgwiZsCPl1qmHCHRkBeSNThy2VVPig9PTc8nkV3SlwAALRW8zPn9\\u002f3V6G1\\u002fvody5t53\\u002fHf78p7mqufy9z+Vt5TytGqr6f\\u002fO7C69nMsOJA6d8eKMlfcf325ffTZ\\u002fnxG13p4xnY44Fbay4djHZWffKurrf\\u002fD01KUeC74PTW2b0r4whNkseJzer2iD2cOX+TANN+HuQEPocfzMOH5f30kjpjOgWhS3oASKAMCgsVg0toAhZdEG8Q3rHp3Gywr7tFO3tUn9thUb8BKWTuNFi+6KFt9DQ1gAAAErEwH0\\u002fsVYPJJy50+N2tui+aRD9z2Gu5s7mE7jhQuuiRHf57G\\u002f7UAC+VrjUmCODRlv2ZTIP2uVz6rN7gahAwD4rIykoKfFnJn2VQErk0mX2bDe74jRJjDvy2CtKO68kVzTPEywJVp8lzeVYXDC1GM61tDzqhntK1PQqf8lQfp\\u002f3mXDkSjR7SH8da2az+T631BcR213hcuMC+atDRdexWHEUNsGEJjj0KFZrnLTNxMAkBEbOvyKrmQ5ieFuEu4AgNKGTphwH+USIoJkYr5S4+5Eo1D58uY\\u002f\\u002f\\u002fIlzkC61Hr31bRHuXH\\u002fBsex9XOz3Cfc95yrfHTLAp8P3OnRGc+U9vbTGYgPp0sNsMB\\u002fybi3fm5WbKiEGh02EY8zOyvuaGEdtYGkdngJ96OFdTju9jQPwKaFOTy273\\u002fZ3rxi6rZjRe6vMcau0XaIQqPfxDnRp8cOoggBDUGSImUl9R3UBpLJKYPI4AMAwoKEbhLu7v9sIchPrk2e7Ei4X+xqvWLPlzenTsuRhQuYLANqa9apzsib\\u002fmwo19ssAICrkiaRJ1kdyKrYtBUxqftbeivNflt18VBr7br4jGSRjMtg9lhMRd3t+1uq7f3l18Vl7Gzy0FGDggDAYLM4JjIFAGAELjfphUyWlwXWV8em35Ex44uK8wAAnCD+e+nwd9WXVsSkpoiDhUy21mauUnftb6luN2gdD9mSNGlLki8HFJKp9Eb7QvTgJ8xgMXrvZOB4X1kRf\\u002fBZwr0\\u002fHl3IowupUd9DwoXXhgo2KY0HekxH9JYyC9aO4SYawmYzQvnMdCl3fjB\\u002fLYPWtyc0hDsvvor0DIPAoIkSpH+PlTyoNB5Qm07rrWUWtB0j9ADQGDQBmxEtYGVKOLODeEvpCJf64EEKwGtJuHPnx\\u002fd+U4xHgTg2uMzEnPDv9daybsM+reWiGW2yYRqCsNJobCYtiMOME7ImBfGWiNiUETfIrJhzzhEXgnhLvPkPCuavms\\u002f3vFmYYHOYYDM1OgC\\u002fvi8AAE5YK7oeIWfbk2X\\u002fiRBeR9rEHRrCDeatErImXWhbiV\\u002fupU4QqNp8WsZb7rztgFj00MSgp+Klf1ebTqrNZ\\u002fXWMjPaasN6cMJCQ1h0Go9BE3EYsVxmgoCVLuHMYTPclREcWCCOQ7sOZd\\u002fpzSWfd86K8zTItN3TLkGjweSkyEOX3BWP7vLpaDuJwMM561J129rZY\\u002fhn6rh\\u002fg+PYsqmpr\\u002f54xE3vvB6t8UxZo\\u002fspo4dg77lKzO0Vf6RMNDXN23u9o1CAv2Tcu24Z5dLFZ7ISwt0n3Dt7hnVOPHzJw8UegoAtPi2V4xAVIp6UFFlU205tGC9G1SEKjX4T50SfGOnhUp8iTCooocacZMQNIoMPAHA\\u002fYfJA1WYgyK+Wx6SsjcvYdTnlXa3ufvq863qwt6RNe3768lOdjeQ09EDemnvFLYe2Xujq7fnRZtB8Uuai++N9WbNvTpvqMeH+funpY211WptFZ7XobBaDzUK5ad5lMsza9h4AgI4gfCZbwGQJmWwhk70iNvWujN7+lBRPT13KpTPeLz1tX23Rq+35d5duTpv67DRvMzNDYEN7r5zZg5+0xvHVMagBN0Mw6D0bnWgIK4S\\u002fNoS\\u002fltrgH3SEG8pfH8pfT23wg0C+1rgUmGNDwMoSsMZq1zM3\\u002fPe+FIY\\u002fTba+ntFB3MXeZ9sd2IxIGW9xl2G3I0J+Ti\\u002fREFYQb0kQbwm1wacCcxx2e+rlIeKzqaHhEfI8PKHOYKaGoNEnMz7MfaLBbHVXJGGwQiTufjsBAH4\\u002fXuL9DF2j0Lh\\u002fg+MYh8VYMS31j5Ol1AaSHafLfZ5w33nGw6+mK+Zmjen5GgP8JeNGbnJkkq\\u002fvPTtkJYRTQ87MVlRvsgi4Hk6dLtlQ7EJl36g+l3ISI\\u002fzXqXbl9LRxnHAfPYcoNCZMnBN9dIiEGnIrVOqhw+Vge\\u002f+4T4cZBr5BDkF+9dbcK4LY3O9IU4NS8Bisf+Ytvik1DwAwJTjKm4Q7j8H6Zuk1\\u002f710+KeaQtzVmDIeg\\u002fnMtGXXJk8GAAiYbHsP+oGU93Q6cvfuYQShtZq11t6f7XFCqXO7k8cnL5wSEvXSxcP12gGrGsQJpU9MXrjO7cSqwxck5Hb26AAAbqaUG0hbd+9\\u002fh1To4e7pMI2ThDsEQWNLt2EPeTVceDV51Xtc5\\u002fozNqyHvDrRGC02asiZcEg\\u002f8t3w+IRao7vrAGiU8NhRy31V4sGalORhNHFhbdtvx4r91E8zAMb9Gxzf1s\\u002fNcp9wP1ZUP+ScqUt17cqqZndz9iAIWDenr2bjWBTgLxk3lualUEO+kxIdTA31o9Qah3bwVDQpPH5Ki72einAIZmXGUUPjyOg5RKExYeKc6CNkHhLoFO47pCMIiBlk8Qf31XtMnn77QJCfMGn0F2asvDE1b2ttUb6ipVmv1tssdBotiM1LEQfPi4jfnDhJxuHZN84LiXJ0h3ePx2C9OGPVzalT\\u002f2goO9HR0G7U6qwWGYcXzReviEndkJAdwu39E0sSyYqUI3MXfElU8sLIxCNtdQdbawq627tMep3NImZxgjn8vJCoRZFJS6KSGTRflrF1KSxIaE+4l9Z3XrUol9o8MAzHi+t7P7rBjrkZLJhwhyBoBOitZeRVASubvDoYzl3+xnQPwGGzeOp+5ZgexFd4HA+FYuFIzzHBfe8hAIDVNugZBdxIipQFiXg92t66ey699tMRHps52BnnR4lx\\u002fwbHt9zkyLgwaZN8wAmorTb0wIXqjfNzqA1DtetMOTXkbFpaTKTfui0HRoC\\u002fZNwYwsxa3hPxPLxNMIyO0oW1bdRQPzMyYqkh34kLl4ZKBIpxWlll9Byi0JgwQU70Qh6bNchaDWwmdepIMqmQN9gn5LDc\\u002fdZAMXfV2CCILEcW0XDjk9To8KRKQp6Ztowa7ef29Om3p0+nRgeWKgn5x5RF\\u002f5iyiNpA8sfqW6ghZx8u2EQN+Q4doS2LTlkW7cdODB7NyIi1D7w7XFD7uHGhN9dgdocv1Wr0vd35Z6T78cIJwIQ7BEEjAsXU5FUGbYipBAvqNE6KSRvcKMVhwgni4NHyg0craurkWp2JzWYGSfihIcLpU+LnzEyOiwnozgAAPM7jN+Tf+QMxmDwM5PTtHK2Qn4g8lQYigItRjcOxeErytmPupg9CMfzZL\\u002feW1Hc8sHHu0HqDjqxx\\u002fwbHtyvmZL6\\u002f\\u002fRQ1SrLzdIWvEu44Qew5W0mNOls\\u002f1y+13QIp8F8yLoVIBDF+mC7VgUGncVgM96da61A7SpfWd1JDzgRctr9nKc9MCFMUjNOE++g4RKExZCKc6GUid93VXWKz3OWXwjwVnOmPyXD3U2KMJtzveODrunoFAODl5zfPnuk0XBsaju9+OvPFtyeoUQA+fOvGzHQPo1KgMWfR5KTPdpwFABjN1pe\\u002fP\\u002fzfu9Z40\\u002fdSoze\\u002f9etx+zKfy\\u002fJrPwwAgLvvLwiCID+h0ZyuO1FcTV71Ek5YlMbD5AiflUpe9SuTyfrYk1tfen3XuQv1PSoDiuIGg6WlrediYdPHXx379Ove73GKbqX+7Y8OKnv88nvVY0V1rdHHFdU9Vozhuu2WAo0SDLc\\u002fZvzhhuV5NE\\u002fXRAQBfjlStP5fX3637+KYGzI87t\\u002fg+LZ2Tqb7\\u002f77C2ra2rr5Jv4cjv6LZfZdhPpe1xJ9VUAIj8F8yLuUkeqixPnxCTx2srOgQO0o3dHoompcaE+z+uB2+lGj\\u002fJvRH0Cg5RKExZCKc6MUCD19o\\u002fbEY7hLuksE\\u002fIQ1x97eJjc2EOwRBw5ceG+pIl+\\u002fPr3riw788Tm\\u002fe1Km667Vf5D06++rViyd7zJ8Mk7svRAiCID9h0cNNeN8EpyrTyXDhNaR2rzSp3yVn6mkIW8QexFitYfr0m+OFJc0cDvPuWxbMnZkcJOUbjNYupa6otOXkmdr1q13XETuTX7d956Ur10yWBQmobcPm8YSh9fUUphpPTygV9ZaugyCyuDDp4inJ7meos9PozW\\u002f\\u002fdvyrvee3LMy9enGu+9qgo8e4f4PjW6hEMCsr7nRpI7WBZOeZ8nvWz6ZGB8\\u002fjdKkrpqVx3HYYhLyXFhtKDfka22O1hCH1k8ZxokWhpkadBSAbnjzI2Q4haBybCCf6IXTMd3\\u002fvSuxpsoT+3BeCHtIXKjRubdkwdeG8VI3WpNGa1Brjh58eMXoajQ2NaY9uWXChssU+j93RwrrTZU2LJifNyoyLj+ib91VjMFe3dNW2dZ8objh4sRrHe782YkIlt66a5tjMTzxdFEIQBPmBhDPTZOtLuDdr3pPxVzJpkr4tPMCb1R+1aj4hh0L4V9BpgUvvHjxaDgB44M7F61dPtkdYLIZUwktNCrvqygG\\u002fuy8UNFJDvhMdIqGGnNW1DziZ+NDUtHRRQ84GOzMSNHE8cd2i\\u002fMoWL0ddaPTmL3ad+2Zv\\u002fpK8lC0LJ01Ni6ZuMfqM+zc4vq2fm+U+4b7rbMXwE+5Gi+1IQS016mwc1JMZPWL9WU\\u002fGr9q6NTZPXePjw4OoIV+LCIbndAjqM+5P9EKujyd\\u002fcj8DKgQNE5fLiiXVdP3i25Mw4T6+xYVLX7vvioff\\u002fQPDcQCA1Ybuz6\\u002fan19F3uaFb\\u002faTV+14bOYb968fwj3FwXJ7xxCCIMg\\u002fQgUbyKsWtLOoY4vGnE8OukQQti7D7oL2TU3qt8hxOsKNkzxMjviVDcW0OjMAIC15EOPTbSh2qaiZGvWdtNhQ94NbKxrlQx7M7lJRnYe50RNgbzhoAKESwb9uWkqNuoVi+P78qrtf\\u002f3XjU199vSfffSGOETfu3+D4tnBykvvJl9q6NN5MYune4Us17osMxIVLJyVFUKPQUPm1gLtfdV4e\\u002fuxGhGzQxZEHKyLI7y8BQWPIuD\\u002fRu5+wdAhgqUkIgnxrVlbce49sCBrMqPoImeiL\\u002f7smKSoQaQq\\u002f9HDHCNSAavSoxowZUMKGETYAEAbCZNCYXLpAwJDw6CL31bggCBrfROypIfwrugw7HBGTrbG48zoBK1PKXcBjpbDpEXSECwDACDNOGC1ohwltMVjLNOaLOGHqe6JeSErwK2xG4OZCYTLoAj5bb7DUNijSUjzk3FvbVd\\u002f\\u002fcra2XtHY1G3voXbr\\u002fV+SN7h+y8x7bltIjgwNj82MjwiqH7gbuxXFSuo7pv4\\u002fe3cd1tbVBgD8xD0Ed3crUCgV6u6+tuvc3e2bu7v71m3dKmu7uruXUtzdHeJu3x+BkJzchAQSCOH8nj17kvfcUEJOrrz3nPfE2GdMjUyhKq3vgKMG8HhccgRKFY0cnkjazRd388TdArFIqpDIFGKpQiJXyJUquUIlV6p1D2RKlaIvMhCEf9aIWJARU93S\\u002fcvBa3DDYBo7eV\\u002fvufjt3ktTEkKXTYmfnRblnDU3XP4NujAykbB4cuzOMwVwg4GDl8tSowLhqC0ODVZPxtmGt4+5nQxkDFVygPQKJXDIhJ\\u002fHENeft547i07A43XjyJzTWO+iyJjj2gd6yyugDoHdfyCCIMjkhNAdb9z23X+XD10ts7w0PZlIWD0j6YGVUzm2l7caGvvs8uQaaaO4vFFS1iqt6ZK38BSdlldyJ+AIHmQ\\u002fL0pgIC06hBEbTI8j4tDdTgQZX6I8X5cq60SKYsOgSFEqUpQaRgaFx1Givd7zZiyDGxxs2uSo46dLfv7jfHSkb0ykL9xsgC+QtrXzGHRyXKx\\u002fUUkzACA22o9GHdjpBfhz9I+HKSs5zELCHQCw62yhvRLu+y+VyC0e0pLC\\u002fegUtG93FIFYVlLfUVLfXtPS09DBberg6grYjS0PrZrGolG+3H1BX1DPehqN9nJx\\u002feXiejqFNDc9emVW4sToIItzPEaBy79BF7YyK9Fywv1ETuXzN88mD1qz24wOriinvAmOGsDjccumxMPREeQaOxlDI3Z9ZXe9gsET7h4sh787HA6w6BSeyHTcw+hwvS6KjEUufKAnkwhwaHgIeKd5bwiCuBAPFv2V2+c\\u002fsibrRE5lXlVLcW1bt0CiS76TiAQOk5YQ6psZHzwvPdqbY\\u002f+F9CwY4kWCjkqrLOZdLOJfqhUVqrQ2nN+oteoueUuXvKVMkA0AIOOp0ayJEzgz4liT8Dg779YRBHFORDxngt\\u002ffFd3P9khOwG1Wc6NOivR4k0GOgRsc7\\u002f47ZubmN3T3ih544s85M+JWLUudkBiMeYqcGBfw5Qc3AwCkUsXi9V8AAF58aml4qBe0mV0syYz769gNOGrg5I3KR7uzAoddhlWj1W47mQtHjS2aFAuHkOFRqNQ3KpovF9dfKamva+uFm8emWxemx4X4vP77MWtqJmCSyJUHL5cevFwa6O22MitxzfQkT2cax+ryb9BVxYf6RgV6Vbd0ww39RFL52fyahUPd0R25WqbRWsrOTE0MG+GrAuCiOxkdOoVEIo7VqwyucPAcN4thqQiSvYx6wt2FuygydrnqgZ5IsPM+k2B5CdRxA+t6EUGQ4XJn0TbMSdkwJ0X3VKFUqdQa+qguHTHEhLtcI73Ute9az2GJeogHFUMKjayEf7mEf5lN8pzqtXyK5zI04B1BxgMCnpHg8z1XerGJ\\u002fy1flmP9UvMEPN2TNt+PtcmNmgm3jRRvL9YPn9\\u002f22bfHL2fXnDpfdup8WYAfZ9nCCauWpbKYI3HRiyk2xCcywNPC4qgajfa9rae+fmKN5Wrvg\\u002frtcHZjJw+OGiCTiAszh5iHQkwV17UfuFRy\\u002fHqllWtzjS0ZccH\\u002fvnn713su7jpXOIQBYnotXfzv917++cDVOWlRm+alDrPchx25\\u002fBt0VSuzEj\\u002fbeQ6OGjh4pWzICfdDV52rnoxr72QAAEOei+AMZIpBBjaRSUTyiNxOGIEVxsxx+S6KjGkueaAf5sUCBnv\\u002fPGeWk1t\\u002f+HhRSVlLL1fMoFNCgj3mzIxbvjiFRCKQrJg60NEpOHys8HpufVs7TyiSsVi0QH\\u002fO1MzI5UtS3NiDz2ca8sv\\u002f2nbl1z8vAAD2\\u002fPOIhzsDAFBb33XgcEFeQUNXt1CjBR7ujMhw78yM8KULJ+AdM2UhJ7f+2KmSsorWnl6x1Pyqp198uCl1QggcHZ7Wdt7Fy1UFRU219V1cnkSpVNNpZB8fdly037zZ8RNTQ+EXmGho6jl2origuKm5hSuRKAAOMBkUP1+3qAiftNTQqZMiaBbXIh7myxFDZBJx1JeNGMqpZx739NH2PyQqAdwwbAJlz7G2P650H1wWcG8CewrcjCCIK3KnTXenTVeoO3skp8SKMrGyUq5qU2vEaq1Iq9XgcUQCjk4kcMgEbyoxhEGOZZEnsCipONxQdl\\u002f25e3Fev\\u002f1deWVbfuPFJy5UN7azvv5z\\u002fN\\u002f\\u002f3v1wbtmrVqWBm89Uu5amvnKL0fgqIGrJQ3f7738yJosuMFqOeVNP+6\\u002fAkeNrcpK9GDZsHoJYs7Fwrrfj1wf\\u002fvKMTo5OJb+wee76WSmf\\u002f3vuSkkD3GwLlVpzIqfyRE5lcoT\\u002f7Ysy5qRF2f2CcQhc\\u002fg26pKVT4r\\u002fafUGlNlux+mpJQ69AYtNKTTql9R2Wy3+xGdRZKRFw1DHGyU5m7A5vBwAolIMseE6xIn1jF0TCKAxQHSddFBnrXO9A76B0qstTqTQffXHk+KkSfYTHl\\u002fD4ksLi5n2H8t99bQ3bYgUwrRb8ue3y1u1XlAZ7fi5XzOWKi0tb\\u002fvn32sP3zVm2aILBK4wM8+V63T0iD3fGlr8v\\u002ffn3ZcMJeS1SRUsrt7K6Y\\u002fnivnHEdiSTKd\\u002f+6OClK1VwgwkcDhDsejxq7+B\\u002f8tWxnNx6KC4UyYQiWU1t56FjhdMmR73+0kpzSxFoNNoffj27678caP4ilyfh8iRlFW0HjhTQaOTtWx7AvOcxzJcjzgm7r5gj10h2NX1ZLsiGG+xKoOzZ1vBhCmfmqsCHSfhRG0aB6F08mPf3J4faGrp9Aj1uemzhgo3j4l7ImognZZKBG6pPf3n7OHnjo4VM8PFn3QxHx4K4GP+4GP8nHpp\\u002f6mzZtt3XGpt7P\\u002fvuhFSu3LR2dEbfL8qM\\u002fe1wtuVUzm+HsyVy5VM3zRzClfOx6xVvbTluefAOnUK6a+novH1XUtPS8+E\\u002fp29UNsMNrisy0PObJ9cWVLd+t++y5fLW1iiqbXvu+wNRgV73r5wyNy16VC5WIS7\\u002fBl2MO4s2fUL42bwauKGfWqM5cq38lgUT4YbBDDq8fenkuBFIEI+rncwQjnfOQ7fiugUke1d+MGeE\\u002f4zjqosirsGVDvQj+6+5jg8+O3zyTN+aZF6ezAlJwTQaqbNLWFTcXN\\u002fQ\\u002fcxLO329La1x\\u002feHnh4+e6FvkzNODmZQQyGJS+QJJUUkLjy8Ri+Uff3G0p0d0++Zpxq\\u002frM8yX6\\u002fX2irZsrd7y9yUAAJVKCgvxolJJ3T3C9g6+SqWZmhkJv8Ae3unPttNp5HWr06MjfMlkQmNz738Hctva+QAAPA734rNLoyN9A\\u002fw5ZDOJ76HhcOiV1R26x1QKKSrSJ8CfQyETe3rF+YWNEqkCAHD5WvUX35544aklRq\\u002fs98sfF3buua577O\\u002fnFh3py2bRVCp1Wwe\\u002furZTLJYDABLi\\u002fM2ly4f5csQ52dBH+cquP+ve7pQP97BhpQLe+S55y21hrzCJHLgNGUFNVe3vP\\u002fCrRq0BADRVt3\\u002f2xJ8h0X6xE8Pg7RBkfKOQiUsXJi+cm\\u002fjeZ4dOnSvb8s\\u002flNcsnmrsB7lB4HO6pm2Y+9uV\\u002fcIOx7afySuraH183Y2KMtZNVO3qFvx7O3n2uEG4w8ciaLF\\u002f3kS497Eq0WvD7kewf9l1Ra8wOrXVhKVEBPz6zvriu\\u002fc+jOWfyqi3XuR5UdUv3898fTAr3e+7mOUnhfnDzaHD5N+hKVmYlWki4AwAOXim1NeGu1miOZZfDUWOOriczzncyY45SPUjC3b4D\\u002fSwYsYQ76qLImIYO9OPWpStV+mz7LRun3HP7DP1EAYFA+slXx85fqmzv4A+8wNjeg3m6dDmRiH\\u002f8ofnLl6ToC\\u002fuo1Zrd+2788OtZjUb7218XoyJ9pk2OMnrxsF9u6PCJoguXKr08mQ\\u002ffN2dmViyR2LfzF0sUl69WhYZ4GW9uBwVFTRevVAEAqFTSr9\\u002fd5e\\u002fnpotPyYxctTztkae2Vtd2arTa9g7Bgrn2P0eiUkg3rcmorulcsjA5PTXUsOyPWCx\\u002f75NDl65WAwCOnSi++7bp3l6sgVcCAACQSBW79+YAAPB43CsvrJg7M86wVaPRllW0nTlfljEx3DCuN8yXI07L2mSQSMX7rfb1XkUb3OBIrdKaLXWv3x3xDp0Ad2hkxFw5WqDLtutdPlKAEu4IgolIxD92\\u002f9xT58qkUkVtfVd8jD+8xYiYlhS2fnbKrrMFcIOxotq2+z7emRjmuzIrMTU6MCLAE7NWY69Aklfdci6\\u002f5lh2hYXSCnpZSWEb5qbCUcRqErnyfz8cvFRcDzcMG4lIoJFJNAqRSiHRyCQahUSjkGhkUrdAXFDdCm892pLC\\u002fT56aHlLF3\\u002fn2YL9l0oE4mEV7S2ua7\\u002fz\\u002fW1rZ054cv2M0V08R8\\u002fl36BrmJ4c7sGm9wokcEO\\u002fyqauqubu6CAbrjwvFdVbXgMzJtg7NsQHjtoP2smMOYOuNDjMdJ71cFjnCXaHuijiGtCBfhzauuOq7sHsGbH33TnTsInNpr3+4sr7H\\u002fujpq7LMK4nlSp+2XJe9\\u002fipRxYuW2xU+IVAwG9YOwmPx33z42kAwJffnZycEWF4t3WYL4ecv1jp5cn88avbPT2MBlEx6GRH5LsBAOcvVeoerF6eps+261DIxLtum\\u002f7ym3t0m91281TDVnu5bRP2j2UwKK++sGLDHT8IBFKNVptX0LhwHvwXaGzqkStUAIDoSF8oXQ4AwONxifEBifEBUFxvmC8fnx76bDcAYNGk2NUzkuA2W7R080vrO\\u002fgiKZNO9fdkJYf727GallUJd41W\\u002fU\\u002fD+1Zm24k4EofswyF504lsGoFBxJEJOKIWaDValVwjk6nFIhWPr+zmK7s02sETNx2yxu0NH90V8RYOTWkaJdxOARThdcERBEH03NzoBAJerdZYLrriaE9vmJlX2Wxh9VS9kvqOkvoOAACTRgnydmMzqCwahUQiCCVyoUTWw5e0dJsdhWEqzM\\u002fj3fuWYibuEWuIpYrHvvyvoGYol\\u002f14HC7YhxPq5x7kzQnwYnuy6Z5uDDcGlUmjMGlkGoVkLmVz+GqZ0yYaAr3dnrpp5sOrpx3Lrth7oXhofxkdrRbsPld4ubj+wweWJTrNADGXf4NjHQGPXzolfuvxG3CDgYNXSp+6yeii2rJDV\\u002frGvpnj0OHtaCczFg26IKpqsCHwYwjqooiLQQf68aO1jVdW0Zc0u+1mjJotBAJ+47rM9z45BDcAAAA4frpEJJYDAEKDPZeaKbO+dmX6v\\u002f\\u002fldHQKOjoF5y5VGiZnh\\u002flyU489OA\\u002fKtjtUY3Ov7kFkBMaYg6j+YFNL32YjiUolTc6IOHG6BADQ2SWEmwEg9h+mu7qFCoXK1nI3w3z5+JRd1ggA4DBpQ06417T0fPDPqdxKoxViPFj0m+ak3L000y5T+qz6IM907miS9N1uwkTGU2NY6VHMlGBGnBc5EI8b\\u002fDdTaZWdssYGSXmNML9GVKDSKuEt+tWJi8937p7lsx5uQEaEh6\\u002fR3UUAgFegOxRBEETv+o06tVpDJOJDgjygJv2xk8eXGLfYH4VE\\u002fPaptfd+9G9zFw9uM0MklZc3dsJRW4T6uf\\u002f47HoWHa29MUQajfbZ7w\\u002fYdCVGwOPTY4MmxQWnxwTFBHvTKKO9FrtjUEjElVmJK7MS69t7910sOXy1rJsvhjeyTluP4J6Pdr52x4KlU+LhttHj8m9wTFuZlWg54X70WvkT6wbmjFsmlMjPF9TCUQNEAn7JZEsXwMOBdjJj1KAF\\u002fZWqwYcx2YXaioluw4G6KOKq0IF+PCgp68vc+XizIsO9jRv7TDJfKkC\\u002fYuesGbHmBi\\u002fh8bhZ02N1xb6zc2oNM+bDfDmExaTOyIqBo46k6l+thIq1G9dfR4\\u002fWmDZfn76qG7p67pDwUC+OG53Hl\\u002fRyxf97ffdTjy4IDoRTARYM8+XjmfW5DkhpfcdDn+0WSeVQvFco+XH\\u002flfMFtT88s45JG25aY\\u002fCEO0\\u002fRebFrLxzt50H2n+G9OoUzy9bVTYk4UgAtMoAWOdVzmVwjye09fal7H1\\u002fZDW8HAADgXNe\\u002fae5z2CRPuAFxvCmLJ2x5f7++qgyJTJy9JsN4EwQZd27kN+zal5M1OSopISgowF1X1a6nV3ThStXPf5wHACyel8RiUqFXEQj4kCDPxuaef3Zdi4nyY9DJurhSpR70WnoIvDnMH59d\\u002f8Anu4Z8HLJJalTgxw8t92DT4QbEal\\u002fvuai7V2+NiADPjXNSFmTEupn0NBcW5ufxxPoZj62dfqW04dDl0jN51YrB1hI0pVSpX\\u002fvtqFKlWTXdgeOIh8bl3+BYFBngmRjmq5sJhKmbL75a2jAtKQxuwHIip9LyZzozJYLDdNSKWGgnM0ZRSINcsknlCo1WOwLTyyRys2Ok7AJ1UcTloQO9C6tr6JtbHB6KnW0HALi7M1hMqlCEUV9Iv2hndKSvcYuR6Ki+1tJyoxIUw3w5JCbadwSOKYb8fd3yAAAANDb1ABANtTY29\\u002f1t\\u002fUzGg44Mon5xcqwabgQC\\u002fsF7Z3\\u002fw6WEAQG5+wx33\\u002fZqZEb5sccqUzAhrLvOH+fLxrLmTDwCoaek5nlNRWt\\u002fRzRer1Rp3Nj0h1Hd+RkxiGPZ3QalSv\\u002fLLEdNsu15ZQ8fz3x\\u002f89ql1w\\u002fwSDHL2BgA437UHc\\u002fg5HkeY47NxhvcaAm7wH2IZBU+f6rV8kueis507z3fu1gK4Bys1ivNde5YH3AfFkREQHOX30k\\u002f3bv34YFtDd1CU792vrA6OQvPXkPFOrdZczq65nF0DAMDhAJVKVqnUSmXf6fK0zMjHHphn9IJ+t2+a+s4nB7Nv1K3a\\u002fLW3J0upUvP4kntvn7FpbSa8qT34ebC2vrL5nT9PnLxRBbfZDx6H2zQv7Yn1M+wy8WrcKmvosDyKVs\\u002fXnfn4+pmLJpkdveLy8HhcVlJYVlKYQCI7fLV8z7lCa6onGdJqwbt\\u002fnfT3ZGXGh8BtTsDl3+CYszIr0ULCHQBw8HKplQn3g6NXTwbtZMauQe\\u002fBaLVAJJGzGQ7PO0sdmXBHXRQZP9CB3iUJ+xdocXe3NAKJzaZhJty5vL450J4eDOMWI\\u002fpWHs9oksQwXw7huFl6C44wMyvm8PEiAMCe\\u002fbmrlqcxGUYjerduu6J7kDXF0lqvw6TVgvLKtpy8+rq6rvZOgVAoE0vkCoVKoVQrlSp4a2OL5ydRKaQvvz\\u002fJ5Yo1Wu3V67VXr9eyWdQFcxNXLksNDR5k9PAwXz5uCSSyJ7\\u002fed6HQePJoa09OedOfx3IWZMS8dscC00UvTudVN3RwdY8ZNPLamclRAV5imeJGRfOZ\\u002fGrdLIprZY0ncioWToo1eqWNBsmVKzSyAt45OAoAGU\\u002fdHPq\\u002fSGYK3DAMRBxpvu8tIfS47Y0fKzXwrYZ87tlFfrfbOo4esYusZalZy1LhKIKMYwlxAQ\\u002ffMye3sKGhsYfLl8hlShKJEODHiYvxWzAncVpmJPyCfgvmJNBopH\\u002f33qiqae\\u002foEjDo5MhwH4ceQVl0yocPLt9\\u002fqeT7vZc7eSK4edhig71fvHVecoQ\\u002f3IDY6ONtZ61Z9S4rOfydexaPQFZlTGDTqZvmpm6am5pb2bLtVO7Z\\u002fBrr55mqNZpXfz26843bnXlopMu\\u002fwbFiUWbcpzvPK8xfa53NrxFLFQwafEIPaeniW64T7enGsDJxPwRoJzN2ubMHSbgDAHhi2Qh8agIJRpLIXlAXRcYhdKB3JTJZ3y1JChmjKIoelYKRhdNotApF32kGxWIJb1p\\u002f9lAsGahtMsyXmxr5gdWTJ0UmJwYVlTT39Iruefj3WzZMiQj3plJJLa3cf\\u002f\\u002fLKS5tAQB4eTJvXu+QYWoAgPOXKn\\u002f6\\u002fVxzS18edghmz4idnBG+\\u002f3DBfwdy2zv4AACBULZ73409+2\\u002fMyIp5+N45lofnD\\u002fPl4xacbTdwIqeysZP3y\\u002fMb6MZ1is7m1egff\\u002ffUuqT+JTE2zk3NLmt84ut9unP+Xw5dc2zCvUZUqNDA51U4gFsf\\u002fJR9s+16Maz0m4Kf2tbwITTOXa6R1IgK4tiO+nYhCIJYj8mgbFw7aePaSXCDFaZPiZ4+BZ4l52grsxIXZ8buOF3w1e4L1lzNWiMp3O\\u002fOJZNmp0ah8WXDV1Ddak3J2vnp0e\\u002fdv9Tcmm9DZqceMZomxgROjAls6eb\\u002fcTRn\\u002f6USpXWzs7v54m\\u002f+u\\u002fjybfPhBufj8m\\u002fQybHolDmpkceuV8AN\\u002feRK1ckbVYNO7T9u\\u002fifoLJsSb\\u002fcvuA7ayYxpnmxLwxV1OnqFIT4cOGpXGq2WJ+obv2l3qIsi4xw60LsASn9Sz8IdemBmMQw8HkelkGRyJQBA3p86xySV9SXK9eVJwbBf7gxwOPDOa2teeHVXeWVbR6fgs2+OQxsEB3q8\\u002fdoathV3oIfg7x1Xf95yXveYzaZNTg+PivTx9XFjMak0GolCIe3em6MbgG8ZjUbeuG7ShrWTcvLqjxwvunilSqFQabXg\\u002fMXKG7n1776+NnWCpSkpw3w5YqqisfPjbWdev3OhYbC8oW\\u002feamyIjz7brpMZH\\u002fLY2qxPd5wDANS09DS0c0P9hr6G5SAJ91pRIRwCIM19brwjE9\\u002fx7Mlp7nNzuaegeK2oECXcEQRBhqamtefQ1dJhZttxOBAd5J2VHL50clxEgAMH5o83\\u002f54tgEMmooO83rpnsd2zDAAAucWrgjEk0MvtpVvn3b0087u9lw5fLbOms++9WHzn4kmB3mNjwIjLv0FntjIr0ULCHQBw+FrZoAn3E4NV91oxLQEO2QnayYxpPhwmHDLR2i2AQ\\u002fbGE0mtH3trK9RFEQSgA\\u002f0Yx2L1TTXg8fuqu2ASCOEhrTru7vS2dj4AoLvH0qTknv5Wd47RvdhhvtwZuLFpn3+w8YEn\\u002fmps6iGTiWq1Bo\\u002fDsVjUyAifWdNjFsxNtDx4f8hq6rp+\\u002feOC7vG6Ven33z3L9B9i2jKPBIcDkyaGTZoYJhBI9x3O374rWyyWiyWK197dt+23+xnG1XJMDfPl4xCTRtk0L3XGhIgATzaRgO\\u002fii29UNG89fqOlmw8AOHC55O6lmcEGgxJ6hH3f0OhAL31Qb\\u002f2sCb8cusYXyQAAF4pqQ\\u002f3S4S2sNsj5SocMXrUGj8PP8dkABe1uru9GPA7+3dplDVAEQRCX9Pr7+1be\\u002fHVPr6VzBcQm\\u002f5zMveO97VXN2AtTh\\u002fq5T00MDfVzd2NSyUQCDgfweByVTHRjUoN9OBNjAhdPjnt49bTPH111\\u002fNMHtr1266NrslC23Y7UGs3Fojo4auLl2+YPum7e0MgUDqzJO\\u002fL8PFhv3b34p2c3BHiy4TYTGo12pxVZHqfi8m\\u002fQOWUmhPh6sOCogRsVTR29QjhqoKmTV9HYCUcNJIX7OWjXinYyY12wj9ugi9c1OX6B9JYuPhyyE9RFEcQQOtCPUaEhfQfx+gbsay4AgESq4Jopnh4fG6B7UFndbtxiRL84alyM0cjcYb7cGcgVqmde2tnY1BMd6bvt9\\u002ftPHXz2xIFn9vzzyMfv3LR8cYppEtxeTp0t0w1Ki4nyffSBeZj\\u002fEGbZ\\u002fUGx2bTbNk396avb2SwqAEAgkF64MsjYC0PDfPk44cGm\\u002f\\u002fPqLQ+tmpYU7ufBprMZ1MgAzw1zUra9dmtciA8AQKsFBy6XGr5E0j\\u002fPw4ONsVYBmUSclhime2wuf2IlOKkN6VXAKxeH0OM5ZB8oaHduJO8wBjxKqMfkl0EQxFVRqSQ8fpBrS8QaGq32rT+Of7rjnFqDPXvxgZVTd7x+2zdPrt3z9p2nP3\\u002foyveP5\\u002fz01PUfn7z07WOnP39o77t3\\u002ffzchnfvXXLPsskzUyI8WBjHJGSYimvbhRJ45RLI5PgQxxXK7zJz3j+mTYwJ\\u002fPvVW2KDveEGEyeuV1gzgszZuPwbdDZ4HG75VEvDz7VacDTb0hD4kzmVcMiY45ZLRTuZsY5MIvoPlnerbOqCQ\\u002famX2HM7lAXRRBT6EA\\u002f5iTG9aW829r5DU3Yq+DmFTSa+7CmTIrQPTh3odLcNhqN9vylvtOJjIlhhk3DfLkz+HdPTml5KwDg1ReWe3oMPrXLXtrbeboHyYlB5u5uF5U0wyGrBQa4z5vddw7Z1sYzarPCMF\\u002fu8h5fNx1zig+DRn7x1nm6x3lVRh+f\\u002fgtCJmGvVTAhsu+7XNfWa9xim0ES7jI1fPIRzkiCIg5i+g+Z\\u002fjIIgrikN19ctfP3B51wmtuYo9WC1349uu9iCdwAAACAzaD+9OxN96+YMvKr4iCGKqzIkiydGg+H7Ke1x+GFCEYFm0H9\\u002fun1mGdghjq4ooaOYZ1LjRaXf4POZtB6L0ezy+GQAcv1ZMgk4qLMYa3LZAHaybiAMH8POGTM8vwJu6htxc4fDR\\u002fqogiCCR3ox5bgII+YKF\\u002fd4z\\u002f\\u002fuWzc2Off\\u002f3LgUL85s+J0Weamlt79h\\u002fPgZgAAAHv23+joFAAAPNwZs2fGGTYN8+XOICevHgBAJOKDAgc55NkXuX8ZWy4PuxbQsVMlw1lMFQAg7r+pzGINpQb9MF\\u002fuwvA43Nw0s8vjJYX7eboxAAANHTy4DQAAgLnpg0H9e90u7rCKLmDMlTCk1MJjDbwofZl+R\\u002fM0+YeUGviXQVyJQq68erTwxpnS2pLmjqZeqVhGIBJYHDrHixU7MSxxctSURRNow6tXpVKqrx4rzD5RVFXY2NncK5MoyBSih69bWHzgxJlxs9ZkMN0GRu8SSUQA+maaDI2IL7lytLDwUmV9WUtnC1cilAGgpTOpviFeUROCJ86Kz1yQRDZeLhlB7Ovng1ePXMNOALEZ1F+f3+Cg8gWITerbB79GmhzvwBVyRiBNM1rcmNRXb1\\u002fw4Ke74AZjJXUdYX4jemZvLy7\\u002fBp1KsA8nLTowr6oFbuhX2dRV29qDuV9t6eJb\\u002fqLNnRjFpA3rJMcCtJNxAYlhvpcsFl3p5oubOnmGJUrtrrjOUpmC4UBdFEHMQQf6sWXzhilvvLcPAHDqbFlggPudt2TpJ21LpYrvfzmbXwgXbdYjEQmPPzTv9Xf3AQC++v6URqNdtTxNnxDUaLR79ud+9\\u002fMZ3dOH7psDDZka5sudga6Wi0ql+e7n0+tXZ3h7sQiEQYYI20VstN\\u002fRE8UAgMvXqusbusNCB+p6a7Taw8cKv\\u002fzu5MDWWP7afqWtjTdnVnxKUhDZuCKNRqs9frLk1Nky3dNJ6WGGrTrDfPl4xmHSGDRLy\\u002f8GeLJ7+GKh2LaKQPpSM9LhVZwbJOFOwlHkWqPF6KmEEZrZQcXDg1tJeEddh4w6uVSxNuppTf9y1RlzE9\\u002f+5xHjTYzcPvHlrtaBO2wkMnF39WckrFJTOl8998+Rvy7qHhOI+D3Vn5OpcJ5XIVeuCn0CCmL69uRLEUlBcNQKV44UvHXXj\\u002fqn24o\\u002f5HixAABqlWb\\u002fr2e3f3FEwDWaxKBSquVSRXcbr7qo6dAfF1gcxqp7Z294fJGFd2rBiR1X\\u002f3h\\u002fX0+7UfVJmUTRWtfVWtd1+XD+T2\\u002fsXnXvnM1PL6XSyQAABpsmsrjaiQUdTT3\\u002ffHbk9K5slckSTAKuWMAVVxU0HPnrIsONtureOesfmk8zvwSHM\\u002fQNezl+pmT\\u002f4fzG5l6pTOnOoUeF+yycmzh7+sCAvlPny9768ID+6aGdTzBNbrHsOZD75Q8nD+18oqWN++Pv58or2zRaEBLk8fIzy0KDB3IcBcVNW3dcLaloVShUQQHuS+Yn37Q6Q3+68+++nG9+Or3370dPnCnZf6SgvZPv5cFMTwu7\\u002f46Zbo5Z93xUFNW2\\u002fXTgChwFAACAw4EPHliGmRVCRl73YDPZ2XSqtxUr5g0NTyRt6uTBURcyKS54QqR\\u002fYY2lqnTNjq997Dgu\\u002fwadysqsRAsJdwDAseyKh1ZPg6MAnLQ4vB04sp4MQDsZl5AaHQiHTGSXNTou4a5Sa8oaHJWVRl0UQSxAB\\u002foxZPaM2FnTY89drAAA\\u002fPnP5cPHCickBdNp5B6uqLC4WSyWh4d5BQW4X7iMfVYwa3rsrZumbN1+Va3WfPndyb+2XUlODGKzqAKhrLCkmdufLVm\\u002fOmPBHIxZd8N8+ahbtmjCtZxaAMCuvTd27b1h2EQhEz08GHEx\\u002fksXJk9KDzds0uFyxXyhTCyWi8VykUQuFsvlsr5U6flLlS2tXDqdwmRQ6HQyg0Hx8WITiQOp\\u002fAVzE3\\u002ffekkgkMpkyvse+2P29NiQYE8CAdfeIci+UdfewafTyLdtmvpbf9rElFSiOHy86PDxIjKZGBbqFeTPYTKpGo2WyxOXV7brl6ZbuTTVMGWhN8yXj080CkkqV2qBmfJJ\\u002fbRaLQBAZVBc11zBJUP6BWOkckcm3CkEulxjlHBXaYc15td6WgCXG6YQBkYfuxgKjRwS41df1qp7WlfabNxupKuVa5hRBQAoFaqaoqY4rP2OTm3xwA8MjQ1wXEbVJlUFDZPmJQm44rfv+rH4ajXcbELIE2\\u002f95ND10yWv\\u002fvaAp98gE+sMKWTKjx\\u002fdcvEg9rwqPYVM+e83x68eK3z9jwcDI3zY7owOM5XXLDvw+7lf3\\u002fpPLh38myLmS\\u002f\\u002f59PDJ7Vef\\u002feaO5KnYE2Fcpm9s23Xth9\\u002fPxUb5rViSggO41jbe9bz60GBPw4R75sTwL97fxBdIt\\u002fxzqc78UjMAgMvXqj\\u002f99viktLB1K9N5fOm1nFpvr4G17E6eLX33k0NBge7LFiSTKcSCoqbvfj1TVNr89strDOcMvfHB\\u002fvKqttlZsXNmxBYUNx88WlBX3\\u002fXdp7cObDHGfbTtjLnDydIp8Q4dKYbYRCwfZHfh5cjySucLauGQy5mXHm35MpUnMjrVGXNc\\u002fg06j\\u002fkZMR9tO2Ph5PtETiVmwv3UjUo4ZMDPgzUpLhiO2g\\u002faybiACRH+BDweczkWvbP5NetmTYCjdpJf1aJfZMzuUBdFEMvQgX4MeeWF5Xg87sz5cgBAd4\\u002fo9LkyfVN0pO87r605c77cXMIdAHDvHTO9PFk\\u002f\\u002fXZOIlX0csW63L0elUq6+7bpG9ZOMgwaGubLR9fkzIh5s+P1o7kNyRWqtnZ+Wzv\\u002fzPnyJQuTn3tyMVQM5KU395RVYH9Htu\\u002fKhiJff7I5OXFgCCmTQXnn1TUvv7lHKJIpleoTZ4wW2PT2Yr3+4sqIcO8tWy\\u002fp1lY1pS9Ko1CoKqvaK6vgCWF4PG7tyvSH75sDxXWG+fLxiU2nSOVKvkgmliosDHJv7RYAAFj0gXGcYtngpVP0nQtnpuaMlQZJuHuQ\\u002fQRKo5yjRCU0fOo4phXbPci+UMSVxKSG6ZOqPe18Qa+IbWaZiNLsGjgEQFlOnbmkqlajrS\\u002fv+8kAgJjUUIPG0VSV35g0JfrlDV9VFzXBbeZV5Na\\u002fsunrL4++YGVqWKVUvXbLtwX9a4MMqqmq\\u002ffk1n3+892k3L+y\\u002fvwVajfbrF7bpB4xbqbOl96UNXz3+yS0LNk6B2wAArtI3TpwtdWPTvv\\u002fsVv28MLVao1SqDbdhMalpE0IAAIeOFVpOuH\\u002fy9bEP31yv2xgAoNUO7BP5Aukn3xyfkBT82bsb9P\\u002fWO58cPHGm9NK1qulTBm5slJS3\\u002fPj57ZHh3rqnz732b\\u002faNupLyVv1yN2NafnVLaX0HHO13y\\u002fyJcAgZPQqTqTAQqvlJKsN37LrRibhLCvLmwCFjCuN90Zjj8m\\u002fQedAppPnpMQcul8AN\\u002fRo6uJVNXTHGa9y19wpLzO+QAQDLpyWYKyJpF2gn4wJoFNKESH\\u002fLEyyulTZyhVJ3xxR4PV\\u002fowKw06qIIYhk60I8hJCLh9RdXLlmQfOhYYWl5K48nYTAoIcEe82bHL100gUQkDDpIefXytFlZMQePFl7Jrmlp5YolchaTGhjgPjkjYtniCR7ug9yAHObLR8veg3m\\u002f\\u002fXlBIJQRCPgAf463J4vQPwhdrdKIJPLm5l6JVAEAOHK8KDrSd+1Ke17PTkgK2vLj3bv33riWU9vSxlOrNCwWNSjQfWpm5KrlaXQaGQAQHuZVU9cFvxIAAMCdt2SlJAVfvV5TXtne3sHnC6QKhYpAwDOZ1JBAjwnJQQvnJgYHmS36NMyXj09h\\u002fp4dXJFGqz2VW2VunmhBTWuvUAIA4ItkuZUtE2MCgXE9d7mZ0w\\u002f9Qu604ZWAHuTcxZcaUi82uqgQq4wqcjhOrwK+MvGhuPJ4zJiUkOPbBhbWqCttSTEY+WuoNBvjfLf8Rh0c6tdc02E42tpcUpVAICzYOEXQK+b3ioRcMb9HJBZIdfMvHKTsRl3ri9sNs+1EEiF5anRoXIC7NwtPwPO6hRU36stu1KpVRsN56stbf3pj96MfbDIMmvP9y\\u002f+aZtuJJGLG3ITgaD+OF0sskHa38fLOlenHhvd28N+4\\u002fXs3T+yktgXfvbTDNNtOJBEmZMWExPh7+LqpVWpup6C2pLn0eq2+SgwAQKVUf\\u002f7UX0Qifs66TIOX9hn1vmEX7hxGQ1NPaXmr\\u002fmYygYAfclG2WVmx+mw7MLgDCQA4fb5MKlWsX5Vu+MPnzYo\\u002fcab0yvVaw4T73Jlx+mw7ACBrclT2jbqmll7XSLgfvw53ez03JjU2xAeOIqOH3D9nzRyxFTNmhqa2tedaaQMcdTlMqtlRDzoOTeWMAJd\\u002fg05lZVaihYQ7AODkjSoo4X461+xANgAADgdWTsO+TrAXtJNxDXPSoiwn3NUazb5LxXcutv\\u002fQRZVaY25JGLtAXRRBLEMH+jEnMyM8MwN70NvUyZFnjzwPR425uzNuu3nqbTdPhRusM+SXD+1Vw7ft32s\\u002f\\u002fnYOADBlUsRTjy709WHDWwCgVmt+3nJeN1z90LFCKOH+\\u002fRe3GT4dAk8P5v13z7r\\u002f7llwQ79fv7sLDhlISwlJSxl6xnKYLx+HJkYH6g7fX+++mBIVEOrrDm0gkso\\u002f+Pu0\\u002fumLPx36\\u002fNFVsSHe208NlL7o6MUeUN7Q0Zcb9Owv5j40g+yXI5kp13qOGEaapZauGeyoWQJniyKYyVDElUQb5zotJFVLrmOOYsbItOpA48ehf0iPQMQ\\u002f\\u002feXthhGNWiPgiqsLG1\\u002fd\\u002fK1h3F5yTg9cr5IppPWPLlj34Hw6C65m3lbf9f3L\\u002f14\\u002fVWwYPPb3pZufWOzpzzEMmirLqTXNgC++NevuV1azTKamFlyq\\u002fO7FHY2VbQCApqr2Jht7+sWDeQe3nDeMEEnEDY8vXPvAPIZJWXB+j2j7l0f3\\u002f3pWn3bXarSfP7U1KMo3OgX+gEa9b9jFPbdOf+aVnY8+\\u002f0\\u002fahJClC5JnTY\\u002fVLYoyNDHRZue7VFS1AwBeeec\\u002fuAEAfd06nehIox\\u002fCYlEBAEKRbetpOK386oG5CxA\\u002f94HyO4gzoA9255wndtQ04R8PXHXkfVVnoRvaYAHDYYtVjgyXf4NOZWJMYJA3x0K13DN51Q8bV5U5nWupbt7E6KBAbzc4aldoJ+Ma5k6M+mznOThqbNvJvFvmT7T7Uninc6t6BYPsZ4YDdVEEsQwd6BEXJpMr\\u002f\\u002fjnMgCAxaS+9uJK3XByUwQC\\u002fo7N03bsztZqQXNzL9yMjDPLpyX8dOCqWqPpFUpuffvvDXNSZ6SEB3q5kYiEHoEkp7xp6\\u002fEbrT0C3cZMGqWbL77t3X9IRIJSNTAfKLu8SaXWEE2GgV4prtc9iPAfZD6KZYMkvCKZKSQ8RakZqHFTJypSa9UEnJ1P4yAKjaxObJRgJeJIkcwUw4iLiUgMJJKI+jU2a0uajdv7SMXyutK+sS3pcxKqCxv5PSIAQFcrt6edj1nZ3LBIN4lMDLN69C6egOd4sXxDhtXDrMHiMD7Y9YS5hVj9w7zf\\u002fOuh9x\\u002f49cKBXH1QpVQf+vPC7S+sMNgQptVov31xBzRI\\u002f4G3b1ptpvRVSlbMNydffO2Wb\\u002fMv2DyxtLeD\\u002f\\u002fVz\\u002fxhGGG60D3c9GZmMXZLVzZP5wFvrpy1JeXXzt\\u002fox5kqF6uNH\\u002f\\u002fj21EvQGqdO2DeGICEu4K+f7v3n32tHTxXnFTZ+\\u002fdOpzesnb1yXObRJ9BaWNhWJ5QCADWsmcdzgbQL9jW57epjcdHEl7b19RxdTIofVYEWGxmewheAEYpkjqgRcK204mQPf23ZJpQ3wnDlIgBfGOJoxxOXfoLNZkZXw\\u002fd6BmWeQ2taehnZuqF\\u002ffEadXKCkwfwcUOHi5VB20k3EN\\u002fp7siTGBuZWWBrl388V\\u002fn8i9c4k9B7lrtNqfD16Do3aFuiiCWIYO9IgLq2\\u002folsmUAIDICB9z2XYdCoWEx+PVag2lv+g5Mm75ebDWz56w43Q+AEAiV245en3L0evwRgAAANJjg+5ekvnIF3sAAPpsO51KJuBxvQLJH0ev37NssuH21S3dJ3L6ht9OiPI3bLIVnMiHkPHUNPe5hhG5RlohxH4bdpTPPavQGI0zTXLLohEGORUb04gkYnj8QLpTnzmFlN+o04+Jjp4QYjga2txAZsP8bERSEJHk2JsltsLhcW\\u002f9\\u002fbC5bLsODo974pNbmG5GszlyDZYfwZRzpqTGeAT3nHWZ5rLtOiQy8bUtD\\u002foE2lwea+snhwQGo6dxeNybfz5sLtuulzw1+qWf7zWMNFW17\\u002fvljGEEuFDf8PJgPv7AvP+2PvLi00vdOYwffj\\u002f3\\u002fa9n4Y2GjU4nAwBmTou55aYp0H+GC7QCAMBQUv1jhrR\\u002fWXZTbd2Cpk4eHEVGT4AXxv0wQ1otyC5vhKPD08MXv\\u002frbMTjqijQa7ekblsYXAwDC\\u002fW3e7TsPl3+DTmjF1EFKrhuuwXg+v9bcElsAADqFNC89Go7aG9rJuIwNc1LhkIlfDl3TD+myi51nCmpbe+CoXaEuiiAWoAM94tr051S6Eu0WnD5XplZrAABhoV5wGzL+PLl+RkrkIGNG\\u002fTxYb9+9eEpi6HM3z9H3NBwOPLdp9syUCADAd3svv\\u002fPnybKGDolcKRDLjl2veOTzPbo16nE4MD89xvCn2WqQhDsAYLrXKiLOaJbf8fY\\u002f1VoHLsohVYvOdu40jOBx+OneawwjLsmwoEdDRRtUuFynxGBVzMjk4Ji0gZeU59bpHxsyTKrGpoYNNDiHpbfPMLekpyGGG22+8ZqiNUXNSgX2Egc6p41XoyaSCHe+tNIwgonGoNzy7FI4apGIL4H+rUU3T0ucHGkYMSdzftL05WmGkV3fnlDI4VSpK\\u002fUNCpm4eF7Sz1\\u002fd4efrduh4Idw8bHHR\\u002fgCA\\u002fCI7X5WNOXTzpR41Wu37f5\\u002fWHUUQZxBnRUn9E+aL8g+BSCp\\u002f8pt9PXyjIksjo6WbbyH56Aj7L5W0dPPhqAEqmRgfarZKla1c\\u002fg0iAABfD9ak+BA4auCCwfKSZ\\u002fItZUkWTIoZ5nJM1hhXOxnXNndi1KCDwaVy5f9+PGQ4Y3o4alt7vtlzEY7aG+qiyBiCDvQIYl8hIZ40GhkAUFnVvvdgHtwMAABAoVDtO5T3yZd9d0mXLU4xbkfGIzKJ+MMz69bOTMbjscfBTEsK+\\u002f3FTb4eLADAprmp\\u002f7x2673LJt+5ZNKWF29emZWoH8Tw34WiW9\\u002f5Z8aj38x58vuXfjrc3X9usGRyvJ\\u002fHsIrxDp5wdyf7zvJZbxjpkbed6jCqnmFHGq1mT\\u002fNXQhXXMJjhsciXaunCxjUYLlmpVKhaajEmjpUaJFWjJoQYvqQsByOp2tPG43UPrAMQnepcf0YcDrfhsYVw1IzUGUbDk1VKVXtjt2HEkEyiuHLUKJk7Y8VEK4euz10\\u002fmcaES8lbcPTvS4ZLj+LwuFufXWbQPgioMA6\\u002fR3TpUL5hBIz9vqHVgvYOo9NEPB6Hx+MsjxAcmvmz4xl08s7\\u002fclrbeYZxLk+ssHiHxsVYrgh8rbThng93VjR2wg3IaEgI8yXgBzkcn82vLrfT59UrlDzy+Z7SeozdyAj47VD2yhd\\u002f+\\u002f1wtkPLAetVNnV9OljJ40lxIWT71Tt2+TeI6FiuA5Nf3apb41EqV2aXGU22g1j+OfYyrnYyro2Ax1tTLqakrv2VX49qNMPNCXZwRY9\\u002ftVdqMhDE7lAXRcYQdKBHEPuiUkibb+qr6fHFtyfuuP\\u002fXL787+c+\\u002f13buub51+9Vvfjz1wqu71tz8zeffnJDJlQCA2TNiF80bidMnxPmRScSXb5u\\u002f5+07H1mTlZUUFhvsHezDSQr32zAn5bcXNn79xBrDYQrRQV4PrZ722NrpSeF+AICkcL\\u002fVM5IGfpYxPw\\u002fWk+tnwFEbWVX5aKb3ujpxSa1oIH15oWsPHoef73uLwVZ2oNDIdjd9WS4wKlnjTQla5He7YcRVRRuvSlxb0hISY1QwSKPWlOfW6x4z3eh+IZ4U2sCoqOqCRpVSDVUFqTGu922YhHUGCZkRVibBAQDh8YFQRMQze5ZTfqPOMAkOAJi8MNnwqQVEEmHirDjTrLc5UGY\\u002fcVLkoKu5GgqO9gtPCDQsFHNh\\u002f405a42upsZ639BoNBvv\\u002fjEuxj8m0tfLkymWyK\\u002fl1La28e66JUu\\u002fjVqtKS1vFUsUYqm8q0cIADh9vszDncGgU4IDPbw8BxnPpcdm0V5+dvnr7++759Etc2fF+3ixuDxJQ1NPflHjtl\\u002fu9\\u002fO1lIZ2JRMi\\u002fEvq2uGogaLats1v\\u002f50Y7jc1ITQx3C\\u002fQy82bw6CQiWQi0QH3QRBLaBRSRlywbpl1c7Ra8NaW4788v8HC3AVrFNW2\\u002fe\\u002fHQ+1mFmQfGW09gm\\u002f+u\\u002fTdvstTEkKXTomflRo56HJ5Q3O9vOmFHw9KBlu0wO4ZT5d\\u002fgwgAYG5aJItOEUrkcAMAAAC1RpNT2TQrJfJaaaOifwkWUyE+nNQo+NzGEcbbTsa1rZs1YdupvEFLw53MqVSp1W\\u002ffvXjIH2h5Y+dTX+\\u002fr5IngBgdAXRQZW9CBHkHs69ZNUxVK1bZ\\u002fr6lUmoamnoYm7DpmDAbl5vWTN280qriNIME+nLuXZsJRK7x4yzyFUn34KlytOjbY+4MHl3u6MaC4raxKuONxhE0hz\\u002f1W+2q7rF4fPNe5q01at8T\\u002fLi+KHS4VtEBbLrh+pO03rsJorAGD6LY59EUy3obhxmNXaGwAmUpS9Fderittmb0mw3CDutIWqaivtH3UhGAAgLs32zvAvauVCwBQyJW1Jc1Q2tRwVUwagxIc5WfQOPrSZsTBIfOgGu4AABHfbMK9phgeUJY2Kx6KWDBhWoyVCXeVUlVdYFS9ZMriCYZPrTF1cYphwj3\\u002fYqVGrcEbrJU81vsGHo+\\u002feV3m9dz6E2dL5XKlG5seGuzxxv+y5hh0AJFY\\u002fujzRlNnPv3muO7BnZuzDFPzg8qaHPXTF7f\\u002f\\u002fe+1K9k1fIGExaQF+nPuvX2Gu\\u002ftw95hjyMJJsdtO5cFREyV17Zbz8pjweByFRCSTCO5Muieb7u\\u002fFjvD3jA7ySokMYFhc6AYxZ+GkGMuJBgBARVPXM98d+OjB5Sw6BW6zAl8k++XQte2n8kZ4HrQ5Go32cnH95eJ6MomYGR88KyUyKylMN+Nv+Dp6hT8fvLb3YtGg79Xfkz071aoKYLZy+Tc4zpFJxEWTYnedM1sYLbusaVZKpGFtGVMrRjBFMg53Mq6KSMA\\u002fsX7Gs98dgBtMnM2rufXdf16+dX56rKWlkkxJZIo\\u002fjuZsOXpd1b840AhAXRQZc9CBHkHsBYcD99w+Y8nC5BOnSwsKmxqbe4RCmVKpJlOIdBrZz9ctLMQzPS1sSmYkg44uNhG7IRLwb9+zeP2sCcdzKuvbe7Va4OfBzEoOn50aOejEO2tYlXAHANAIzHsi3tna8G6DeCD3Xym8USXMjWNPSuHMjmKlUPBwPtQaXfKWMsG1fO6ZLrnReFsAAJPIuTP8TS9KABR3VQQiPjIpWL++pWF9bZ2S6wY1Q5L7hjzHpIXqkqoAgPIbdVBS1TDvHDUhBGemttFoCU+w4W4NxSSRp1KYLU8J\\u002ffXcfdhsW5KtAeE+cMiM6sImqOR6RKJtVzXA5CVSkaypuiM0dmAM+1jvGzgcePDu2Q\\u002feDccNubFp5w49D0dNrF0xce2KiXDURESY96vPLYej\\u002fW5alXHTKqM7FgCAeTPj58204a6Mk5sQ6Z8RF5xTDt95sguNRiuVK6VyJV8kq2\\u002fvBf1VVfF4XEKo74KMmIWZsYMWmUUMLZkc9\\u002fXuizyRFG4wll3WuPGNP1+8dd705AjrJyI0dfL+u1C061yhrsaFKSaNQqeQRmYYoymFUnWxsO5iYR0AIMibkxEXNCEiID7UJzLQ09YTnV6h5GpJw6kbVRcK66xcpeDxddPNVf2zF5d\\u002fg+PWyqxECwn3\\u002fKoWAMDl4nq4oR8eh1s2NQGOOsx43sm4njlpUYszY49mV8ANJhraufd\\u002f8u\\u002f0CeGb5qZNTggZtJRfZVPXseyKPeeLBJK+YRwQHw7TQZ8j6qLI2IUO9AhiFwF+nDs2TwOb4TiCOFRKVEBKlEPSztYm3AEAVALj7vC3T3b8faHrP31QC7RlguwyQTYeR\\u002fCjhvrTIr3IARyyD5PoRiMwyXgqAUcEAGiBRq1VqzQKmUYiUQkFyh6esrND1tgqrRGpePqfZiiQFrU59H9skqfuqVKjkGskmqEu1qr\\u002fOU4uOiVEn1StKxsY8qxTmj0wTko3ihkAEJMaqh+LXZZTu\\u002fKe2f2bAABAjcEoZofWDBma4GhHjapuqTEq8hhodQJdJzDCGw6ZUV\\u002fWCkUME+VWCo2DX9JQ0Qr9nPHWN5Dhe\\u002fm2+be8\\u002ffegs1DtSKPRFte1F9e1f7X74sJJMfcsmxzub23NqGGSK1UiiVwkVYikcpFULpQqxP2P9cGKxi74Zcaulzc9891+Jo3CpJKZNAqDNvB\\u002fgwiZTiUPmrOwFYVEvHXBxG\\u002f+uwQ3mOjgip78el+ID2fV9KS0mMC4EB8KCeNQ3iuQlDd2FtS0XiqqL2\\u002fssDA8ikwkfPHYqtL6js8GqxA6Apq7eM1dvL0XigEAZCIh2JcT4uMe5OPmxWZ4sOluDCqZRCQTCQAH1GqtTKEUSeW9QmknV9jQwa1u7mnu4sE\\u002f0aKpiaELJxmtDuJoLv8Gx5XEcL\\u002fIAM+aVuxZz5XNXRVNXRbyd5MTQnzdR+7GJNrJuJgXbpmbW9lioYMZ0qUC2QxqZlxwdLB3qK87m06hUkgqtUamUPUKxG09wurm7qLaNss\\u002fcHJ8yEu3zVv10u9wgz2gLjrWz2QGpdFq+9+RQiyTiyQD707Y\\u002fx7FUoXWwkcFAADgzS3HPVl0c2+NQaMwaWQmlUzG6hUjAB3oEQRBEB1rj0M8RWebrK5NWtclhzN9OhqtulVa2yq1NHPWJjgcfl\\u002fL9yIVV6wSiFV8ldZoELGt3k4euEngzAzznj1tPAFXbDgu22hVzP5RzNEpAy8pv1GnfwwAkIpk7Q0Dy4o6YVKVwabBITsRGS9iY1NRdQAA28Paa2AhT2z4FIfHedheJdzL3x2KdLVwoch46xvI8IX4cD59eMWTX++Tm68g7CBqjebItfLj1ys3zk19dG0W5pXwEFwpabhYWKu7JNNfm+kuz+wy7b2bLz6bN\\u002fBVMgeHAzQKmdV\\u002fUccwuKa9d9nkoc1zBwDcsjB936WSQcvy6jR28r7ecxEAgMfjfN1ZTBqZSaPgcTi5UiWUyju5IivXuMPhwFv3LE6LDqSS7fMZ2ZFCpa5p6alpwc5mDp+vO\\u002fOde5fA0RHk8m9wPFgxLfGLXefhKAAAAI1G+91eS6nDkS+5i3YyroRNp37y8Ir7Pv7X+kO8QCw7eaPq5I0quME63hzmu\\u002fct5TBpDBrZ3DjxYRoPXdS1z2R0vvnvEl8kNXxrIqlcKFVI5YrBculWsXL6JolIYNLIDCqFSSMzaGRW\\u002fxvMiA2eOzEK3tox0IEeQRBkPBv8xOK32lfbZHUytVFWcQQ0S\\u002fqLFIwnUN6zrrQlJStG97irlasvD0JjUgP6h2DHpITicDjdWID2xh5ul8Ddm61rqi1pMRwj4IRJVRrTUdX5JUKjmbBUG0t9Ua0+0RQaL9xKY1j7QkNUOhmHx2k1Ax+WkAt\\u002f48Zb30DsIjM+5Pf\\u002fbfzfj4carbt8tS+1RvPPydxrZQ0fP7gi1A++qzQEOeVN20\\u002fnw9ERp9UCiUwhkSkAFx4JuHFu6pAvU8lEwtv3LL73o502XXJrNNq2HgEctQ4OB17YPHdBRgwAIDbYx3E5FCfEYdK+fmIth+mom76jzuXfoJNYNjX+6z0Xzc3615UXwMSmU2enjVDGRw\\u002ftZFxMYrjfu\\u002fctef77gyNQkZxBI3\\u002f6yEp3Fg0AEBXoVVANz++0i\\u002fHQRV37TEbn7+M3FKohzkq3I6VKzRVKuUK4SJFcoRqxhLtDoQM9giCIkxu8pliduHjks+3jVlCkr2EOus6gVHfJtYHRCnETw3D90wAZbrSgSF99U\\u002fmNev3jmpKB+\\u002f8sDsMv1Ev\\u002f1EmQHbOeOzBJuJvWf7eMQMQTSQQ4ikUsMDqNG1rCHZi8UN6\\u002fPqreeOsbiL3Ehvhsf+O2eROj4YaRUtPSc89HO8objao8IZiSI\\u002fyfv3kOHHUMAh7\\u002f5l2Lb5qdonuKx+NSIx1Sus4J+XCYPzyzPjJwbNSaGwKXf4POw4NNz0oOg6NWWJQZSyZadZphX2gn42LmpEW9cdciW4tE24pGIX31+OrEsL5Tyugga+suDgHqoghiDXSgRxAEcX6OPT9DbIXD46KS+wpwAwDqywfGjxjWDEnINFqIPD4jXP9YX+YbAFBrUKQ7OrWvzMj4NISVZKxMuKuNR3AMeYyR4fB2AADJ5F9HfQOxlUarvVRU9\\u002fafJ1a8+Nup3CFOIbcLrlD6yOd7Wrr4cANiYt2sCU9tmAlH7Y1OIX388PJlU41WCZ4YY\\u002fOCz2NRSlTA1lc2Rwe57F1Gl3+DzmZolWGG9iq7QDsZF7NsavwnD6+wV+k2Ux4s+jdPrkmNCtRHHL17QV0UQSxDB3oEQZAxwVEnZ8iQxaSGFl3pS401Vrbr4+W59frHCRkR+scAgPiMiOPbr+geV+bV6+N1pQMF98dbzRA6kyo3mBMqs31+qFpl1WxWaOy8VCw3fGolrVYrkxj9hkwO3fCpDuobiJWkcuWBy6V\\u002fn8i1deUlx+GJpE9\\u002fu3\\u002frK5tJozGoc2y5dUG6N4f55u\\u002fHra\\u002fMa5PYEJ8PHlgW4sOB4mkxA\\u002fkUx6FTbZtvZEcUEvGhVdNuWTBxCLdgrefybxCBzJgQ4c6imVYtsCAy0DOhf7DwqHDtncw4NDMl4q+XN7\\u002f48yG7l4qOD\\u002fX99OEVvh4sw+AIpPlQF0WcGTrQIwiCOBWJTJFX1VLX3isQy209c3jqJgfe40cJd6djmP1srGrTPVAp1fVlfRlSHB4Xlz4wbBkAYPi0qrBRq9XqKnc3VPS9HAAQkxqmfzwe0FlUbtdAPUe5cTp7UEqFSqmw6ovqZry8qkwi1\\u002f39DYODkkkUhhXVAQBMN+yEu\\u002f4x6huIOSdzKj\\u002fcdqbXeN1gZ1Dd0v37kev3r5gCNyAmFk2KjQ70euuPE0W1A1\\u002fV4aOQiJsXTLx\\u002fxRTMWhaJYX4UEtHWcxRbPb1hVlp04Jaj10vqBu4aOhqRgF81PemeZZN93a1dEHvIXP4NIhAiAb9kcvw\\u002fJ3PhBvNGcXi7ngvvZManyEDPrS9v\\u002fm7f5e2n8pX2KJ9NIRHvXpZ5+6IM048yOtALhxv6nE4roS6KOC10oEcQBHESIqn8+72Xd58vGvLJD0q4jy\\u002fRKQP1PcR8aU8739PPra6sRZ\\u002f\\u002fDY8PpLOM1hoNjfWns6i6quUSoayltjMo0rejqddwiPd4G8VMZxktINPbadtiSiK+tclKr0B3w6dajba7jecdYBQcVGdzLxRhG+fxdVDfQCzji2Rv\\u002fXH8bP5AiSHLPNh0NwaVSaMwaWQCYZAKY1otUGs0arVGKldK5EqeUMoTSW1dqO3PYzkb5qSg9Z2sERHg+dv\\u002fNh65Wv7LwavDX\\u002fOWTCQsnhz34KppFq7TiAR8cqR\\u002fTvnAAg+OgMOBuROj5k6MqmnpOXil9ExeddOw350Fvh6sVVmJq2ckW3jj9uXybxAxtTIr0fqEOwGPXzrFqIrFaHHVncy4RSYRn1w\\u002fc8Oc1O\\u002f3Xj5+vcKmdUcNkYiERZmxD6ycGuDJhtsAAADQqeQAT7eWboeXiUNdFHFO6ECPIAjiDPgi2QOf\\u002flvV3A03OI3BE+5L\\u002fO+GQ4gj+Yd5M93o+oRvY2Wbp59bVX6DfoP4SUY1QwAAODwuNi0s73y57mllXkNQpG9T\\u002fwhoAICHr5unn5v+6XjgH+ZVVTDwR2up6TBoHFx3Gw8OmREa6w9FGivabE24N1bCg3ciEoOgCEB9A7Gokyd6+LPddW3wzRuInwdr2dSElKiA+FAfDxbGRArrqdSa9l5BTWtPaV3HlZL6kvrBv2VSuXLH6fwHVk6FGxAseBxu2dT4JVPirpY0HLhceqm4TmxjdSwykZAU4bcoM27hpBg23ehuHKb0mKARSzREBno+sX7GE+tnNHXyrpU1FtW2Fde2N3Zwbb2LY4pEJMQGe09JCJ2REpEY5mfjjCO7cfk3iOhFB3nFhfhYuTT0jAnhw9z32pFr72TGpwBP9tv3LH7qppn7L5UcuVZe3WLDVWh0kNe8idHrZk3wYA\\u002fSRaODvUYg4Q5QF0WcGzrQIwiCjKKPt58Zcrbdz4OVHOE\\u002fIRLO5tnX4An3aV4r4BDiYDGpobnnynSPm6ra02bGVRU06lsTTZKqAIC49PCBpGp+w9z1mU1VA8mvcTiEOSo5+Py+G\\u002fqnPe18qUhGYw5+nq3TWA5nwM0JTwgkU0gKuVIfqSluSp+TYLDJ4AyXMAUAcLxYAeHehhE91DcQTB29wns+2tnWY2kmR0SA55M3zZyaGIq302k7kYAP8uYEeXNmpUQ+tHpaTWvPpzvOXSsduAOE6fDV8iEn3B9bN\\u002f2xddPhqKvD43DTksKmJYWpNZri2vbiuvbK5q6WLn4nTyQQy+QKlVKtJhGJVDKRSiKyGBR\\u002fT3aAJzvIm5MU7hcf5os5a96c+1dMGfmaP8E+nGAfzvpZEwAACqWqvp3b0MFt6eZ3cUVdPHGvUCKSykVShVimUKrUKrVGrdZogZZIIJCIBCqJ6MakurNonmxGkA8nxIcTEeAZG+ztVEsFuPwbRAAAf796CxwaO1xjJ5MaFXjj56fg6Ijb995dcGg0eLDpdy6ZdOeSSb0CSXZZY0VTV0M7t6mLxxfLpDKFTKkiEwl0KplFowR6u0X4e0YHeU2KD7F+\\u002fOynD6+EQ47kGl1UZzycyVz5\\u002fnE45OrG7oF+9Yyk1TOS4OgwLJoUu2hSLBwdhhXTEldMG\\u002f06bAiCOJvmLt6Ra31pLgAAlUyclx4dH+rLolPwONxrvx3VakGQN+e2RelAqxVI5DUt3ReL6kVSOQBgZVbi63cuHPhZDjN4wh0ZedEpIfqkantjNwCgunAgqZqQGal\\u002frBefYVSqGwDQUjsw0mocJlWhEeJarTbvfPm0pamGQQuqi60d20KmkJKmRuWe7fu8AABXjhZueGyRwSaDu3ykwPCp4acJQX0DMaXRaF\\u002f+5YjlbPvtizIeWj3NpmtOW0UGeH7zxJr3\\u002fz6153wR3GaguYtX09ITGegJNyCDIeDxKVEBKVEBcIMLIZOIMcHeMcHYdxxdgMu\\u002fQWRMGw87mfHGg01fPDlu8eQ4uGFsQl0UcX7oQI8gCDICjl+v1D9ODPf7\\u002fJGVnm4MfeSdP0\\u002fKlSoWnaK7FaojkMg+3nb28NWy\\u002fZdK6BTSczfP0Tc5yCBFe5FREZ0ykANtb+zRqDWNle26px6+br7BGIkqw7UxGypawbhPqsalh5PIRveTrh23lASEXD1qlAG3bObKdMOnFbn1HU09hhHL6stboZIyWUvTDJ8aQn0DMfXroWt5VX0L52K6b8WUJ9bPcGi2XQePx72weW6YnwfcYCyvymhKB4IgCIIgCIIgCIIgiDX0y1bTKKTPjLPtAAAKiQgAkCkGClEAANh06lt3L16cGQsA2H46\\u002f9+zNiT9hgYl3J1RTNpADrSjsaeltlNfsSQBq2YIAIDFYQRG+ugei\\u002fnSnjZeW12XvjV6\\u002fCVVGWxa5gKj+XFn\\u002f8vpabeq3GTxter2Rhsy5jNXTmSwBxaB1Gq1f3100KB9EH9+eMDwqbs3e+Zqowy+IdQ3EAhfJNty9DocNTA\\u002fI+bBoZZwGQIiAf\\u002fAykHmYg+51BqCIAiCIAiCIAiCIONZTWtfym5eerSXcbYdAEAmEQAAcoUKiuNw4H+3zGPQyACAr3Zd6BX2rY\\u002foICjh7oy8A9zdvdm6x+2N3XWlA2NXE7FqhujEpw\\u002fkW6uLm7rauLrHfiGebHe4\\u002f40Hc9dlGj5VyJV\\u002ffrDfMIJJq9X++tZ\\u002fcNQiGpO6+v65hpHTu7LzL1QYRsy5eqzwinE9mSW3T4fG5htCfQOB7DyTLzM5kOiRiITHR7xU6LSkMALe0sGleURWWkMQBEEQBEEQBEEQxMXwxTLdg+RwP+MWAPpHuIuwFlpn0SlLMuMAABK58uDlUrjZrszm9ZDRFZ0Skn2yGAAgEcoKr1Tp4\\u002fFmRjEDAOLSw0\\u002fuvKp7fP1ksVbTtzx6TGqYfptxZfLCCcHRfk1VfTNNAADHt1+JSglZcdcsg61gP7z8b\\u002fmNOjg6mPUPLzi546q+koxWq33rrh\\u002ff3\\u002fl47MQwo+2MFV2pev+BXw0jfiGe6x+abxgxhfoGYmi3xYLpy6bEB3q5wVEHY9IoUYGeFU0DEykgXKEUDiGIAYVSdaO0Kb+8paapq7WL38uXSGRKlUpNJBIoZKIbk+rtzvT3ZkcGe0eFeCVHB9CpZPhH2IOT\\u002fBoIgiAIMoq4EmnWuz\\u002fAUQDeWrNgfYY919tEkPGggy+a89HPcBSA5xbPvGuG2WnuY4tEocx48xs4aoWFidFfbF4OR0dcbkPrD2euFjS1y5Qqfw5rcXLMg7MnU0kod+pcJLK+ZDqHOVBtQo9JIwMARFK5VgtwOLg1Iy5417lCAMC1ssbbF2XAzfaDOo2Tik4N1SVVAQBn9\\u002fTViyBTSZFJwQMbGTNcG\\u002fPsfzn6x0Mr0q3VamViuUQoa6uHiz+0NXSzPZl0JpXGpOBMO6\\u002fTIBDxj7y\\u002f8X\\u002frvzQMfvfijvqy1rteXsV0oxvGAQCNlW0\\u002fv7E75\\u002fRQ7nFR6eRnv77jf+u\\u002fUKs0uohUJHt21afrH16w7uH5pv+WoFe046tje38+o1H3bQ8AwBPwz31zJ41JNdgQw6j3DcR51Lf3dvFEcNTAjAlmb8M4lDsb7vOGZP11kBAE0tEj\\u002fPNA9tGLpWKs8QgKpUqhVAnFsuYOXl7\\u002fovR4PG7d\\u002fNRn7jCaZjRMo\\u002f5r1DR3f7\\u002f9Qnldh0SmDPZz37h44tIZCfBGyLiE+sa4dTq78qUvjYoQDurP926LCe0rKoggCIIgyKBy6lvu\\u002fm2Xqj9L09jD++lsdklLx093rHXi1Nd4RCTgdR+TTIkx3Z\\u002fFoAIANFptF1\\u002fkw2FCrUHefUMS6\\u002frr0jgISrg7qeiUEP1jsaBvNGhsWhiRZHbZw7C4ABqDIhXLgcFLAADRqQM\\u002fClNrXddfHx0QC2QSkVQskEoEMrFIKhHK9OOgIe\\u002fc85PuAQ6HozEpdCaVzqIx2DQ6i0pnUuesy5y6eGAh4NGVMj123k2TT\\u002f17zTB4+M8Lx7ddyZibEBztx\\u002fFiycRybpeg+Gp1fXmrfpvUGbFMN\\u002frFg3kGrxtE0pSoh9\\u002fb+PXz2\\u002fQRlVK9\\u002fcuju747MWFaTGicv64UTG+noK60pehKlWGqXef2F1YkmC8LozeSfQNxcjcqmuGQATwelxEXBEdHBIdh6b6RVou9b0HGub2nC7\\u002fcelZq4\\u002f0YjUYb0H\\u002fOZBej\\u002fmt0c0X3vv6PVNb3C1TUd7z1wxG1RrNiFhpION6hvoEgCIIgCOI4nx+7qM+2612qarha2zg1EiVPnAiLTpUpRACAXgFGHXYPVt\\u002f4v4Z2rmnCnU7pm5fs6Jn3KOHupDCHHptbFVMHT8DHpIYWXKo0DOLwuOgJg+wXuJ0Cw1HP1tNqtRKhTCKUgTaePhgS6+88CXcAwOMfb26t6yrLqTUMqpSqq8cKrx4rNAzqBUX6\\u002fu+He87suW5Twh0AsPT2GWq15vuXdxreq1Ap1bnnynLPlRlsiOHWZ5dtfHwRHMUykn0DcXLVLfAEFENebAaTRoGjI0LZP9UDE6q8gZj6dc+Vn3dfhqPWmZoyMIlnmJzh19h9skCfUdXbevA6SqoiqG8gCIIgCII4iEShLGhqg6MAAACuVKOEu3MJ8GLr5vpXYlWyDff30D3IrWyeFAeXghD1l6NRaSxlLYYPJdydlLs328uf022QyAYADDr8OT4jAkqqBkX6DlqixLWRqaR3dzz2wQO\\u002f6suwWBaTGvralgfdPJkxBgPJrbfirlkBYd6fPflXb4e1y0Ky3RmPfbx5+vI0uMEM1DcQPZ6ob6kQTBwWRjmzkcEVYdxn1mOghDti7FxO9ZDT3H5e7LDAvjOqYXKSX6OxrRcOAdDSwdNotHg8mss6rqG+gYwVj27df7qsBo5aRMDjSAQClUR0o1E9mYwADivc2yPa13NiaIAHw1KdOgRBEASxiy6hWGNmKnY7XwiHkFEVF+JTUN0KAND9HxIV6KV7cOx6xf0rpkLlgMoaOnQPmFTHDk9ECXfnFZMW1t2Wr3+Kw+EMK3FjijPZAHM09HhDY1De+Ouho1sv\\u002ffXRQW6XAG7uR2NQ1j+84KbHFpLIRABAZHIwnoA3LfwyqPQ5CT+df23H18cObjkvtZgPpTEoi2\\u002fN2vj4YjdPeJKLZahvIDr6tbkxuVms6+JQ1S2WqqH5ebLgEDKOKZSqj7ecgqP9vDiMuAg\\u002fDzc6g0ZWKNVCsUwglvEE0rqWHrlCBQCYlgrv3IbGSX4Nc1RqjUarxQOUVEVgqG+ME5FBXrctn8QTSnlCKU8k5QulPKFUJJabSw2MOWqNVq1RyZQqnkTW0MPLbRhoivLxXJAYtSwlLsLbPvc1EQRBEMehk0lnXriPK5ZyJVLd\\u002f3l9j2W9YglPIuVKZF1C0dg6fKGRDc5mYnTgjtP5AIDWHkF1S7c+w66TERdEwOPVGk1DO3f76byb5w0Mb1Wo1DtP5+seB9qpIqg5KOHuvF797X44NJjJC5KPtH8HRweTODlyCK8agqlLUobzDxGI+CG\\u002fHIfDLblt+vyNU64cKcg+WVxd2NjZwpVJ5GQKydPPLTwhMH12wsxV6Qz2wIhgCo18qOUbg59hA4Yb7e5XVm9+eumVowX558uri5s6m3qlYhkOh6ezqL7BnpHJQanTYycvnEClD2Wo74j1DcTJyRRwbQFDagfPkDKnrKFDYPFOQKivOxxCxrGD50u6uRhr\\u002f05ODrt\\u002f\\u002fbTEKH+4AQAAgEajrWvpKa\\u002friA71htuGxEl+DQBASP8USEMRQZ5EAh6OIuMM6hvjWWiAxyM3z4SCGq1WIJTxhNKth64fPGfVVM6xqLqzp7qz54ez17Kiwx6ZOyUlGHuHjCAIgjgJXzbTl21pTOG0d7\\u002fnSSxdMI4KHxYDj8Nh3skO5LDhEDKqpiWHk4kEhUoNADh8tezxdTMMW9l0anpsUHZZIwDg0x1n69u5S6fEubPoTR3cXw9l1\\u002fSvlZoWHWj4KrtDCXdkHCGRiTNXpc9clQ43OAaVTp6zdtKctZPgBgSxEzqFBIcMdHLFcGhEHLoyyIoFUUFG959HzMXcmh1Hc3WPF0yLWzk72bgdGR1HLpTCIQCWTE949cHFeGj6nwE8HhcZ7BUZbLe+5CS\\u002fBgBg3YLUHUdzJf21BXXuWTvN8CkyPqG+gUDwOByHTeOwaV7uDLjN5Wi14GJl\\u002faWq+pWpCS+vmMPsX\\u002fEMQRAEQeyCRialhgTkNrTADQDMS4iCQ8ioolNIM1MiTt6oAgAcuFT6yJosAt5oAModizJ0CXetFuw6W7DrbIFhq87q6Y5dBgmNiEEQBBmrLBeNae3m8y0WNXKELp5oz\\u002fkiOGqAgMdnxMDrloyME1cqrpc06v5raufBzchoEIplJdXw2kQcFu2ZO+ZaSHPbnZP8GjpeHMbPb96clRbhyWEwaOS0+KBvX94wb3IMvB0y\\u002fqC+gSBaLdiXV7rum601nZaK1yEIgiDIEDy1MMt04uBt09ISAnygIDLqNs1Lw+Nw89Ojv3x8NZRtBwBMSQydk2bpNslNs1MiAz3hqF2hEe4IgiBjVYjF2iwarfZMfrWjb9sa0mrBm1uOy5UquMHAhEh\\u002fBm10RqVdLzGoCIs4h5KadtNpm\\u002fOmxDLpjl3BBuIkv4ZeZJDXp8+ugaMIgvoGggAAAGjq5d\\u002fxy7+\\u002f3bM+xteeE4wQBEGQcS49LHDLPTf9cOZqQVO7TKmK8Ha\\u002fdWrauoyRu6BGrJcWHbjv\\u002fbsDPM1W+3nrnsVPfr33RkUz3ADAgoyYZzbOgqP2hhLuCIIgY1V00CBlo\\u002f86dmP51ATTu\\u002fQO8smOs1cGS2ovn5YAh0ZEdWNXL18CR5HRVoe1vm5m0kgv6ewkvwaCIIhrW5+RhDcZgwYA0Gg0IrlCJFcIpLLazl6R3KhuEqZesfShP\\u002ff++\\u002fAtHoyBFZgQBEEQZJgmhgb8dOdaOIo4JQvZdgAAnUL64Zn1ey8U779UUtPaI5UrWHTqhEj\\u002fNTOSZ6dGwls7AEq4IwiCjFWT4oLxeJxGA4\\u002fM1atv7\\u002f3pwNWHVzu8wq9CpX7vr1MHLpfADcbYdOqSyXFwdERkFw9yJwAZFZ29QjgEgH1LolvDSX4NBEEQ1\\u002fbyijkU4iCXn1otaOrlFTa3788rvVzdaDr9SK+NJ3ztvxPf3LoSbkAQBEEQBAEAj8OtnZm8dmYyAECj1Y5wsdBBzngQBEEQp8WiUzJig3WLgZjz2+FrHmz6prmpcIP9FNa0vfPnCf1i3xbctiidQhqd4w5KuDsngRBjmQEPNzoccjAn+TUQBEEQHA6EeHJCPDnLU+I6+KJPj104WFAOb9TvdFnN+Yq6mbHhcAOCIAiCIIiBEc62A7RoKoIgyJh20+wUOGRMqwUfbzvz+u\\u002fHBGKMlOIwFVS3PvXNvrs+2G5Ntj3Im3PrwnQ4OiKUKnV+GcZy88iow6z4T6WQ4JCDOcmvgSAIghjydWN+tGHJt7etopPN7pB\\u002fOHsNDiEIgiAIgoy20RlpiCAIgtjF7NTIyEDPGqwK1IYOXi49n1+7YW7K2hnJvh4suNkWGq22vKHzQmHt8euV9e29cLMZBDz+tTsWkIkEuGFEFFW1yhRKOIo4AbVGA0XweNzIjz5wkl8DQRAEMTUnLuLrW1c++MdepVoNtwGQ39hW3tYV5z\\u002fIqjYIgiAIgiAjCSXcEQRBxjA8Hvf8pjkPfLoLbjAhkMh+OXjt10PXEsP8MuNDkiL8ogK9\\u002fD3YeLylrKJGo+0WiDu5ovr23rq23rKGzpK6dpFUDm83mMfXT0+PDYKjIyW7CNWTQRAEQZCxampkyDOLp39w6BzcAAAA4GhxJUq4IwiCIAiiVKm15ld\\u002fMUV2ZMFbB\\u002f5oBEEQZARkxAXftij9r2M34AYsWi0ormsvrmvXPcXjcV5sBptBZVDJRCIej8OpNRq1WitXqkRShUgqF4hlFtYrs9KtC9JvXTA6xWR0UAF3BEEQBBnTbp6csvVyfjOXDzcAcKW68ckFWXAUQRAEQRCXJpLKj1+vvFbWWN\\u002fW294rlMgVGo1tuYsbPz8Fh+wHJdwRBEHGvMfWTq9t7b1UVAc3DEaj0XbyRJ08EdxgP\\u002fcsm\\u002fzw6mlwdKRoNNrqxq7yug64wck0tPVeuFFTVtte29zDFUjEUgUAgE4lc1i0ID9OqL\\u002fHhNiA1JggDpsGv9JGcoWqor6jor6zqqGrrqWHL5IKxXKhWKYFgEwkMOgUdxYt0IcTGuCRGOWXFh\\u002fMolPgH2EnMoWyq1ckENl\\u002faQGbOMmv4YS6uKK8suay2va61p7OHmEPTyJXqBRKFZlEpFKJNArJ250V4MMO8HaLCfVJig7w4jDgHzH2tXUJcsuaqhu7qpu6O3uEYqlcLFPIFSoqmUSnkelUkheHGeznHuLvHh7omRIbyKCR4R\\u002fhKrp54oKKluLq1vLajl6BRCiWCSVyEoHgzqZ5uDGSovyz0iJS44KIhBFaHUqrBaU1bVcL68vqOhrberkCqVSmIBIIbCaVzaT6ebJSYgNTY4MSIv0I9v6VnGcvOt6QCIQ7sia+e\\u002fAM3ABAeVuXQqUerbJ1CIIgCIKMvGPXK97769QQJt+PGJRwRxAEGfMIePwnD6949tv9l4rr4bbRw6CRX7tj4fz0aLjBkYQSeXVjV3VDV1VjV1VjZ01TjwJrPcy\\u002fDmT\\u002fdSAbjpq3fkHqs3fOg6MWnc6ufOnLA4aR3Z\\u002ffG+jjZhgBAFzOr\\u002ft979WiqlYoDgDgi6R8kbShrfdSXu0\\u002fhwEOB5KiA9bPT12UFQ9vOhi+SHoxr\\u002fZ8TvW1wgZz5eylao1Uruzmiqoau3QRIgGfmRy6fkHatNRw422totFquXxJF1fUxRV19Yo6ucJurrirV9TFFXb1ioQS7HMjjUY75ZZP4agZP762KSU2EI4ac5Jfw5BCqZp555dw1KIHbsq6a\\u002fUUOOoAYqni0PniwxdKK+o7MCe3yBRKmULJA9K2LkFh5cBaxH5e7CkTwmZPis5IDBmxrKuDyBWqfWcKj10uL6lug9sAAABIZAqJTAEAaGzj5pY16YJ4PC4u3DcjMWT+lNiYUB+jF1jNCftGblnTzqN553OrTUcMKZVqiUzR0skvqmrddsSqWVY6Fr4yzR289U\\u002f\\u002fahh5+9FlC6bG6Z9qNNq9Zwq3H7nR2MY12AoAAFRqjaxX2dkrrG7suphXCwDw9WRtXpqxam4y1fySm1Yalb0oAsmKDoVDAAAAlGp1C08Q7uUON9iojScsa+ssa+sqb+vsEIgEUrlQKhfK5QAAEoHApJA9GDQfNjPMyz3Wzzs1xD\\u002fC2wP+EaNBrdHm1DdfqWmsau+u7eoVSOVihUKrBXQyiU2jBLm7hXhyEgJ80sMCw7080OokCKYx0fnVGm1hc9vl6sbK9q66Lm6PSCJRKFUaNZlIZFEpvmxmmJd7YqDP1MjQaF9P+MX2JlOqrtY0XqlprGzvbujhCWVymVJJJZGYFHKIJyfa13NKZMjUyBAGBb4T7+hzpDHxUSJWUmu0Rc3tFe1dle3dVR3dvWKpWK4QyxVSpZJCJNIpZDqZxKJSgtzdQr04YZ7uUT6e8QHeBLyj+lhtV+8\\u002fV\\u002fOv1jS18gQAAC8mY0Kw3+LkmLnxkRbWvmrm8rddK7hc1dDCFag0Gm8WI4DDnhUXviQp1teNCW9ttbyqlld+PjL8ufgOhRLuCIIgroBMJHzx2Oovdp3\\u002f+0Qu3DYaspLDX7h5TqA3nF92kD\\u002f3ZxdVtVY1drV3C+A2p9HZIzRMuAtEsg9\\u002fO3HqWqXBJpZotaCosnViXDDcYFFJddvOY3mnrlWo1PC6oINSqTWX8+su59elxQW98sBi07sFlmUXNjz50W44OuKc5NdwfnKF6p\\u002fDOX8duK5LJduqvVuw93Th3tOFszOiP3hqJdw8dhy5WPr9joudvUK4YTAajba0pr20pv3P\\u002fdmRwV7r5qeunZ8CbzSm8ITSd386diG3Bm4YWZX1nfqEe11LzytfH6xp6jbexKyOHuHnf53ZevD6B0+uTIzyh5utM4p7UQQS5uXu58Zq52N8PVu5Q0y4qzWa63XNp8pqTpfVtPEwfrKOSq2RKpRdQnFFe\\u002feFynpdMIDDXpQcvSkzJdjDbp+sRKHMePMbKHjwyTswk2JciXTLxRv\\u002fXi\\u002fiSTDmafGlar5U1tTLv1LTqIt4MunzEqIWJ0VPiQwx3nakabXgjX0n\\u002f71eBDcAAADYNDnl1RVzzWdvnM6O7MI3952CowC8s3bh2vREODo8X528\\u002fMOZa3AUgC82L1+YaNsYF2fo\\u002fAfyy17496hhJNrXc9\\u002fjtxtGAAC9Yum2q\\u002fk7rxd1CcVQEwBAqlBKFcpOgaiouf1AfhkAIMzLfWPmhI2ZE6gOqM7cKRD9cj5nX16pUAYP2tAlQzsEout1zf9cLaCSiMtT4u6ekRFmsHcyTcEPnzN8lGPFQ3\\u002fuPVdRB0etUPquAwuPmMptaDlYUHG8uLJXLIXbAAAASBRKSf\\u002ft\\u002f5KWgcncDAo5PSxwckTwrNhwzAOHOeVtXWu\\u002f2WoY2f\\u002fE7VE+ffeuNFrtN6eu\\u002fHwuW20w\\u002fKKZy2\\u002fm8g8XVsT5e3+ycanpP6fWaL8\\u002fc\\u002fXHs9lqzcAZVFMvv6mXf6226dOjFx+ZO+W+WZkEi0vKmfPH0RzDbHtkoGdkgJc7i0YmEQg4R91ysJX9d0AIgiDIqMDjcU9vmDU5PuS9rafabc8W2Ut0kNcja7JmTIiAGxxp+9EbvXwJHHUyHT0DH0prF\\u002f\\u002fx93c1d\\u002fAGmq2TlWbtMEmJTPHoe\\u002f+W1vTV6x+OvPLmO17+67tXNgx53C7i5Iqr217\\u002f9lBLJ0ZxZFstmDYwGHlsUShVb3x35HS2tffALKhp6q5s6ISjY0pJddsLn+\\u002fr5mGkNkZYRf9f8lpR\\u002ff++2C+VYY8ut6CLK3rwnR0v37to8XTbpgehvagTCuBgJ9z5UoyMs2U1nT1br+QfKaoU2P5anVae4PcLN\\u002f64mLt6YsLTi2Z4MIZb9s2cdr7QNJGx7VrBJ0cvSM3Mt8DUI5LszC6s7uge3YS7Vgve3H\\u002fKXLb9tmlpLy6bDUed29IJsR8cOidXwVMq9+eV2jfhrtWCg\\u002fnlcBQADp06J86GE29n7vy1XVy5SkUhDuSptl0r+OL4JdPstgX13dwPD5\\u002fbcvHGKyvmzkuIhJuHSqPV\\u002fno+54ez16z83smUql05xXvzSm+blvbE\\u002fCxd2SsqiUgmEhQqNbz1kDjzR4kMTUVb14dHzl\\u002ftv1FqK7Fccb6i7nxF3cdHzicF+a5OS1yWEutGo8LbWaG8rUuXcNdqwSt7ju\\u002fNLYW36Ffe1rX5xx1b7llvuIC5WqN5dseRY8Vmz6vVGs1XJy8XNrd\\u002fc+tKCwPkzSmq7ZuH6u\\u002fJ\\u002fvzRVdFBXsbtTgEl3BEEQVxKVnL4rrfu+Ptk7j8nc\\u002fkjWJkaj8NlJoTcuiB9aiL2jG+kvadv9H1bl+D+N7d3c20une\\u002fGpCVFB8BRM+hUMoVst6O8SCJ\\u002f+qM9v7y12c+TDbchY9z+M0Uf\\u002fn5SbfvoXVMcNm1mut2ubEeSRqN9\\u002fvN9Vwvq4YahWjtvDA9vb+7gPfPJfzwh9qAqEongwaazGFSZXMkXSs3VZbKXyvpOAEBRVesLn+03V8tlUEql+p2fjgb5cZJsGeeO9qJOyN1MMkiGVT4Ok1YLzlXUbr2Sf7m6AW4bEo1Wu+dGyYXK+s9vXj4x1NpjtE06+EYnDDKl6pnth86U1xoGrTcz1to7946g1YK39p\\u002famV0INwAAALhnZsYzi2bAUafHolLmxkceKaqA4tfrm9t4Qn8OC4oPWV5jC+a6wcsmxJEIBDhqYkx0frVGU9XRkxToCwCQKpQv7j52vLgK3sg6HQLRY3\\u002fvv2tG+rOLZtqezYMJpLIn\\u002fjl4rbYJbhiMSq35\\u002fcKNazVNX92yIoDDBgC4M2jQl9pWY+KjRGyl1mg+PXrxz8u59iqTUtzcUdzccb2u+fObl8FtVihv61qeEgcA+P1ijoVsu45AKntk6779j9+un8PxweFzFrLtemfLaz8\\u002ffnEIe355\\u002f3H\\u002fzsUZzpltByjhjiAI4npoFNK9yyZvnj\\u002fx8JXSfZdKSusHZpnZHR6PSwr3m58esygz1suNATcjBjp7hECXc\\u002fl4D2a2HY\\u002fDMRkUNyZNqVILxTLd0qmGpqaG2XT\\u002f\\u002f7blmXllzXC0n68nKzzI041JYzOoBDxeKJZ19ApLatrMDSDt5ok\\u002f+OXEFy+sgxsQGxEJhLtWT+ELpTyhlCeU8oVSnkjKF0qHULBi+LYfvfHFX2fhqAl951Sp1SKJQiSRYV4LLJ2eSBqb6xb+9t8Vc9l2AgEfEegZ6MPxcKNTyEStFgjFMoFY1tjGbergmlY2BwAkRPjFhA1lILMz9A2FUvXUR3tMs+0kImHJ9IRlMxMTIvxIpIFPubmDdymvdtuRG+YqerEY1GBfTqAvJ9DXLciHE+jLiQv3hTcyjyeU1jR1v\\u002fL1QdNsO4tBjQv39WDT3dl0hUrFF0rL6ztbzMwcUqk1r3x1cOsHtzNtWcUU7UWdDZNiw8eHSaVRv7HvVKcA4yg8HF1C8X1b9vx851pH5KraDX5boUz+4B978xoxln6x0mxbhkLbl1YL3j5weoeZbPuDcyY\\u002fPn8aHB0j1kxMME24a7XgQH7Z\\u002fbMzofiQ7c8rg0MAAABWT0yAQ1jGSucvb+tKCvSVq1QP\\u002fbk3u87sHthKv1+4oVRpXlo+G26wRa9Ycs9vuyvarS1oZqq0tfPWn3ZuuWd9iCfHg0EfZsJ9rHyUiPUUKvVjf+\\u002fXl\\u002fqxo5smJcEh65S3dQEAGnt4X564DLdhaeMJvz555X\\u002fLZgEAzlXU\\u002fX0lH97CjN8u5CybEGc4Ot4aYX4eZQ0dAAAqZbjr9DgOSrgjCIK4JjqFtH52yvrZKY2dvEtFdVeK6wtr2+wyFJFFp8QEeceH+qbHBqXHBDFo9q9FaKtPnlmjNDM3s4cvhhYvBQAsyoq3afypl\\u002fvQV3TR6+gVAgDe\\u002fvFoXUuPPojDgSkTwqelhk9KDA0JcDfMp\\u002ffwxNVNXTklTVcK6qobuwAAWam2XSRPSw2PDPKqaR64PAjwdps+MXJaanh8hK8bE2OcoEajvVJQt2X\\u002ftaJKjIv5q4X1RVWtydaNsk+M9v\\u002fxtU1w1NiP\\u002f17Srzmpg8fjvn9lo2HEgqiQwc\\u002fMnOTXMITH4x64KQuOAiCWKnhCaXF16+vfHobbHON0duWXW8\\u002fC0X4cNm1GWmRGYkhafJCXO9Owc0plyrrWnuqGrhulTTmljT39hUdWzUnWbzOG9PDEf2Ctohwd4n3riknT0yLN7eUUSlV1U3dOcWN2cUNBZYtS2bcXWj1vgvGG1nKGvvHv8fymdi4UDPB2+\\u002fiZ1ZHBGAOIgnw5GxdPXDNvwoe\\u002fnjx0oQRqffWBxctmDreiwgNvbRcZH7wWTI1bM29CSkwgwWT1udYu\\u002ftYD1\\u002fefLTK9S9HeI\\u002fjvVMFtK2xIfo3uXhQxZa6shPWVmkkEwm1T0z49dgFuGDapQvnktoP\\u002fPXqrJ5MOtw1PR38VHYVKPcxsuz+HFeOL8UUeGe8cOL39WgEcBQAA8Pj8aQ\\u002fOmQxHx46pUaE+bKZpAnS\\u002f\\u002fRLuCpX6SBHGcNFoX8\\u002fEQKvuYo6Vzl\\u002fe1gkAeHHXseFn23W2XsmLD\\u002fBeM3GIByOZUvXQn\\u002fuGk23XaecL7\\u002f5t986HNw\\u002f\\u002frzRWPkrEei\\u002ftPuaIbHuwh9vUyCHOPtcl3D8\\u002ffkmpxr7KNrXzeuH9szOZFPJ7B8\\u002fCbeZpteC3CzkfbVgCN1i0aV7q678dAwCcyatePtWqm44jz9pTEwRBEGSMCvHhhMxLu3lemlYLmrp4lY2dTZ285m5+R6+QK5TyxTKxVKFQqZQqjUarIeDxRAKeSCCQiQQmjcxmUNkMqjuL5uvOCvBi+3uwQ\\u002f3c\\u002fZ1vOnxCpB8c6tfWhTHo0seDlRIbCEcdrKNHuP9s0bmcan1kelrEAxumR5tJ13pyGJ4cxuTksEc2zahr6Tl8vmRKis3TwG9ZPumtH46wGNSlMxJWzk7GzJcZwuNxWWkRU1PDf91z5dc9V+BmAP45nPP+E1YticmiUwb9I7uxMEoKDvoqmzjJr2ENBo3MoJHFUux0kt21dQne+fEY5kB1Nybt1uUZ6xem0cyMGaFRSQkRfgkRfivnJAMAiqvbTlwp7+oVhQbAVYbHhIPni\\u002fW5cr1lMxNfvHch0SSfa4hMIur+DrevzJTKlGeuVx27VFZW177Q3oXsR6xvSGXKv0zuPTBo5K9fXB\\u002foy4Hihsgk4qsPLhaIZdAiq99sOz8zI4ply6ByU4bZdj9P9usPL0mLCzJoNxLg7fb83fPnTYn93xf7hWK4rtq\\u002fx\\u002fM3L80wTdNbMIp7UcQU5uqgAAC2LQVqN2Qm\\u002f3j2mkiugBsAAAAwKeRYf++EAJ8ADptNo3BoVDweL5DKukWS4uaOGw0tpklVvW6h+NNjF95btwhuGB79CPdX9hw3l20P9eR4sRgcOtWNRpUpVQKZvKGb28zlQzv5WbG23bm3o3cOnN5mJtv+7OIZd8\\u002fIgKNjCgGPW5ka\\u002f8v561C8tqu3uKVDVyBlmM5W1GLecFptSx55THT+8rauPTdKjmLdXfBhMydHBMf6eQW6s+lkslKt5klkLVz+tdrmwuY20\\u002fuseh8cOjczJnxoSeQ3950qam6Ho\\u002f0IeFx6aGBmRHCYlzubSlFpNEKZvKmXX9jcfr2uGar23soTPP73AQCwzr1sNCY+Sif0wU2LOwVirkTKE0t5EilXIuWKZTyplCuWciVSnljWLRJbX6PMXvbnlR0uhKfI6NHIpIywwBg\\u002fr2APjhuNQiWRcAAIZHKBVN4hEFa2d1e0d2OubgIAuGlSsi2zo430iiW5Da0nSqp0TwPd2WvTkyK83QEA1+uad+eUmC5cIVOqjhRWkAj4pl6ePpgeFrgwMTrQnS2WK85V1B0pqjC9+jhRUi1VKGlk7OsOTMumJBTVtO06V3g2r+bTHeceWjWVTsUeHzOKUMJ9cNm9ubuaD3TKujwpHisDFs\\u002fyngZvgYy4In7Ze2Wfw1ETFDxlS+bXcNQV3Zn9mFwzcAb2YOSdqKMipnA4EOLDCfHhwA12gvqhZbXN3R\\u002f\\u002ffkr3GI\\u002fDPbhh+u0rrR30FB7o+cjNM+GoFRZOiyPgcbMnRdtUiRiPw923bhpXINlzEr42vlHapNWCIZ+6Ic7j\\u002fV+PS2QY12mxYb6fPrfGi8OAG8xLivK3qTS2s7lWCJdADfZzHzTbDqFRSUtnJCydkSBTKKm2XDA4lauF9abFZO5dO81ytl3vxXsXXn3iJ8O7F1yBZO\\u002fpwtuWTzLYauj8PNnfvbohwNsNbjCRnhD89iPLnvp4N3RR19krvFbUMC3VhpuXaC\\u002fqVLqE2EmiAFvKZLOolJsyk3+\\u002fcEMfweNwqSH+c+Ii58RHhHt5WPh0NFrtydLq705drezAHu66L6\\u002f0oTmTgz04cMMw6Ea4b7tWcLDAaMFMPA63MCl6bnzk1MgQzEyiRKEsaGo7X1F3try2oYcHAJg1SgXc3z1w5p+r8HdB58Vls2+blgZHx6DVExNME+4AgH15pXZJuGPWkyHg8StT4+GoeWOi8+c2tOY2wDeW0sMCH5w9eWpUCGZxxccA6BCIfj6XvSO7UI1V6k0ok\\u002f987rqu2IVNTpfV7MsrhaP91kxMfGTeFF1ldlMShXJvbun3Z672iCT6YG5Di8EmQzcmPkon5EajWl5B9OMj53+\\u002fOPBXHQFqjeaLE5fgKAAAgFBPzsNzpyxJjh30jLRXLLlc3XipquFydUOXsG\\u002fiKZGAH+a6zXf\\u002ftktXUH5D5oRXV8wl4Pt61aKkmFVpCff+vsf0LuCe3BJR\\u002f\\u002fUFhUj8eOOS+QlR+tYVqfHTo8Ne2n1MH9GRq1RXa5tsWvwZhwMv3jovPsz3h31X\\u002fjmZu+d8YUZccGywj487k8OgEokE\\u002fW9r2YwJNvyjtrLhxHF8apG2fVn1k0arAQC0Stt\\u002fqNkSSPOPYo7OmQqCIAgydmk0Wo2mLw\\u002f1v3sXrJw9EsU3iAT8oiwbrsQMPX7LrFNXK\\u002fkio+ybQCSrbuoyNyofGSuyixqyi+AsMwAgIzHk46dX06hjNV88NPWtPVBk3pSYQa9tzBm72XYAQHZxPRShkIm6eQzW8HCjz82MOXbJKCu0\\u002f0yRXRLuBAL+0+fXWJNt15mSEjZ\\u002fStyJK0YJSgBAYWWLTQl3tBd1Hp0CUVMvH44CQCTgA92xE17m3D5t4l+X81RqTYS3x4bM5BWp8e50GrwRFjwOtzAxelZs+Fv7T\\u002f93A66hBADQasHO60VDWP\\u002fNgna+qKGH9\\u002fGR84bBhYnRj82fGunjaRiE0MmkqZEhUyNDXlg6K6+xdV9e2eSIYHgjx3v34Jm\\u002fr+bDUQBwOPDaynkbM4dYg8vZRHh7TAj2K2yCh0IfKaz439JZBPwQjyk6PInsfGUdHAVgRkwY5r0WC8ZW5wcAkImEl5bP2TBpkCORL5v5yoq5M2LCn9l+SGKy5gcAYFdO0RMLptk0hFahUr9z4AwcBQAAQCURP7xpyYLEgUyiKTqZtHlKyrKU2Bf+PXq+AuPjG6Yx91EimM5X1GGOT58ZG\\u002f7ZpmV063qsB4O+PCVueUqcVgtuNLQcyC87Vlw1NTLEg2Hb\\u002fgGiUKkBALPjIt5YNQ9qSg7ye2bR9Df2nYLiZa2d+scfbTDKtuusnphwuqzmZOnAnG+d3IYWmxLuOukxQTNTInafK5QpVBcL6y4W2vxFu\\u002fHzU3DIfoa13x8PcrgFumz7QKQ3z\\u002fApgiAIgthk2YzEkcm2DxOVTMJMtOkKyiNj2pZ91+AQAO5s+tuPLhtv2XYAgOniFt4cOyzbMBblmiwQmhDhZ66EPaaMxBAo0tTOxVwm2lYbF02MDBqknAtkw0KMYbOFWKXVHQTtRe3rWq3RYht6cf7eFKJtw8h82cyXls3ecu9NB5+84\\u002fZpE63MUulRiMS31yyYaWa0+OnSGjg0PHyp7L7f9+irHBDwuFdXzv1i83LL2XZIWkjAG6vmWV\\u002fs3l7eO3gWc+k8PA739pqFLpNt18GsEt4rlg6\\u002fNPORogrMeilWLpdqaGx1fjKR8OMdawbNtuvNig03VwlaolCeKrPt1\\u002fvnWj5mJhSPw322aZnlbLueG4367a0rh5BJHNTY+igRcy5UYYyA8WIxPtm41MpsuyEcDmSEBb65ev6FF+9\\u002fdeUcuNl2BDz+xWWz4SgAAIB1GcneLLMzYmfHRZj7jtyE9Y0ua7X51Gjbqby1r27Zfa4QbnAaKOE+CL4CHkbBV2Lsc5ER5kl2n+41OYWTGMEI9aZ4UgnDqg2KIAgyYph0yjN3zoWjzmqSSe4MAACN1kTGnMY2LrRIrM7\\u002f7lngzh7WQJgxyrRUfZc9EsRjUVcv\\u002fMajQ32giGWYA7cr6geGOw0NhUy8e80UODqYpOgAFgOeOV7dZPMV3XCgvagd7c3FLuyQGT6UUdubJqdkhptdDGBQeBzuvXWLMPPXdd1cwwoSdtHM7bsmJeDx39666ubJKcbtTuqDQ+e2XsEYrEbA495fv2iYtQ6c0NIJsZj3fvbnY1SDsQlmPRkOnTq0NO4Y6vxvrV5g67SMufGRy1OwV1K5WFUPh8xTa7R\\u002fXMqFowAAAO6ZmTHblr88AY\\u002f\\u002faMMSc5VnhmMMfZSIObpVgiGr0uKZFBuGO5giEQjDHN6uMzUqJNgDe3IhAY9bkhwLR\\u002fvdOzMDDvWbEhlsOumnrrsXilh2obD2k+1nNVglpJwHxlcLMcQhw33Lk+IORZCRF0DzeyTqHsOIUqMSqkSnO8\\u002fvbj5oGEcQBHEqq+YkO+GKLubEhmPk2gQi7DXrkLHCtMgGACA6xHtWBvY4FJfnzWFCvfrE1Yp71k4lEQmGQZenVKlNy\\u002fq7MeGEtWVsrO15w84vT0+LYNq+8ioOBxIi\\u002fK4V1RsGRWL5SJZQR3tRe8mpb7lS0whHAQAALEqKhkMjwoNBW5ueiFmavKyta3p0KBy1h8fnTzM3InWEDfod+vDwuT8vY+QrCXj8xxuWLE6OgRvGPhaVMjc+8khRBRQ\\u002fU1YrlMlZVJt3YjqNPbyCpjY4CsCyCXEkwugcp0am88+Nj1yZNpSKXnfPyIAWPNDJrbdhhtOZ8poOPnwTGgDgy2Y+NMfmG8AMCvn5JTOf3OZ0mYqR+SgRC7qFGDc2on284NAosdwH0sMCMffzXixGWkggHO1HIhAivN2rOnoMg50CkUarxVykAdMfR3P0jykk4uzUyAlRAX4eLDqFZJrNHy0o4T6IDPfUHU179VVlSHjiNM9M400Qp0DCEz3IHA4JvkGCIAjiVNbOGxuj0nRYDCoeh9OtlqNnWn8DGVuuFGDUN1wzpnqmfU2IDahpNlo3rKWD9\\u002fGWU\\u002f+7Z4H15\\u002f0uQCjG+GpTTYb\\u002fW0bGGignk2GU07XJgmnYwxUH5eMBVwfSaLViqXwI6fuhQXtRuxDLFe8eOA1HAQAARPp4Jgf5wdGRMjc+EjNR1dI\\u002fIN2+JkcE3zvTDisi2IXl9So+Onwec3QwiUD4dNNS06q+LmNNeqJpwl2uUh0rrlqfkQTFrWRugPyaUZ0i4OjOj8fhnl40HY5aJ87fO87fu7wNns\\u002fUwuOL5QqGdQOHDxXAn6POndPTMYeED2p+YlSYl3t9NxduGG2O\\u002figRy1QajGpRziMp0NIRNjEQY1QBAGBSWJDlM+hgDw6UcFdrtL0iiZf5GjWQmpa+lwd5c358dr2fB8u43Sk4S+LfaQXQ\\u002fJ6Ivj+YHkjBk8MYIc\\u002fFPhpAs9ThEARBEMQcHw9WoC8HjjoxPA5nWtFb69xz9xDL5ApVaS28pBthGMtCuoC5mRgDLfefKXrsvX9rmowS8a6NQsbIIIhsTA2LsLL2nGGXKkqOCoBD1mEyMBLrtr6p4UB70eFTqTVP\\u002fHOwoh37y3jPDLOT1kdAakgAAY+RVOgSiuGQPTw2f5rlFMZIYphPuH985PyWSzfgKABkIuGrW1a4cLYdADAtKsSXDd\\u002fnAwDsz8MuiGSNA1gJ9xhfr4QA7FTXyHB0558RExbh7QFHrZYWgnHU0GpBC08AR7EoVGrM+jMkAmHIpZDwONzKVGc813L0R4lY5kbHmBpY0Q7fLhotUT6Wvoa+bCbm2JSkQF84ZAxztWeBzIbTMyKhL5t9+6J058y2A5Rwt0amx8SPJry+JfOb95NfSXazeVkSBEEQBNGZEINx9u\\u002fkcFhnUcjYVd3YZVruMCLQ06aFMV3MpKTQuHCMC4MbpU23vfjni1\\u002fuL6qyYRL62MWgkU3L2Xf22LZ2UXs3Ri7Dww3jssp6HDbNk2PtiCcI5l0EzLUHHQftRYejsYd3+y87L1c3wA0AAABi\\u002fb1XjGoCi04mudEwFiqUKoc7q8NUUqDvxFAnOotgmimQ8vHR879fxMi2U0nEb29dNcs56uE4Dh6Hw6yCcqOhpYWLsXscVG5Da1MvxkDj1emjnJdwdOdflTasN5hoJt\\u002fXJbAqiVzQ1CaWwzXWAABTo0KGXBoIADDPKe82OfqjRCwLxCruvy+vTCAd\\u002fepzLCqFTcO4H6BHwOMxU+fh3u5wyBjmGr+YXzpzMuL6Vndw5gqQKOGOIAiCICMkLNATDiHIyKrtn4BpKD5ivM\\u002fee\\u002fn+RZjn6xqt9kx21X1vbLvzla37zhSaljh3MaZLpOZXtkARy\\u002fIrm6EIHocLDxjWri8yyFkqmSIjqay18419p1Z8+Wd+I0b1agAAAY9\\u002fY9U8zIGZI4mDNThRoVTDoWEbckESB8Fc0O\\u002fTYxd+v4CRbaeRSd\\u002ffvjrLYi1gl7F6IsYIaK0We6D6oDDryRDweGcYK+24zk\\u002fA44bZWzCTgMDqjF5eI\\u002faN9mlRGOtgWy\\u002fKx9ODgZFnHHWO+yiRQaWHYdQ67xVLntt5RKZUwQ0jy9uKAi90rNlOAe4YdxEMYZ51K1Q2dLlH1kzjMGkAgCPXyo3r9jkRjEEfCIIgCII4AhuruMHI02i1ze28xrbe5k5eN1fcyxeLJAqJTCGTKxVKtVyhkitUcqVKrlAplKoRHgqKOBrmgOXoUG84NM5Eh3i\\u002f8sCit344qjbT4cvrOt7\\u002f5cQXf52dkxm9ZEZiRkKIS45anpwcWmicYW\\u002fp4OVXNKfGBhkGzVGrNUcvwrmhhEg\\u002fDntY+YXhV6SxO7QXtSONVqtUqyUKJVcsbecL67t5JS0d2XVNgw4Hfmbx9JRgfzg64mhYuQYtsP\\u002fV\\u002f0SspMwoMi2E\\u002fdmxi7+eH1jFTo9BIf94x+qJoc71+ztOuJd7SrC\\u002f6TKn+\\u002fPLHpwzGQpaplSrjxZVwlEAZsaEeTBGf8fouM4f7es1nIHkAABzL5errMpglrR0wCEAAAATza8DaQ0cDiQE+Fyswp6yM4oc91Eig5qfEPnp0QvQWi8AgAuV9Zt\\u002f3P7GqvkTgkdtWIy5G1eGKESMrPKgmXpSf0EYQzaVsw\\u002fy5vz2v42v\\u002fnIku6zx2e\\u002f237t8cnwo9ryWUYTxp0EQBEEQxBGYWONHRkxTO\\u002ff8jZprRfUl1W1iqVUDfBDX080TwSEA3J0voTnyFk2L93ZnvvHdkc5ejHsSOlK58vCF0sMXSv282MtnJa2ek+zljlGrd+xalBX\\u002fy57L0EXft9svfP\\u002fKRn2tTAt2Hs8zLSkzB6tEvk0YVDivN1rQXnSY0l7\\u002fGg4N1a1T0+7MSoejrotFpUR6D2umiN0xjb+YX528\\u002fMv564YRHRaV8tOda5zh1shIWjMxwTThXt\\u002fNLWput2mN37PldZhlJTAH0buS4Zenxxx1C6zO6NV2YSxtisfhonyH+zWM9vVywoQ7MoqCPThz4iNOldbADQCUt3Vt+mHbnPjI+2ZOSg0Zhb2ouRtXhkzHquNwgE0d5JqXhIdfBQAwvetgmSeb\\u002fuymOd\\u002ftvXQ2v+Zsfg2DRg7zdfdgM9wYVBKRQCRglpeHvbB5LhyyH5RwRxAEQZARQqWMwmFXqwXncqr+PpxTVIk9PRYZVzCzhEza4OfT48HE+OB\\u002fPrzj592Xd5\\u002fItzwqub1b8Mvuy7\\u002fvvTo3M+bW5RmxYU43pmZognw58ybHnrxaYRgsqmx9+8ejL9+3kEyytAc7nV357bbzUNDHg7VuQQoUtBV9tBPuaC\\u002fqbO6ZmfHMohlw1KUlBPhYkzgYSYYlZXZkF\\u002f5w5ppBYx83GvWXu9aaq6btwpZMiH3\\u002f0DnTwdT78kptSrjvz8dYapVDp86Oc\\u002fFS+KGegxSAdiiNVtvYw4OjAPi5sagWj4PWGLTUBjIOPbd45qWqBnMFZM6U1Zwpq0kO8rtlSuqi5GjMEeUOAt1YtRKVRBqBam9Lnvu503gUkViqKKnHnptiAUq4IwiCIAgyFJX1nR\\u002f8eqK0th1uQMYrhQKjPCLTOYodOQMmnfLUbXM2LJr4239Xjl0qs5x2V6s1J66Un7hSPj0t4sEN06NCXKEyz0MbZ1zKr5XKjJZKO3aprLqh687Vk2dPijYdylTT1P33oeuHL2Akhh69eSbVzDBD65FI8L84ktBe1KmwqJTXV81bOiEWbrAfrRZ0CIR13dxWrqBTKO4VSYRyuViukClVUoVKrlLJlCq5UiVTqeRKlUypUqoxdqp25zaqM+RMkYkEEqHvi3mmvPadA6eN2wEAwINB+\\u002fWudbH+rrBjtBWLSpmXEHm40OjmJQDgcGHF\\u002f5bOtmbCEACAL5Wdr6iHowAsS4nT\\u002f\\u002fHty3k6vz+HBYdGEE8ixXxrdvmt\\u002fN3s8EMG5TwfJWKNEE\\u002fOy8vnvPrfCbjBQFFz+\\u002f92HX3v0NkVKXFr0hOHPwvEGkNL7tOGfV\\u002fKGlC23TmNxB\\u002fCXkQqcQ43v1ZU3yhp6VH0StQyuVpGwBGoBCqdQPOhePnTfINpgfHsmACaDfeNR5dGqynkl5YLq6qEtd3yXrFaLFPL2SSWB5mT7JYww2uKI96LUqPM4eYX8kobJE1d8h6pWkbEERhEhhuJFckMj2NFp7unUAnDuvZulraVCyrrJU2N4maeki9WSWQaOQVPphGoDCIjkOYfSg+KZ8fEsqLgVyL91Fr1DW5BLrewVtzQLe+RaxQkHMmd7BZMD0x2S5jmOYlBHKgAQMQT5JZyAoMYsc+rWdq6t+VwMb9cpBKzSaw4VvRS\\u002f\\u002flRTHiMRi638Fj76WpxvVKj5JDYsayoOT7TE9g2X1yN2PsCo\\u002fFdrhU35HILq4Q1bbIOgVKk0CjIeLIbie1P84llRWW4pwbTh1VkUGck+yFidwfOFX\\u002f0+0mldUsesegUBp3CoJKpVBKdQqJSSHQqmUIh7j9TBG+KjGVqrMnUeMePQxlbAn3cXn1g8f3rs3Yey91\\u002ftlgoxpjRb+hiXu3lgrp1C1If3jiDRhlufnl0Bfq4PX\\u002fX\\u002fDe\\u002fPwLFa5q7X\\u002f3mEJlEjA338fVgMekUpVLNF0kr6ju7uNjXPHeumrxwWhwcHVPQXtR54HG4pRNin18y02uwyrBDoNFqi5rbL1TW5za0Fje3i6xbVnEkudGcK+GuH95e1Nz+zPZDag1cB8CTSf\\u002f9nvVRPsOtvzF2rZ6YaJpw50lkFyrr5sRHQnFMRworMNOga+xaT8Y5O787fVgrfwxTj0gKhwAA1tWzHpTjvsvO+VEiVlqXkaRQq987eBbzRF1PIJX9fTX\\u002f76v5UT6eayYmrpoY79DlHMgmYyysQcBbdU9xmMbExcvYSLi3SNt2NO3N5RaqtfAhR63VKDRKgVLYLuss5PeNrHEncya5p830nhrJDDPa2jylRnl79iNwFMsHya+GMoLhqO00Ws2xjjNH2k52yXugJq6Cx1XwakT1e1sOQ00QPA7\\u002f9+Qf4CgAOb35n1Z+p3\\u002f6Y\\u002fqnbBILAKDWao61n\\u002f6v5ZBIJR7YGgC1Vi1XKHoV3Dpx48mOc0wiY7Hf3JUBS0h423pIs7TtXOelHG5+u6wTbgNAqpZJ1bJeBa9J0nK1JwcA4Ev1XuQ7Z5HfXDxuJL6TY8i5rss7mvZyFTzDoFwrb5d1tss6r\\u002ffmbW3Yudhv3tqgZRQ8BQBAJ9DEKonhxtZw3OeVyy38uOIb3eM1gUs3BK8GAFztufFdza9KTd9UKa6Cd6Xn+tWenJtD1q4IWKQLaoH2l9qtpzsv6J4CALrkPV3ynovd1xb7zb0jbJM+boHj3hcmh36XMWX35u5uPtgoaYbico28U97VKe8q4JXsbNoXw4rcGLx6CDcq9EamHyIOsvd04Qe\\u002fmh0owWZSMxJCkqL9o4K9g\\u002fw43hwm5hhStVqDUkUuhoz1QWPWmUF8PVmPbZ513\\u002fppp65W7j9bVFBhtJooRKPR\\u002fnssL7uw4aOnV4UGeMDNY8qS6Ql8kfSLv87CDQAolKqiylZrdgqbFqc\\u002fuGE6HB1T0F7USTAp5CUTYu+cnh7uZf8qE81c\\u002fs7son15pV1Co4sjZ2NNId2RxKBQAACNPbyH\\u002ftyLWQZBrdFQhpSpcRnTokJ82cwOAXw\\u002fcl9emZUJ9\\u002f35ZXAIgBhfL3uNbHXmzm+6JO9IwqybDwBg2+NryHZAwt2ZP0rEejdPTonz935x1zHMikaQ6s6ej4+e\\u002f\\u002fzExXnxUbdMTc1wzKrahCGlR6wqnT5s1398Eg45H9vSqaNiV\\u002fP+vS2H1VpL93kgXAXveMeZ4x1nHo26N8srE252Ar0K7tdVv5QLq+AGx6gV16dykkUq8WeV35UJBv9HRSrxruYD+bzip2Mecidz4GYsJYLyfS1HivgYpwUWdMi6\\u002fmzYeann+tMxD3qQ7X8OPRYpNMpvq3\\u002fN7s2FG4wpNMr9rUdvcAuejX3Ej+rDJDJNs70WjOTnVStu0P3\\u002f2+pfVVr4jFwLtP807valemd6TAQA\\u002fNXwr2G23dDR9tPuZM7KgMVwg4GRfF86I\\u002fxdFqpE31f\\u002fnsez6tK9Uljzdumn83xn3hG6ydabZyPTDxHHKa1t\\u002f2TLKTgKAAAgJTbwtuWTpqaEE6yb0Yy4GMz6HmKJHA4h\\u002fahk0rKZictmJja2cQ9fKDl4vqTbzIBuAEBDW+8Db23\\u002f+Y2bg\\u002f1sO5o4m02L04N83d\\u002f56ShPgD3QzwIOm\\u002fbyfYtmTLQqo+S00F50dOFxuDAv90nhQdOiQmfGhg1tVrtlPSLJZ8cv7s8rszyc0Ek4qITIkDGpZK5Eev8f\\u002f\\u002fWKsXcRPIns4b\\u002f2b3twk2Gp93EFj8OtTIv\\u002f+Ry8kOzZilqhTD7oHZTGHl5+YxscBWB1egIcsp3zd37SqO5dTYvv69CwzqBsZd8bUc7\\u002fUSI2SQsJ2Pf4bb9fuPHL+esShVFxP0wqteZYceWx4sqEAJ97Z05amBRt32S3fX\\u002faOGT\\u002fcxf7+rH2j7Odl+CodWgEarr7cJdpcgSRSvx26aemY2\\u002fxODyLyKTgKRK1BBqBPkw1ooY4Vsx7ZZ\\u002fXiRvhNvOqRXXvl3\\u002f5TtJLZPzgh5Z6cZOtWU69GlHdB+VfvZH4PJ0wmnPHnIFKq\\u002fqo\\u002fKsSATz90JwWadubJR+\\u002fnvgcm8SE2ywayc+rTtyoBdqfa\\u002f8yzbbr\\u002fVm\\u002fM909pUJYfaTtJNxmYE\\u002fzobk+M5hEs1OJR\\u002fJ9gRH\\u002fLrfLOt8v+7JT3gU3WHSq43yrtO352MetrxM1Yv0QcZxPfj9lWnsaj8c9ccvsjYsnQnFkXPFww5h5KhisZAoCAAjxd39ww\\u002fT712edv1G941huXhk8zUiHJ5S++OWBX9\\u002fcTCE7+2m2ZdPTInZ\\u002fdu9Xf5\\u002fbd6YQbjPD35u9bn7qqjnJLIb9R\\u002fCNMLQXdYT1GUl4rJnmRDyOTCBSyUQPBs2bxQh0d4vy8Rz+4oQWHC6seHPfKaEM3WscIhwAD\\u002f251\\u002fIwzJrOnme2H\\u002fruttUjsHqec1o9MdE04a5QqY8UVW6YlAzFIQewhrcT8PiVqfFw1EZjovOPTEkKcxQquKyCjl3ue1lZwd8aY+KjRGxFIRIfnDN5Q+aEX89f35FdaE3aHQBQ2tr59PZD0b6eTy7IsnIOjTVQvn2YHHgeM3xnuy6ZZtt1dZ\\u002f9qD5MIoOIJ0pUUpFK1CJtb5I085QCwy2neWVan2DC4wizvKcJVSKhUiRUiYQqkUQl1QK4Gp1dfFH5I5Shi2dHrwhYnMCO0ZVlAABwFbwcbsH+1iPd8l7DLQEAdCLdl+LtS\\u002fX2o\\u002fr4Ua2aUFYtqv29\\u002fh\\u002fDbDsBR0hgxwTRA91IbDzACZTCalFdlagGmknQJGnZ2rDz7vBbDIOY5vrM2NN8UKI2GuNAI1BD6EEh9EA2iUUn0OUaBVfBqxRWN5iUwmiStPzdsOu+iNug+HizpX67aZaTiCOmcBIDaf5sEkuikvQqeUW8sh5FX8fgKfmfVHxra6JzJD8vgVJ4vP1svbgRAJDIjktwi9VqtZd7slul7fptehS9Odz8\\u002fS1HdU+DaAEZHql0Aq1EUF7AK9FvJtfIz3VdXua\\u002fQB+BjOT7AiP7XeYp+e+WfWb6QwJp\\u002fnHsaHcSh0agilSiDnl3Mb9MoBQablMmqPq6+pfnYq2qmgVGsB8iDpJb1oS5vt+L9y5cMSsJjlqEWTwUGdN8PFhwCICapm44hJiBx+NmT4qePSm6tKb9p12XrhbWw1sAUN3Ytftk\\u002fualGXDDWHM6u\\u002fL4FaOkj58XW6VSC8RylUpNoRDZDCqLQQ324yRHByRHBSRG+7vGYCi0F3WQl1fMccRYdVt9c+rKd6evwlEsNDIpxMPNh830YjHYVAqTSqGTSQwyiU4h00gkOoVEJ5Me+Wt\\u002fr3jc1dMrbYWHm2C6UFn\\u002fydHzLyydBTeMD+Fe7qkh\\u002fqYD1ffnlQ2acN+fXw6HAJgZGz7Mes2o81vDXA7ILse3odXoMIU+StfmwaA9t2Tm\\u002fbMzt18r2H6t0LQ4Faaqjp5Htu6fHhP25qr5dlnjFxmm0T\\u002fjMUej1exo\\u002fM8wwiGx7wy\\u002fOdNjIg5g7+raZB2FvNJcXmExv0yj1cz3mQlvYR4Bh38w8k7DiEarEanEteKGD8u\\u002fMowPUz6vqERgdPhcEbDo5pC10JtyJ3MW+M6a5jnp44pvKoTV+rj1ZawN5fOK9Y9JeNLKgEXL\\u002fBfSCPDgow5Z15b67fnG1SpOd15cHbjMY7DCMjQCdb7vrP2tRwEA\\u002flTfTM+J6e4pUcxwzA+rRlT\\u002fW93fukojeue6Lq8OXOpN8TQMjitVwtrTHXA1lbk+M24OWWs6prtEULGl7p9maRsAoEXa1mKUYR7cCH9eW+q3AQDuj7h9jk9fOdcVAYveKfu0Slir32Zrw7+6bPIy\\u002fwW3hK7X\\u002fSYrAhb9Wb\\u002fjSPvAnO5CfqmFhPtIvq8R\\u002fi7\\u002fULMFyrancpI2hawNpQcZBgEAWqC90HV1W+Nuw3uQudyCo+2nF\\u002fvNNdgQ20j2Q8RBTl6B75cAAKamhNuaJwIA8IToQ3U1wf7ucAiAstoOOIQMJiHS74sX1p2\\u002fUf3OT8cEIhnUuvXg9U2L08fEgk7mfLv9wl8HsvVPCQT83aun3Llq8nioo4L2oi7s53PXLWepfNjMOXERUyKDEwN9g9zd4GYTo1v7wnkQ8DjTpVMBAH9cyo3y8VyXYfN3xzWsnphomnDPbWhp6uUHe5jtXXmNrU29PDgKwJqJw6ongzq\\u002flchmRrKbTnsaLeijHCfcaNQHZk++d+akk6XVO7OLrtY2ajH2srCLlfWrvvrz3XWLFiRGwW3IyHLehHuxoNwwW0TCk15NeDaA5mewCcyf6uvv57vIbw5fKSjil4YxQuAtbIHH4dkklg\\u002fFC24YngOtxw2fJrBjTTN0egwi\\u002fcmYB57Of1Wq7ruQO95xdr7vrECav\\u002fGG1mISGa\\u002fEP21u0VdfqvfzcY9+VfXT1Z4b+qBaqz7ZcW5D8CqDDbEt8Z\\u002fXreid6zMjcbBFGiOZYW8kPv9u2eeG+Ue1Vn21J0e\\u002feOZ4owXa3+r\\u002fgSZV3B62cYnfPMOIXiI79r3kVz+q+KqYb5Tztd4If15TPSfps+0AADKedFfYzS8VvauP6LLJ0awIfbZd56bgVSc7z+mXWq00+B0wjdj7Gsnv8omOc4Yj\\u002fQEAawOX3WTmW4kDuJneU+PZMe+UfmZYf+afxl3p7imW7yWMfD9EHAFzace184dSY42PUkUuJzYMYz5NVUOnSq2x4zTn8WNmetS3L7s98OZ2icxo4dlevqSstj0xyqqdvBPadviGYbYdj8O99sDiRVnDLWUwVqC9qKvKrmv+8gQ8f1ovJdj\\u002foTmTp8eEucZEjZE0Jz7y5eWzX\\u002fvv5OVqo4EsOm\\u002fuPxXq5e6gNf2c3JLkmA8OnTVdV\\u002fZAftnDc6dAQb39eWVwCAB3Om12bAQctRrq\\u002fNYjmymzLjdTasYmCjXcGWyFPsrxhoDHL0qKWZQU08zl\\u002f3ejZF9eWSvPqLaHKZFc8eS2A6+tnLcxcwLchowg5024NxpXe0h3T7GcbTfkRmJP9zJ7ABtFMrXcMLUHAFgVsNhchk6HQ3Kb4TX1eMcZ3VONVnOk\\u002fdS94bcab2UVHMA9H\\u002feYuWy7Dg7g7gu\\u002frYhfJlYNTCkq4pdak3DnkNwei7oXjppBwpMejbr3yfyX1dqB41apoNKaRKdLKuAV64qu6E33mmwuy6lDwhOfiXnkucLXTcuMWGOEP69FfnOgSDgjNIDmZ1hYBgCw3H8h9I2gEajRzMjS\\u002fgonMrWcrxS4kdiG2xgamfc1kt9liVq6rXG3YWSm91Rz2XY9b4rnY9H3vlb8gT57rtSoDredsDysfuT7IeIIje1cOARAZlIoHLJCRZ1Vc8aRMcSdTQ\\u002fy5TR38AyDUrnywo2aOZnRhkHEStEh3vetm\\u002fbl32eheHF12xhNuDd38L7bYTTVad2C1PGTbQdoL+qi1BrN2\\u002ftPa8yMD3xiwbT7Z01GGSpbeTLpLy+fszg5BgDw+c3Lbv5he20XfEKoUmue+PvAjodvtmakrYthUSnz4iMPFcKTZvabT7gr1eqjRZVwFIBlKXFDvi+OOr9N2GaWtBVI4dlsQyBRDCvhjj7K8SzI3e2x+dMemTf1cnXjzuzCM+U1mPOKdLRa8M6B02Fe7pMjLCUAXYNEpsiraqlr7xWI5XKTG5yWPXWTDZVRbOW8CXeegm\\u002f41PKozLGiSlRjmNcj4ogJboMMwgUATOAk6JN0AABooKv15vvOjGYOflecTqTP9J5muHZlvbhJqVGR8HbuLV4Uj0keqYaj6RskTQbt48uF7muGTwk4wsbgNYYRTFQCZV3gih9r\\u002f4AbHGA4nxcZT4rC6nsxrEjDhDsRR0zjYBQ0DKYH6BPuAIAueY+FhLuthva+RvK7fK7zkn5cPACATqTfGXazQbtZUczwqZ4Zl3sGFms603lpfdBKBtFs8Ufn74dDgFnSQaNxljmhdidTKJUmA3DcmLShrd+YXYwxVA0Z66amhP97PA8K7j1diBLuQ7ZkRoJpwp0rGKvlULcfuQHtRm5dPsnwqWtDe1FXdaq0pqazB44CAAB4beXcTZNT4Kh1zGW+xoM16YkvLJnJplF1T1lUyne3rdr4\\u002fTa+SV6SK5E+\\u002fOe+bQ9uYlDIUJPLWz0x0TTh3tjDK2hqSwnGuCl7obLe9A8IhldPBnV+m3gwsa+VuGI7zFjiSYb1Q9BHieBxuOnRodOjQ1t5gr8u5+3ILjSdQ6Oj1mif3XH46NN3ufCOVySVf7\\u002f38u7zRaZnblZyaMJ9iPdIRwAZTzJ82iEbKIwwdnGN7yL4Ub2JuMHP3aGiE93yHsPsm5VwALcyYAkcNSOJHWf4VKVVdcm7DSP2ksQ2Gi0lUlm1FoTrkWvkN3rzDSNTPNO9KB6GEXNmeE8xLcfvIEP+vAJo\\u002fpjrw\\u002fhSvA2fBtEDSMZffB03ktFwGJFKbPh0+Ibwvkbsu6wF2uMdZw0jC3xmWv+Jz\\u002fKeZvhUrpEX8UsNI4bGSj+0FZOOMUpFJDEq\\u002fuBKCHiM7xqVMnj\\u002fNCUUy64U1MFRZOybOzkGDgGQXVxfXtcBRxHrcFg0Mgn+lsmHN4ptFF0rMsoRsxhUX0+WYcS1ob2oq\\u002fovF3ugw+y4iCFnqQAAAqkcDo0PTy7MenftQn22XSfEk\\u002fPlLSswB2JXd\\u002fY8u+PwOMzrTY0K8XVjwlEADuRj1I0BAGAOb4\\u002f184oPwKgIZyXU+W3iwaBhHgja+EI4ZLthrk2KPkpEL4DDfmHprJPP3bN5SgoBa4QZAKBHJPn7Sj4cdRV8kezej3ZuP50\\u002f5Gy7o2HsR5yEL9XoiJLHK9StyzemCY0TeXTz40wN0Qk0KAL9HGvEsCKtTJwBAELocIk9wwozdhRgnH9UalRyjctmwSyoEtZBb3yiu7XHSwKOkOw29PEONhny52VuhgrHOJMeaKZsFNP4m6Kw7h+13hDe14h9l+vFje2yTsPIFM8Mw6eWxbFj8Ma3OsqFVYZPDY2VfmgrBo1seuHX3MGFIi6DRCTQqPCNK75wkFs7mLYezBFLB\\u002fkuIGNRWlxQqD98SqDVgje\\u002fP6IwM0YGsUwiU5j+6TDv9o0Jnb1GOQWlUu20VzKOgPaiLkmpVl+rxZ7C+OTCLDhkNalCKVfB3\\u002f1xwoOBffabGR702krsaoTnKuo+PWZUrmo8wONwq1IxTpKPFFaqTSZcKlTqs+W1UBAAsHpiIhyyGur8tiLg8UHuGNOpW7iC4a+b2twrgENWQx8lYsqDQX9lxdyt92\\u002f0YWPc2AMAbL2a76o3Oj\\u002fefqaqeYgjg\\u002f08WAsyYp7ZOAtusCs4B+E8kt3iDfNESo3q3dJPC62rwOC0oKUIVf3rQFqm0CihCAVv85SQZDejMbyWMYgMKCJW23lMsQ6USAUAqK37m7gY0xomE2zJXSawMcYqOsKQPy9zFWCgiSxQ\\u002fl0PGjxu94T7EN7XiH2XK4U1hk9ZRKblZRggZDzJn+prGCk3v+rsWOmHQ+DFgc8\\u002fSmvapXL443AZHm7wPlymUNbYeDpSVtux\\u002fehAqSXExdyyHOPWXV1Lz0e\\u002fnxqHww+H72phPRwCINjPHQ6NESyG0ZBVmUL5239XDSMuD+1FXU99Nw9z0n24l3uMrxcctVq3yCGXSGPd+oykO7ImwlEAAAC\\u002fX7hhboiuC1uNVQ2GK5Ferm6EgperG0Ry+EqHgMevSDWagG4T1PmHINwbHpcAAFCq1bXd8BIFtqrvGfqgH\\u002fRRIuakBPv\\u002ffs96aMqRTrdQXNnuCvVCIM1dvCPXyvVPqWTisqnxz26a\\u002febdi96+Z7FuJYMgb86Lt8578Za5j6zJWpwZy6T1DYVZmZV46MN7P3hg2eb52Icqe3HehLsH2T3Tw+jN85SC98u\\u002ffKf000J+KZTtGiugtCNXaVSVwpxehdFOGQdwLCKcPBpUCD0IDplHNkkCqjQOGdlEwBHg0LhULzZKdHJIbKbJPQ8L\\u002fIwzqo4z5M\\u002fLXNFwAt7oB5rLy0NjtNXa4Y4sgAzhfY3Yd7lSZDTIJZgeaHlpVlMc41+1x\\u002fzSpmOlHw7BhNgAKCKVK\\u002feeLoSCLiM52mjShs7RC2arCZlq7xY89+l\\u002fY7cgBjKo5TOTwgIxLiYPnit+6YsDw\\u002fnox1y+\\u002fmpBfUX9sGrpyBWqX3dfgaMApMXbcOrlVEL94VsFv++9+vA7Ow9fKK1v6ZXKlGPuU7YV2ou6nuZeHhwCAACQGgKfIdgkr2HMz8B2kOeWzJwZGw5HAQAAvLH3VG5DCxx1aWFe7qkhGHuVQ4UD2SKdEyUYI2Nmxoabm09gDdT5hyDRTAGf\\u002fIZWOGSj0pahn3KgjxKxINzL\\u002fSkzEx0KmlzwIz5+vVL\\u002fODHcb\\u002f97d7919+Kb56Utn5qwdEo8mUgEALDolPWzJqyfnXL30sx371t64IO7l06JBwDsv1Ty8bYz+pc7zlDKEY6Y20M3FPPLoHrNJYKKEkFFAM1vvs\\u002fM6d5oXgIAAAEAAElEQVRTBk1XOZVwRojhU66C1ynv8jEuY22qwnhEajA9EMo\\u002fWiPATLEOxBm0yYyOu7YmLv2M6y85IQreqmn1mAXcndOIfZfrREaFdH2oNg9eoBGN6tiIVRIt0GJm7V24H6bFBR+\\u002fDF\\u002fS\\u002fLjzUlpcUFy4bW9zTMhMCjt6ES4MuvN43vypsbFhg7\\u002ffqwX1r393mC+Swg2IC8HjcS\\u002fft+iBN7ebZk7P5lTd9tKf962bNn9KnG54yKC0WlBW2346u\\u002fLUtYovnl8XGoCRyndaVwrrdhzNjQn1+T97dx3ettU1APyY2bGTOMzMbVJm5m6Fbd3Wbh3zN2bmd9QxdtBxu3WldWVmSpuGmTlOzEzS94dTR1acxEmcNEnv79mzRzpXrh1Llq1zr869bkbKrPGxvuLe\\u002fbBUqPWvfrW789jn9MQQSS\\u002f\\u002fqaFj8fTkS4Xke56yiuqyishBIgoFOCymgMcS8tjRYb6JkQFjU8KiQ3r9tTUUoLPoyKMxkkcN2\\u002fkK+p7HBIDzVd19KK5lVArl45sX3\\u002frdX+WdJni02GyP\\u002ffnf5odXB4lcD7UZkZZnJGfXknNeRworzFYbk94+9MeGYUeKnO5ttevPdKmADv4+6SqFfaaidtX4NHLUbWqDsfMnwn1oVyLdu3504vu7j3cuENSmHZAC0VdXQVWzfYHDYnzyyPU+zvcmshh0k8VqNDvd0S7kst+6eyGGYfsulPx1JDsi0Pumme5W0O2bIZ1wFzNFLyQ8\\u002flHJVyoLudBVo6H5t5rNf9ZuHSMeNdNvyiiv5B7zVkNBCCfIjyWRmjpu6DjQfOy28JsIm5DZcOyI9CQxkiHuyym+qyHGA8eKWxsMzTW6ukZDk8qq0Vp0GqvWaDOaMYsZs5gxswW3dK6wcW3SO5fI92aKiKs9EjA8cEk\\u002foPuLQXXrVMNwY97R3hqgv2vQPstqq4a4ekx6+pj0NDHSWzjgOqve5dD1oXAcDpDZE2K\\u002f+PMYqYaM0Wx58K2\\u002fH7p56rLZqWxml509VhsmlWt4HKYXn1yCf8iaMTZGyGertU4Vh01m62PvbX3hnnmzxscS40SFlc2\\u002f\\u002f3fhWGYZMQfLZTP1Xfy+RzzIaLZo9WadwVRV7+JKTCrXVNS38ThMPofF5TCpbibCu5UaG3T\\u002fjVO+++cUuQGgtknx6le7f9p+dlpGzLjksOSYQB7H6dY3mw1ralPXNSvK69pyiutzShs1ur5UuB46SmukH\\u002f925OPfjkSH+k4aFTk2KSwmzLeb5DuOQ2lNy8GzJdsP57gs0n3XsonkUF8N\\u002frGxYHLirmP5l4vryQ3dwnHQG816o7lFpimrbbUnrFNiAm9ZNGbuxHjy1kPbsDiL2t9wrd6kNZha5S6mhKmql7EYdB6XxeMwOawuv+auEV19Mngs8n297jNZrSdKqshR5Aoei\\u002fnN2mW3fLtJriN3Psl1hod\\u002f+3fjg7dwu\\u002f4BNsIsTot\\u002ff\\u002fcxUj0Qrcl8urxmVkKUffVCVb3KQP4yFXM5M+PbN+gbdPD3QUZ4EItO75y4PFFSZTBbOH09bk+W1dgw8kAH96FdiXSPw2QEi4WVreQ72kfkpLgVje2\\u002fiueMifXtVAmQyaABQOcbDSkUeGHNnJN5VTqD+YstJ+eMifXuX39V9zyf3vKsaH7EB2mv\\u002fVK96ZzMRQ1EG267IM+6IM\\u002fyYXrPC5g5z2+6m1MXXkXzA2b+UfOPY3Vv8+FUr6RRomTCJk5+r9lMnDKRQWXM8ZtGaHcXm+qinNNAMNiMmfLL5+WX8lSFFvcKWyMGm9PPUBbNrfHgDm6OH3dpcPYX1b3qVR7sNhuEv2sQPss44Hor+RKl\\u002f8yYGYD8nQRX9TgcaF58zvI5aZv2kL9HjGbLp78f\\u002fW7zqZSYwGB\\u002fEZ\\u002fLsmGY2WLT6kxqnVGu0suUWrlKj+H4Gw8tXji1FzNhXF08DnPNkrHf\\u002fk1OpKq0hhc\\u002f3xkR7D0tIyY2TCIScKhUilpnVGoMxVUtWYV19S1K0kNuXTQmNED84c+HSHGkb7YdzqlqkOn0Jq2+PU3Wvmww2bqdhmv74dzth9uLINnHEfO5LB6n\\u002ff\\u002f2hBqfw1o4NTEuvBf3mty5fEJjm2rn0TxyAwAAVDfIqxsu\\u002fP7fBQBgMGhefA6PwzSZrHqTWas3Yf24YhzKKuraKura\\u002ftiVCQBCPjsy2Mfbi8vnsPhcFoVCMZotOr25QaqsaVJ008eweFrS+NRwcrRbQ+rYoNOoHzy57OUvd2XmO91l1Qf55U2vfLlr36nCF++d7yNy8dUzNA2ds6jOYP7l3\\u002fM6vUlrMGn1Jq3e7FjWG82dblBx8vo3exzLVCqFx2HxuUweh8W\\u002fssDjMnkc1j0rJ3bT6zxiiLiue801xr6nIbZk5stG4rBBDwoRe32x5vq7ftpisZHLk5a2tD27ee+Xa67zSDfh0MdnMecmxezKId9weaig3JFwP+SqnszS0Ql0Wr8ukdDB3wccJmNSTFjnCWyNFut\\u002f2UV9HuS+83IhOdQbaFciPTK7muWezRjqid8+UF35HZ4a6aKYB4tBBwCtqzExAi5r0fiELcdz9SbLrjOFaxeMJW\\u002fhOcPgffdiCB+PfWBhQPnmun8L1SXkZgAAkJnlf9Vu+7dhz8KAOdcFLeDQBim53Afz\\u002fGcebDnWYmwfGIvh2EclXy4NWrAoYA6pKnSNvn5z3b9ZihxicGngPF+WDzHipkEo1mHBrAdaju5o2EOqAoT0yGBzumjvXEO\\u002fezQKlUah2XAX59ZuDOb+Gszf0YP2dw3CZ9lgNQzmfBVX5TgcNHctm3jiUkVDp1QIABhMlsyC2syCWnLDcHbrojHHL5YXVrTfakdkz6KSo64snJr42JqZ9S1Ocw8g\\u002fbH7REFBOfmO8t5yjCMmNwBEh\\u002fq6n1S1e\\u002fGe+RwW4+99WeQGZxaLrU2hbbvGjgW11phT0kCO9iQ9IeS5u+eSoz0ZascGl8O8benYRqmyQaoit\\u002fXeqcuVD73z9\\u002frXbhELh\\u002frIGIchchbVGUz2Tq9+wjBcozO67CVas2TstZBwF3eRqKqVKckh92hN5h9PZpKjSCcZ4UFvLp\\u002f70tb95AaAo0UVnx04\\u002fdSCqeSGEWp5RlLnhPvR4gobhtOoFByHLurJdDmax03o4O+bxWnxnRPuAPDTyYsrxiQzaL2eA6ysRXaqrJoc7Q20K5HuGS3WFrWLO94kwmEz4sF9jp+7Ile3ofM5TADQGkw47uLWkLEJoVuO5wLA+aLaaz3hbhcviHk16elybdXepkPn5VkuczoGm3F7w+6j0lP3RK0ZKx5Nbh4amFTGozH3vl34sQlrPz5sOPZvw96dDfuCOAESlg+TyjTYjA2GJtL8igAw1nv0jSHXk4JDhNKi+rD4yypddxkrOoXOp3O5dC6LymLTmCwqy4JZCrroRLmWUXufoKZT6C4\\u002fFF0ZqftrMP+uQfgsm3GnEiiDbBCOw8Ek5LPXPb38vjc2afV9HwMyjDAZ9HVPr7j39Y2NrX1Mlt04b\\u002fTTd8yhUCA0QBzgI2yWkWu7ISMDhQJP3j4rIdL\\u002fo58Pu0zUIr0yZ0Lcqw8uHNbpS6Xa8Nf+S9sO5ZAKqvRTbZPiyQ+3bXhrDZXa6y+XqwKdRUeYYG8hhQKd7wm4VN1gT3eSG7qF4\\u002fDc5r0tKhd5DaSz5RlJla3yH0+4yOv9eCIz2s97WXoSuWEkmhgdFuAlaFY5lYtU6o05dU0Z4UEFDS2dM2XxgZKEQAkp2Fvo4O+b+cmx7\\u002fOOdS6IVCdXfX8885HZvS4c9\\u002f7uY533Qq+gXYl0b39+aefbiQAgzt+XHBr+6DSq1YYBAKlUl52AxwYADMdbVVo\\u002fEblEZIjEy75QdaUuzQAZNgl3uxh+5KOx991h0RxvPXNUeoo0xZ+d0qL6uOSbFcFLVoUuI7cNDdH8yOcSHv2sdL3G2nF2wwFvMDQ1GLoc3DTLb+pdEbd6sOaGB6ks6lfz32szkWtFCRmCdFFqkjA+mBMYyPHn0shdT2Xaytfy3ycFr0FsGtuRtAUA4rKbMHBxYu3KSN1fg\\u002f93DfRnmUEhp2zGeo9OESaSgr3V1YwOg3wcDr7IYJ+f317z8pe7Squl5LaRyNuLu+HtNW9+u\\u002fdsTu8qM3LZzGfvmrNoasfV79iUsF3H8wmbICPNoqlJY5PDvvnr5P7TRZ2nUXUfm8lgMYfZb8vU2KA9JwtdjvztlWA\\u002fr8fWzJwxNobcMKzsO1X08W9HXL4bHBZDwGPzOMzOA4VwHMwWq9liU2oNFkuXXwTFVS07juSunDuK3DBUobPoSCLmcuICJCVN7TcmOsh1hr15JUtHJZDi3cBw\\u002fIM9x10OfUW68uT8qZWtcpcjuF\\u002ffcSjcRzw6LJDcMOJQKZTr0xO\\u002fP0a+Z+VkaXVGeNCRYhdvTj+nS7VDB3\\u002ffMOm0WyeO\\u002fvrwWXIDwHdHz40ODZwSG05u6NpHe0+crehuTJg70K4cef69XMhm0GcnRvfhngkSuU7\\u002f6YHT5CgAh8kYkSdYAZdtNGsBQK52URPJUZm9plnROeHOvTLtgUJD7lHzrGF2UWQnZAiuC1pwXdCCInXZIenx87KLNpxc3XJ7w24RUzjffxYpPkQkCeM\\u002fSHttc\\u002f2\\u002fx6VneiwZEckLvyFk6Rjx0L0++bHyD1KWU8QQ3hy2YprvJJobWUWEQ2MTZwY2Yb0bgWvBrL0qUz5S99dV+bsG9LPMobGpFCpGOL+FcIIWBAzUaW2Qj8OrIjRA\\u002fNMbq7cfydm8\\u002f3LnSrtdodGoXA6582NYEAk4nzy7ct\\u002fpwj93XyyvJf8674zHYS6elnzX8oneXk69MuNQqugaIBHzX39o0b03TN56MPvQuRKp3GkIXvcYDFpqTNDCKYlzJ8Vz2b2rRnXVzZ0YPyU96sj50kPnSrKK6jpPr9Q9KpUyJils4ZTE+ZMSGIz+XixdXev\\u002fOf3zjnOkYFy434o5o8Ymh4b4izun2jtTaQ1lNa2XCut2HsuTKcm13TZsP7t8TtowKtmMzqIjyaSosM6JKgD4aO+JcZEh\\u002fkLyBblLaoPx2c17T5ZWkxuQblEo8NGqRau\\u002f\\u002f7vzLjBbbY\\u002f+uXPzQ6sDRQJS08izIiO5c8L9bEXN4\\u002fMmd0590mnUpaP6O87GDh38fXPnlIxN57I7D3K3Yfijf+5cd\\u002fPi2YnRpKbOLDbbp\\u002ftP\\u002f3L6ErmhT9CuHGHy6ps3nssRcdnXjU5clp6UGOjXt59I1W2Kxzf+J+10lwwALEqN6382fwgK8hW2KrUAUFrn4hMRGehtX8gqrR+XEOrcCNorN\\u002fVaMXIm2bOGZcLdIVEYmyiMXRN2w87G\\u002fYdbTlhxp2ukP2r+GSse7c0UE4NDB5PKNNvMjgwdm8ZiUVlaqw4HnEvj8OjcQLZ\\u002frCA6zSsphh\\u002fp\\u002fNChpUhddlGRTYz4sXxfTXrGl9V+iHenh\\u002fzktYI05lpp7t1Nx3qbiz69rozU\\u002fXUV\\u002f66B+yxTKVQRQyg3Kx0RtaUXKbDeGszj8CpiMGirFmTcND8ju6Q+p6ShoLypvkWp0Rk1epPFYmMwaBw2w8eLJxHzQ\\u002fxFEcE+sWGShEj\\u002fPgzanT0+7tyfT5Ojg45CgUVTkxZNTcorbbxUWJddWt\\u002fQorL\\u002fvVQKhc2ki724gb5eMWG+o+KDJ6RGuPxLF0xOXDDZM5d8JO893nNtpUEwaC\\u002fjpzdXk0NDTLCf12NrZjy6ekZpTUtuSWNxVUtdi1Iq02j0RpPZigOwGHQ2i85lMwN9hUF+opAAUUp0YHJMAHM4T8fEYTGWTE9eMj3ZYrGV1EhLq6XVjfJGqbJVoVNpDDqDyWi22mwYhUrhspk8NpPHZQZJvGLDJbFhfqPig0mp1b656sfG9sO5pGw7lUJ57LYZNy8Y06trPy8+Z2xy2NjksDVLxj754ba8skZia5tSV1zZkhQdQAwShfiLPHvmfPCmqQ\\u002fe1K8K0Vf3LOrnLfDsG3Itu2Fs8m9nsjrfxNOq0d354z+f3LIkMai7eQ6MFuvfF3I3nLzYqiH3JCHu4DAZ396+bNW3m9o6vYEyrf7hP\\u002f7deP\\u002fNnOFcj8sd4T6i9LCgy7VOJ8aCBmmdXFncKYs6PS7Sm0e+Mbdv0MHfNzwW85mF013OQGC0WB\\u002f9c+fy9OSHZ08MFgvJzQAAYLVhR4srP95\\u002fss811jtDu3JEUuqNv5+5\\u002fPuZy74C3pSY8KlxEeMjQyQCtwqvV0hlG8\\u002flbM8qcFlZhUal3j1tLDk6IiSE+eWUNwKA\\u002ff8kMcG+9oX9mSX3XzeJ9FO2qKa9VgqfzXJq8DQXvwiHHW+m+M6IW+b7z\\u002fyi\\u002fIcaXZ0jbsGse5sOrwm\\u002fkbDtUKG0qN4o+NAx3WKaKPnBqDvETJHTRsPEqTbyYKgHo+9yK8sJoLOhEz0AgD\\u002fbr1JX41h1WSupG51LhHdjpO6vq\\u002fV3DfRnOZgTSEy4NxqaO9o8bTCPw6uOQoH0hJD0hBByw8iVGheUGhcEMIHcgCCdUCgQH+EfH+FPbhjpGAxaSkxgSswIvPG2e60K7ed\\u002fHCMFn7h95qoFGaSg+\\u002fhc1qsPLLz52Q2ktMCF\\u002fJpuEu5DGTqLDnfRfj4LUuL25pWQGwBqZMpV326clRC9KC0uLSQwwItPo1JxHDRGo0xnyK5tPF9Zd6KkSqknV1tKCvJ7YOaExzf+R4ojLgV4Cb5ac\\u002f0dP\\u002f5jspITQyVNrc\\u002f\\u002fs+\\u002fz1df1qodvOFqekURKuNsw7OdTWcSIXf+nS3VAB3+fLc9IOlhYftRVNSQch+1ZBTuzC8eEB4+LCo3wFQvZLIvNJtcZ5Dp9bl3z+co6nYlcovOWCaOalOrjJVWkuJuG8q602rB6hUprMmuNJo3RrDWaNCaTxmjWGc0ak0ljNGmNZo3RpDWatCazykB+GQBwtLhy1oc\\u002f8FksAZvJZzH5bBafxRSwWTwWU8BmtUfYTAGLxWMzg0QCFt3dfGaLSqvUGzQms\\u002f3ZNUby63G8YLmWfEOD3bT31ne8ho5XxeSzWHw20\\u002fGqBGyWL58n4rLJj3dPm0b37+XCfy8XAoCIy471943z9w0SCfns9nfAhuNGi0VrNDco1bUy5aWahu6r8N8zbWyUxK2UyLCTERv895FsAGiUqcsb2hwZdruxCSE0KtWGYTXNir+OXL51TrqjyWy1bT6SbV8OvlLMfYC4e4AOfUGcgNcSn34x712pqaNzOFuZNwQT7hiOvV\\u002f0uSNDN0qU\\u002fGz8\\u002f9Eow\\u002fUuj0Ln2SZDucGJwlhipBuqgRyuO4xE8ELPyjIdqwqz0mAzcmjunqbr9C769LoyUvfXVfm7BuGzHMWPyFMVOVYrdNVW3EqnDMipezCPQwRBkCHof0Xfn5flfjf29UB2xyR1TcbWBy++OcEn7aXE+wnbesyptqzzstyn4+8kNwyizfsvG80WYiQu3O+m+X3PttuFBYpjw\\u002f1I02ZIZX38zkWQ\\u002fnt83uRTZdUao4ncAGDD8EOF5YcKy+2rHCbDbLXaMPIwUqIQsdd3a5fz2SwGjeZynjqks7TQgHdumP\\u002fs33vIDQCHCss\\u002fP3T6iXlTyA0jy6K0+Pd2HyONRf3rfA5xFQC8eZwZ8b27NbZ76ODvs\\u002fdvXLB6\\u002fd8VUteTK9ow\\u002fEJV\\u002fYWqenKDKykh\\u002fi8snvHDicw+J9xhCO\\u002fK4ubWVd9sJEd7w2Kztai0LdBdBtnh+ztWTI2LIEe7cN8v28q72INukmn1Mq1b93PfMDbl7RXzyNHeU+qNmVX1me4dWi5Njgl\\u002fdO4kcnSkmJwayaTTzFYbAOw5V\\u002fTYDdOIrUIue0x8yIWiWgD4+O9j1c2KxRMTxAJuXYvip90XKq7MlZoeG0x8lMcNVF3jq4JL564IXkyMNBlbeqyqPPhOtp2r0Xd8bO6MuNWzGbpBpjCriKtx\\u002fGjiavfKNORaddemCK5TVSkc8HxCjrVH1fqOGzt6NFL311X5uwbhszzKy2lsiwWzFKhcjGjwiME8DhEEQRC787JcufNX2OA7cqGUFLluZopHxplGBJHHVSk0bl2vIshACPMRfXzLEhq154PbYLZ0n6WKknj\\u002fft8qXwGPzaCnhQ7LmzauliVp8Q\\u002fNcn2nyPfHLuzKKSZHRxY+izk3KYYc7WTJqAQ6zZO5GnTw95mAzfr+jhVhPiJyQy\\u002fF+vt8f8dKJp2WFNjRqd8HaFci7pgSG\\u002f7VbdfTqJ48jQwpXBZj+qgo+\\u002fJ\\u002fpwttnaqx37GgvZYOjsOWYzl3v\\u002f\\u002f3ipd\\u002ffuyLHTkVHcMEl09NcSwPhJH27kfywoirNhwbgpP4ZSvzHcs8OjeA3V2ZrSHOiltJMysKGG7N1AEAGI7lqDreimtZjCCKQXUas5ylyCWudu+SPJsc6sJI3V9X6+8ahM9yvCBGxHC60Wlf8xHiqgcN2nGIIAiC2GE4lqscqG5UNynU+oZOk0iP9lDFLTaLXJHZaiVfESHIYJoaG\\u002f7uygXuFyJwaUJU6O\\u002f3r\\u002fL3av+1OTGKPCEb0r3\\u002fmzN5QUocOQoAAK9uO5hbN4AVFIeC5RlJ5FAnHqwn44AO\\u002fj4LFAn+uG9Vf1LSE6JCf7n3JnulkYTA\\u002fl4zol2JdINJpz00a8J3a1ewh\\u002fMES+64ZU46lUKZOyb288eWd+5amJgcPiu9u97Nm2aOig72IUc9ivyahju9zaniEpvGYlLJP\\u002fSvOgWhIrPeauhtoeQhhU6hk95hg81FNS6XTrWdbzPJydFrEpfGSRelESOnZReIx0k3ijVlUlMbOdqFkbq\\u002frtbfNQifZSqFOttvKjGSo8zPUxUSI54yaMchgiDIcHe8NfP5nI9vPvvUzWefejH300x5nqPJYDPefPbp\\u002f8t6h7B5u0ey3rn57FNGmxkA9jWfeibno5vPPq20aPJVZctO\\u002fZ\\u002fjPxs+qCnpVrmWHAKQiNztt+5em4L8j3M5TFIEQQbZ9emJmx68pW\\u002fjVdkM+hPzp\\u002fx09w1iLscRHI8SVb1EocB7Ny5IDnYxU4jJav2\\u002fP3d2X5V4uJsYHRbgJSBHCeIDJQn9GwTdFXTw95mvgPfHfTc\\u002fMHMCk967G5pZdPr\\u002fzZn0410rHe9boEjQ5xrfDmhXjgyrJ45elBrfOVncZzMTov57fO2jcye7cw\\u002fEcJceG\\u002fzve3d\\u002f8ODSpC7mnXrrnoVj4l2PIJk3Nu7pm2eQo57msf06RFxWdlzwAICE5UtcHSKIEznigL9VsG5v8+F6Q6MJMw3BAjg9EjFExNVKbTVxtSsys3xT7VZy9Bo21dfpzkoLZtlc9y8x4hIO+Maa3r2NI3V\\u002fXZW\\u002fa3A+y4sD53HpXMcqDvg35Rv63EnQvUE7DhEEQYavX6p3fFLyq8aqn+c\\u002fea7\\u002fZKVF\\u002fU7h+h0Nh+2tHBp7hmRsnb65WO1Ur6xCW1uvb57sk86mMQEghOO\\u002fwH\\u002fKPVE3AEAQx+\\u002f\\u002fYlY7\\u002fqN6pJiL27QGF3Vg6XQPXCPgOFQ1tFfJdAga4PmpEMQdCYGSnY+tfWvFvEhfMbmtC948zh1TMvY\\u002ffff9M8aTPqSjwwJH\\u002fEBCj2Mz6F\\u002ffdr2\\u002f0EXfXptG9\\u002fAf\\u002f5KqnI8kVAplWXp3g9xXuDEEvs\\u002fQwd9ndBr18XmTdz9x56rxaVxmz8M6OUzGLRNG\\u002fffE2odnTyRlVPs\\u002fyB3QrhwRoiTeH9+y+PgL9728dNaU2PA+74IQsdcDMyfseuKOb25fFuotIjePXEE+QnKIgMtifPf0jS\\u002ffPjc1KpDLZlIoIOSxp6ZFfvzI9e8\\u002fsITRy86zPujj7hxoJsz8TfmGWX5T0rySqRR3f\\u002fGXa6tIxRbGikcTV4eIyT7jT7ddcKwqLarfqv8mtDuhU+hcOkdI54fxQuP4UZN8xgkZ3fWHD74EYYy0tWOi2jJtZYW2Opof0bFFJ20m+YclXyotanLDNSxDPCqYE9hgaHJEjrWejuSHzfefRdiK7Nfqv8q0vas\\u002fPlL311X5uwbns8yjc1eHrfyx8g9HRGlRv5r\\u002fv6fiHo4VtJct61GNru5465lUUVK6KJXcRjBox2Fn60uPf1XsolrOn9PuSxO77pfuxi8Vp78rOe7L4n8+\\u002ftZowYAMEbI711p539lfASCC7\\u002fvf7EfJzSPCjce+LVE3A8BXE9bM8Hd9AzgyENA7PzQVqyu31x9K8Yp9Pflh+81VRtv1bxR89Wv1v+mixHBeEAAsCpy2v\\u002fn0gZYzCcKOs\\u002fRR6QUAmO3f3q+Z4hWb4hVrxizflv\\u002flzfSaFzDZseUgY7q6umtT6PhcFjnaS5cKa5tayV+yMWEDeE62M9jMZ1pLzrWVFqnrFWadyqxnUGlCBieM55vkFTpVkpAqCic\\u002fBvGor267nhwaeph02o1jU24Yk1LQ2JJZVX+puqFerlIZjEq90YphbAady2T4C\\u002fnBYq\\u002fEQElGRHB6WGBXgxAZNFrWG734DVAi\\u002f7RC9ZN9OV78RLToXkcTl8kofPdJx+pQIOZyPPKSWg2nMpsftC8H8OZl+H3qJ+Qfff4+562uFY\\u002fPm\\u002fz4vKt32r96Bz\\u002fJdaMTrxudSI56QkKgxCPHbWfBYuEby+Y8t2j6mfKa8xV1pS1ttTKl2mgyWawsBl3AZoV5e8X6+06KCZscE95VXn7D3TeQQ30ydHYlAKQE+w\\u002fQe95\\u002fOx9fSw4NJd487ppJo9dMGm3DsLIWWVGTtLpNUS9XSzVauVavMZl1JrPFZrNhGJ1K47EYfBaLx2ZKBLyEAEl8oCQxUBIl6RgI2Dd9\\u002fsj88\\u002fBqcsgNd00bc9e0MeToAKBSKCunp66cngoAGI4P8qAWF7+whwIcxy\\u002fIsy7Is8RMUYYobZQoOcUrkUPr8r4brVV3sOXY9oY9FsziCNIp9OmSPs7JiwNutJkMNmOLqSOFZ9diahUw+Bwah01jUaAveytDnDbLb+pR6SlygytW3Kq2aNQWTb2h6Uzbhd9r\\u002fpkmmXhb2E08wojXq2uMeNSJ1rPEyGdl659PeCyEE0gM2llx68nWcxtrt2qtOnJbP2A4ZrQZ9TaD0qIixnHAGg3NXDqHQ2OzqP29bhxQNAr1rshb3yn8hBj8uWpTrb7h1tCVnXd3vaHpj5rNOcoCUrxHQ2F\\u002fDYSr8ncN2md5jt\\u002f0XGXhBXmWI6K0qF8v+GCs9+gFAbPi+NGMTrWzcMBlJnmlrqZAVZKvLmo0NANAFL+HFMOgHYcD7buS4zqrSWc1ba259FzKQnIzgiBI1x68+CY5RHCo5SwA3BK2yFHKjE1j3hy66I2Crw+0nL4v6iYAiOSFxArCT7dl3Rd1o\\u002f3nqw3HTrZekrDEKV6xhH9sSPD3cdH7ez6vOiK4XxdvFovt679OkIJUKmVcchgp6EEYju2ov\\u002fBTxRGZSUOMW202g83cYlRlyip+rTwWKwj8ccJDLBr5q3MoWHb8\\u002fRaj6ujcNzk0VHtnMFAokBLsnxLsf9fUwbjyR5ChAx38\\u002fcFlMuYmxbgz\\u002f+0gQLtyxKBRqQkDVlEKGeRsOwzZhLuDwqw8LD1xWHqCAhQJyzeMG+zH9uXSuFwaGwPcaDPJzPI6fUOlrgbrVO9yRcgSd+YwbDZK\\u002f6nfqbcaDDaD3mb\\u002fv9FgNXRVEeLT0u\\u002fsCxSgsGksDo3DpbE5dI59YYrvBHeG1d8XdbsXQ7ijYQ+5oSc23HZMejpHWfBK4lNBnL7P2uFB47zTQ7nBdfoGR6TNJHsp7+0J3mNTvRL92RIGlaGz6hVmZbGm7LIij5gTvy5oQbm2skhd5oj0KEuRc7LtnH0fXdlfBqPNxd3QAGDGLE\\u002fnvGZfplKoHBqbQ+NwaZz2BTpnZfCSYFcJ2asiWZgwzXfiybZzxODhlhPHpWdGiZKDOYFChsBoM6ks6mJNGfENT\\u002fFK4NF45+WXCI\\u002fr0iDvr0Fztf6uQfssPxJzj6pIXaIpd0RwwDPllzPllxlUejAnSEDnc2kcM2Y2YiatVddilJoJHZDuG5zjsLPboybND0pWmPVKs15u0q0r2K+zuv5c9wq9i0EcCIIgXZnim84lDPLQ24yn2y47Vit19QAQzXfKGkfzQwGgTFPriCwKmPZF2R8nWi8uCJgKANnKIqVFsyp0Yd\\u002fGagwoiZjvK+aTiq3\\u002fte\\u002fSdTNTuOw+5nwtFtvb3+8rqiRPbTItI1rI73IATT8ZbOaXsjeebStxRChAETN5YhbfZLO0GJUWzGaPh\\u002fMkQzPbXqNrbTE6jR1BEARBEARBhqOhnnB3wAGXmlqlncabd2Wa78RlQYvIUVdUFvUZQlEI9+GAG2xGg81ILKUczAnqPuFuw20n284daD5apeu4KusthVn5btGn76W+4mZJigFFAcr9UWvfKlxHvL3AgllPtZ075ZyzI5nlN\\u002fXWsJU7G\\u002ff3KtFZo68\\u002fJ+tLRg\\u002fDMZ1Vr7PqicGZkilDJ+EOAPdG3d5skpZpnKpzWHHrJUXOJUUOMegQxAl4NOa+07ILbiY6B3l\\u002fDZrB\\u002f7sG+bPMpDJeTHzii7LvsxS5pCYLZq3ux2vobBCOw864dGYk3zfyyupXxUf6k3B\\u002fKH7mtyXHgrmiWyLHk9uGtnkHP2k2qM4vfplL72OeC0GQfro94vpAdsfYoiZjKzHhrrcaaRQaMSMPAAIGjwIU4m+MaZIxP1VtPdB8xp5wPybNBIBZfk7zZAwdM8bGbD2YTYw0tapf+uK\\u002f\\u002fz12XR9y7qXV0ne+319aIyXFKRRYe91AnZMtmPX\\u002fMn8sUNXZV31ZwrujZ830T\\u002fFmtleItuK2IlX94ea83Y1Zd0fP7njkUHJB1tGtjiAIgiAIggxfQzTh3p+R\\u002fnQKfVnwwhtCrhuCY4jqDY1flf1Yo68nBr2ZoiBOAJfGpVGcavbjgFkwq9FmVFhULUapzXkIv9ys+Kd+5z2Ra4jBqyWGH\\u002flY7H1flf1kwtxKkFGAsjx48arQZQCQ5pX0F2wjb3GtYlIZLyU8+UXZ96Tpf7sSzY94Ou5hIUMQxeuhVAjRSN1fg\\u002fl3XZXPMovKfCb+kT1Nh\\u002f6p2+nm30hEAQq768JcRINzHA6oO6In3xF91Spj9lm1tq3ZgMY2IsiQxqVzbEab3mrg0jmOoMaiwwEn1t1iUhmz\\u002fCbsajxWpWsIZPuel+UmCKOCOEP0HuFVC9J3HMm12Zy+ns7lVN\\u002f+0u\\u002f33zB59oQ4d+aVUmoMlwpqdxzJzSxw3Qe8fHZacsxAjXL4omSvI9s+3S\\u002fpzbSbSSVZ6BRaqig8VRT+SNwiBrXnP+equCDrXd8\\u002fgiAIgiAIMjQN0YQ7i8p6K+WFA81HM+WXTZiZ3NwFFpU51nv0jSHXu1NJZvDV6OvfLlxHHP00UzLluqAF7lSTMGHmHGX+nzVbiWP8j0pPrQ67oZvS9oNprHj0\\u002f1Jf3lD1Z4G6405el8J5oWvDVyUJ4+2rEbxQIUOgtjiV2ryWsWmsZxP+74j05D91O1Vdz+fJprGWBi64Pmghg0oHgAheGJVC7VxYqSsjdX8Nzt91FT\\u002fLFKAsCZw32WfcrqYDx6Sn9TYDeQtXAth+k3zGzvSb4sdyN9czOMchQnK2tYIcQhBkiIkThFdoa8u0taNE7d8gAFCurYUrhWUcFgVM3dV47EjLuVhBuAkzz3E1vJ1GoQLAVT9thgd637pozB+7Mknxhhbl69\\u002fs+WDDofEp4dFhvqH+YgGPxWEzcBxMZqvRbNHqTY2tqkapqqpeVl7XirsuxwgAkBIT+OTts8hRDylU1f1Te8a+PMEn9r3Ra+xvrEtDNttusJmz5E73liEIMgJUa8\\u002fuqn+ZHO3C3TFbuHTX82dIjSWl6sMthiKlucGM6QBwBpXDo\\u002ft6MYP92YmhvAw\\u002fdse3EoIgCHLVDdGEOwDE8qNiY6KsuLVCW12sKW82tEhNbTKz3GAzmjGzGbNQob0ktz\\u002fbN4wbEs2PSBelsWm9nhgzXhCzaeL35KinWTDrF2XfEzN090beNsd\\u002fOmGT7rCozPHeGVG8iGdyXnP0QNhwW4GqeKz3aKdNAQBgrPfo\\u002fvxRNAq1Dw8P4gS8kvR0la42U55VoqloNkp1Vp0FtzKpDCFd4M+WRPMjM0RpsYIo4qMoQFk\\u002f5mNipHsrgpesCF5Cjo4sFKDM8Zs+3XfyRcXly4q8Kl2tzCw32kxMKkPMFIVxQ9JESZN8xnFpHWPrWFTmnxPaZxdw04DurwxxmjuH0ATvMe5sNkMyeYbE3dHKA\\u002fp3waB\\u002fll0SM0W3h6+6JXRlnqqgUF1apauVmto0Fo0ZszCodDaNzaVx\\u002fNmSQLZ\\u002fODc0xSvBl+VD\\u002fifcMDjHIUKEEu4IMvTN95+yr+nU33V7E4SRLCoTAEyYeXPdPgpQ5vpPIm4Zwg1I9oo5K8tWWjRMKmOqbwax1Y5GoYmZwkaD1IJZ7T2XV8uDq6aW1kgv5NWQGwD0RvOxi2XHLvZ98HV6Ysi6p1cwGQP1B\\u002f5ZfdK+wKezX0m5sZtse\\u002ffMmHVPY9ZJaVG5pklu1rGodH+211ifmOtDxkXz\\u002fclbAwBApqz80Ys\\u002fAcB7o9fM8k+x4djexssHm3KqdFKFWcumMgI54jE+0TeFTQ7iiMkPBihWN1yQlZWqm8o0TXX6NuxKl8WsQ687bwgAcHvkjEfiFpKjV\\u002fThxdv9XHFkfflBANg98yUflgAAKjTN2+svXJJXSI0qDMd9WIIYQcAk3\\u002fjrgsdQ+\\u002freIgjSZzqr7GjzJ9Xas6S4zaYx2jQyU1Wl5tTZVvBlRd8S+T0Mvbv8PeWXilu0FukDcbsZ1I6rDwRBRqovt54ihwbAozdMJYc8Z6B++HoKnUKPF8TEC4bE7M\\u002f9cartXKOh2bGaLkp1P0Pn4MvyThennZNddEQajR3\\u002f5hARyQuL5IWRo0jvMaj0ST7jJvmMIzd41EjdXwP3dw2dzzKDSs8Qj8oQjyI3eNTgHIcIABhs5sy2anJ0COhPkTekP9A7PzRF8UNuj7jut+qdT17+YIw4CQf8kqKw0SBdHbaENMIdABYGTP245BetPH+CTxqxBA3RNN8xOxuPvpz3eYY40YZjKov24ZhbyBsNPDqN+tFTy9\\u002f6bu\\u002fh86Xktn5g0Glrlo69b+VkGm2gErVKs+5oS4F9eUXoeAlb6NzurovyinfytzQblI6IBbNqtcYKbcs\\u002ftWduDpvyaPyibtLN1Vppm0j93OU\\u002fCq9UtgEAC2bTaJpKNU1ba8+9nHLDgsDRHQ8AAIB\\u002fas7sbswiBfugny\\u002fers2k8WEJfqw4vKHisCP1DwD1elm9XlaiblwWgn4MIEjfUSl0X5bTiJ\\u002fOqBRyfsZgU26teUxtaboSoAgYEhZVYMOtBpvCaOu4D9WPkzCCs+0Kc53WQp4aBEGQEeyXfeQ7LwfCNZ1wHzFIEwnO9ptGXHVfsHPNCjcrYCAI4inoszw07W3Ie+7SFnIU4L2MlUtD3O2TONtasbMuJ09R32rS6K1dVjPbMPmucb4RpCCNQgEADMf+q8\\u002fZ25BfrpHKTToWjRHCFY33jVodOT6Y62JsY6Gy8WxrRbG6uUTVXKOTORIcE\\u002fa867whAMDdMVOfTJpHjnrI2daK7bWXs+W1MpOOR2dFCnwXBqXcEJ7BpNKZPY26rdcrjjQVXZRVl6qlcpPOjFl5dFYgxytZFLQ4JHWCr4try01V5\\u002f+XtydG4Ld91iNSo+aD\\u002fD1npBVcOmteUNJjCXPsE8Zuqjr\\u002fe+U5qUEdxve5I3rSstB08r8CAH16dpL+7HcAMGHW\\u002f+qyjzaXlKpb5CYdk0YPYAsnSqJuCB8TI+i5wF1\\u002f3nnkarkhZH4wJ2BHw6H9zacpFIjkhdweft1kXxeH6GTf9B8rt6osmtmu6snY3R5xPYPKON2W9U\\u002fdfjaNFc4LIm8xWFhM+ruPXTf7fOlXm443tXYkcfqGRqPOmxR\\u002f9\\u002fJJYYEuzn4elKOscdTkub6vGeET0sKXczZaMBsAcOms0eIICUtotFkKVHX1ehmG45tqTrWZNW+nddkXUqRuOHm5qFBVTwFKvDAoku\\u002fHoNJqdG25ihoccDNmfTtvSxjPN1EYQnzU\\u002fKDRscL2PW6yWb4t229fdlloPsnL6bEO\\u002fX\\u002fxdm0m9cnywh8rDgMAh8aM5PuxacxWo7rJoLDitimSBPIDEI9AnavXDA5NtCqi13eCnpJ+a8+2M6icSZL74oVzWDSBo9VgUzboc2q1F8o1J5K9Fnc8bMSp0zldgiEIggx96FpukFTpaomrkfw+j711+k02BCeGRZCRDX2WRySDzfz8pa1Hm4vJDZ1QgEKjuhgkyKOzpEbNE5mb8hQNjqAFsxWrmotVzX9XXXhz9LIlIWmERwAAbKw6\\u002f29dNik4yKyY7bXsf\\u002f+rz3FEzGarQqbLktVsrs78fPytQobrYbkA0KhXvpGzs3MlHLXFoLYYStTN22qzZgbErxtzE4vGIG0DADU6md5qfuDsb+UaKQBoraY\\u002fK8816pVfjL91fenxr4qP2DcrU7e8cnkHAGVZ6GjCo\\u002fv77OCJ\\u002fX6+req1yzsaCaNKzZi13GIs10g3Vp2\\u002fLWri00nzuxpV2p93HhkgLyXeTw4BBLIl\\u002f079ihSc6JM20Yf8ie6MRqFSKVQx02u0qMtkJZPKWBtx\\u002fdqI68kNV8mcCXHTM6J3nyw4eLb4cnE9hnVdl90VBp2WEhs4Y2zMvIkJPiIeuXkA5Clr7As+LEEo19e50S1NBsWbeZvtCeuVoRMeiVvEo3fUqNzXePl\\u002fBdvMmPVgU06GOHJFqOu+kxPSQgCI4Pm9NeqWOEHH3LCFqvqns35VmLVW3PZT+ZF1GWs7HgMwwSd2gk+sfVljNToS7jeGTSRN+toVj7x4u10Nl461FEjYwsfiFs8KSKFfmQFeZzWdlBZGdluXBukzCpB7VhDEwYzpy9XH7MsLg18P5413agbg0EQxghkxghkzAp6gUVz\\u002f2hkZ6nQddwYjCIL0H4\\u002fDjAjwJkc9CiXcB4nWqiOucmlc4qr7Wk1txFUho6N\\u002fG0GQQTB0PssyqfqvH4\\u002ffet9Mb0mvHzvyzA5M3DXnMaVZrzDplWb9D2UnanVy8kZde+FK1pVHZ90WNTHeK4BFpVdr2zZWXWjQKwCASqG8m74iwSswlOfNcjXumEqhPH5hU76ygQKURFFgNF\\u002fCoNKqtW2X5XU44CbM+kr2jgi+b7LIaezq4pC0eK\\u002f2ex1MNuvnRYfsy08mzes8tjFFFEyKeMTL2Tv21Ofal\\u002f3YgjE+EVw6s9mguiSrKddI7zv7ayDHy\\u002fkRHbxZvCJV+z3ObBojwSsglOvNojHaTJrMtmqd1QQAx5pL3snb\\u002ffbo5cQH2lkw24cF++p08gVBKWbMcrS5BACONhfvacj7tuSYH1s4SRJdpGosVbcAwDfFR0kJ934+O\\u002fR7vx9tLn76Ynuei0dnZfiE+bOFBpslT1Ffq5NjOP5bxdlWo\\u002fbDMTeSHmjXn3ceGS6ylcUKs+qGkC77XYYmBoO2fHba8tlpcpX+cnF9RV1rZZ2sXqrU6k16o9lgtNhsGI1OZdJpfC7Li8\\u002fxFfMCJV4Rgd6xEZKESH82c1CTPk1XerxiCWnuXvm2bL\\u002f9jDEnIPW5pOWk1oVB6UqL\\u002frPiXQDwU8WRpcFjO5+f7QQMzlfj7vVlOX0pJ3mFPJGw5PXcvwHgvKzMgtm6enjfeOrFA8DRlnwJW\\u002fjzxP8j\\u002fQk8OmthkIsbOBCPoF5JkhqsDQ3a3a364wZrkxlTMKhCNt3flzMliLdYwGzvlemeylRwuvFm+\\u002fKcsGMsWnv\\u002fkwXT1Gt3tOpPaM0VZkxBoTBYNF8Ozd+bPc6XO0XESqVADycoG26S6o+1Gk6pTPkmm8yCqRlUIYvmK2aN9uPOkHCn9fgv9FaV6rci+YdX1ijJPi+FC28lbtCZylwo1R1tM54zWptNNjmVwmDSxAJGrIQzJYC\\u002fgEkVkR\\u002fgrFy5vlTxJQDwmTHTg3fYg2ZM2aw70Kw7pLfWm6ytADiDKuQzo73ZY0IEK9m0nu9j6yelud6GWwCAR\\u002ffpnG0nGtnZdgtmbNBnk6MIgoxoj67sutgLBRg0mhePLeJzftt\\u002f8VJpvT3MZNATwiTh\\u002ft5iAYfLYjDoNIPJotIZa1sURbVStc5o32z+uPgnbpruL+Z3\\u002fIMDg3z1iAwQJpVhtJkcqzqrjkNjE9rdYsEsWYr2i3O7UO6A5F8QBOnK0Pksnz9R8t9f55esGo8S7gDAotLDeT7hvPbpYf+ty3Y\\u002f4X5RVn2kuRgAODTm1pkPOWq\\u002fTPePuzli3G2nfixWNWM43qBXdlOdJlteBwBRAskHGTcmXMmhA0C+suGR83\\u002fKTTorZvuu9NiX41d3PAZgsiR6siTavqyxGB0J91sixtvLqgy0o83FjpzvvbHTHk2Y7UgLKs36N3N2HmoqatQrHduTsGmM26MmlaiblodmTJBEEkugaC3Gly5vs+fQd9Zl\\u002f1\\u002f8bH+Oi8LK22qyfpp8p71Uy6eFBzeUnwKAF7O2hfO8N067j89gWzDbrSe+L1E3NxqUFZrWaIHE8dh+Pns\\u002f93ujXvlS1jZ7tn1VxLgnk+bxCaNKd9XnvJG904RZ9zbkjfOJuClibMcjAaDf7zwyLGA4trl2H51CWxo0g9w2THh7cedMiJszIY7cMJSoLe3TmHsx+tIFLjNpDjfn2Zcfjl3g3Nhuecj49WUHDDZzm0mdKSufLIknbwEAALeGTyWlqu1m+6e8TdlixW0WzFqrb+t+CtNe8eCLt3sy4TqXfwIycGgUNg5YleqXUsWXGG5xxE02mckmU5kKK5U\\u002fhQpuTPB+mk7txS0jRqvUnnBv0O4qkL1jxbQdbbhFj9XqLbUyY2aZ8pvZoYfY9I7fLZ3gdZptZYqvjTan8tlmm9xsk2vMpbWazXxGdKLPMxLONOIG\\u002fVGp+qVYvs6+TAFqqu+bIYIVzps40Vmqi+TrpPpjxCCGm6yYVm+pa9EfKVZ8GuV1d7ToXnc6BvSWGhy3USi0Ru2uAtm7Fsyp6qPNZjQapG2Gs+XK9dFe98aIH3Ln3+wzK9Z+0cGkDnhiyAEH\\u002fPvSpRbM4MeOXxXxLbGpyVCwteZR+\\u002fLSkP9F8CcSW3fWPV+ry6RTWA\\u002fE7ya+LWpLU6XmVIM+R2aqMtgUNtzCoHIFdD8\\u002fTnycYHYIL4Pwb3SQGkvrdJfaTOVtxgqluQ6H9tut1pcucd4QACDD59bJkvvI0StsuLlYdaBKe6bNVGmwKmgUpoDhF8LNSBIt9mFFkrcmyJT9cb51AwDcHbOFS\\u002fcGAJmpMl+5q0F\\u002fWWtpxQHn0r19WVFhvPFJokUDeiQgyDXozkU91AnEcPx\\u002fvx+2Z9uDfIQPLps8KyOGy3Ld+4hh+LnCmq+3ny6ulR7ILAnyFXaX0PcQdFIYJN5Mp\\u002fqVuapC4qqbttT\\u002fRxxdy6AyEgRujXdAEMRThs5n+fLZcnII6ZNDTUX2hVsix5EqrbNojIfjZ9uXD1\\u002fZrCtCBufHSXcQs+0AkCIKfj55oX35jLTCnp8dOn4oO2FfmB+U\\u002fHjiXOIgXBGTu27sqjhhD4mh++Omfzz25mn+saSC43wG+4MxN4qYXADAcPyCrIrY6pAmDnEURl91JSuN4dg9sVP5DDYAMKi0eUFJ9nilttW+4NCfZ+\\u002fnfv+86JDWagKA+UHJr6YtJWbbAWBpyKjHk+bal78rPdZ5v\\u002ff\\u002fnUeGLBzwX6q2b6zd\\u002fWLup4XqilvCFnsz0c0KA0hvbU9IEUupuO+ivMKGYwAQLQgI5rb32pKwaQzH8PnLChfnE7vZASnkEAAAMKh0X3Z7FtvRPeARHnzxACBgcGb6JZOjyACjUTj5bW8Wyz8hZtuJcMBqNZszWx6wYk43WXbPZJMCQLny+5zWF5yy7c4EzLhusu0Ybs6WPpfX9jop206itVRkNj9UpvyG3NAnFaqfOrLtFNpovw+7z7a3Gc6calxFyraTWDFtqeKLi82PuPMeYrhFb62rUP6Q3foCKdtOhOGWMuW3+W1vkhs8ikf3ti+oLQ1mzJNnj25QgOLLigEAubnakea2azYUOJZbDOSLoDZTBQD4sqMdqWe1pfnfumd\\u002fq1hzSvptlfaM2tJkwYwYbjPZNG2mikLlnh11z+yuf9mKdwxmcshVbD\\u002fb+kOZ+qjCXEt6Gb1Sr7\\u002f8R+UdR5s\\u002fqdae01qkNtxixnQyU1WOYutfVfeekn6DQ\\u002fssIN3QWWUAcKHt17+q7stT7JCbasyY3oIZVOaGCs3JS7I\\u002fUbYdQQbfpkOXt5\\u002fMA4DUqMC\\u002fXr99yaTErrLtAEClUianRPz28q0z06MB4Je9mfbHDig0wn2QJArjGg3NjtWt9bvGe2fw6e6OU8AB39GwZ2fjPmJwis94Nq0vlxYIgvTZEPksWy22nMzuLpsR91Vr28v7xAtdXHPGe7XnPau1MucWsrXRkyRXUipEc4OS6Nk7rJjNjFlrdDJ3JtIcHHU6uaPi\\u002fANxLkbg0ijUO2OmvJS1jdzgHg6NOdUvdld9DgA0G1TkZgAASBF33NsRzBXz6Cx7bYSxPhGEuMi+0GbsMmXQWY\\u002fP3p\\u002f93mbS7m9sv+Z8IrE9sU5yU\\u002fjYL4uOGGxmqVFzrrVymn9Hp9pAv\\u002fPI1YXj+Mm2LJVF48MU3RmxfHnIHPIWiEc57gcy2MzOLW4pVNXbF2L4Lk4FDo5x3y1GpVPDFUwqPYwrIUevYFHbrwCtnbrf+sNTL94uQRhMRRN4DrpazWa58SIA0Kl8P+5MMSuNSfO2YQatpaJJt99gbbJvpjBmZ7c+P9b\\u002fK6cHd81ok9ZrtpcqvrCvsml+YnY6m+ZHpbItNqXGXKo05+O4zY\\u002fr4jvoCjxL+oRU3949DABceog\\u002fbw6fEUmnCiyYSmMubdEdceTiyxTf2DBjgvdTju37oEL5Q4nic\\u002fsylcJM9\\u002fvYnzvLeRMnbYazmS0P4Xj7x4pO5flz54hYaUyaGMNNektdi\\u002f6Y2tzecd5qOJnd+uxY\\u002f69JEyl1VqH8oUH7n33Zi5kk4U7n0oNpVK7J1iY3ZrbojjhStHWarRLOtACe618C\\u002fefFDObTJVprqw23nm\\u002f9eZr\\u002fI+QtBoYfO67JkGfFTCpzg4gZ4og3GfIdy81GpxEJBptSb5UDgB873hHk0sStxjL7Mp3KkrBihMwgOoWlt8oa9Dn2LoQq7dnjzZ\\u002fPCXzO8Si7OOEcCSvGvmzFTWdbf7QvT5bc37l+jh+n40mJqrSn9zW8acOtAMCkcgO5qXy6rxUzNRuLVOYGHPBs+RadVb4g6BXyI53prLKqtjMX2n4FAAaVLWaGM6gcnbVNbWnGcGu480h\\u002fBEEGQYtC+\\u002fX20wDAZND\\u002fd\\u002f9iHsetu8NpVOrrd8y\\u002fWLxBazB9\\u002fPfx6WlRPl7uZnL6ACXcB8k034mHWzp+r8jNilfz37s\\u002f6o5EYQ\\u002fDWq249aI8e2fj\\u002fipdDTHOorJuCLmOGEEQZBBc3c9yQ63s7x+PVxQ31VRIrRYbADy48kviBqvunn73E\\u002fOJkSfWfFecV7\\u002f5xEsCIee\\u002fv88f\\u002fDervkYGOO4XKJo2P+W2h9qH8TpUl7Vs\\u002ffVUTmaVvE3DZjMi4wLmXp8+f1kGheri4qRXGw9ZjtHHbFdTa7KvZEkwvIfxL\\u002fMC2wdikzCpdD+WwD6vpspsIDdfPTmKOvtCAMerq\\u002fHUjoo3feOoQq6zuk6EBThXevFicHRWE5VCDeSIHEHHqFUT5nr0X1e6f\\u002fb+7PcLrZX2UaWxQv9QXvvoMxJ7WfnL8loAuCirJibcB+GdR64iKoX607i3yVFkwPDobPuC1mp0bnGLzNQ+gnV\\u002fU\\u002fb+pmynNlfUFtencTGTN\\u002fjZak+9eDsxcwCvOZGu2LPtftyZab5vM2liYlOc+NFC+Ye16r\\u002ftq1L9sWbdgQCe08+8rrTpT7caTgMAnxGd6P20hDudtIEV0zbpDohYqaS4Q6XqZ0e2nQLUBJ9nI4RrSMN4E72fq1D9VKb4BgAHgErVBjF7tD+X\\u002fNvSTY4q6gBAo7DH+H\\u002fpy5nkvIkTs02e0\\u002fqCI9seyFuY4vsag+r0uyJW\\u002fEiDdlde2+sYbgIAqf5ElfqPSOHtxG06q9f+CwB0Kn+U5F1\\u002frlOnaYRwjcpceLH5YZOtvdu+QvXjwCXcAWC0942npN8CQI5iq8GmnOL3AI\\u002fuS97I0\\u002fw4caAAAJCZKokJd\\u002fsI9yjB1ErNqRZDMQDu6L1oM1bYF4gJdzqVNdr7xjZjRaLXwhBeBjFRbsZ0Bxvfq9KeAYBi1f4Jkrv4dKc+yzDe2DDeWPuyyaZ1JNxTxcsYVE7Hdl1TW5oPNr5nz7aniK6b7PcAk9pReaxEffBI08c23FymPhLMHZUi6u5yrEi1r0Jzkkf3ner3YLRgOpXSnkYzY\\u002foq7WlvZoTT1giCDLx954tNFisATEuNDPIh1w7thpDHnpkevetMocFk2XOu+PYFY8hbeA5KuA+SeEHMZN\\u002fxZ9ouOCLNRulbhR9F8MJGeSWFcIN9mGIWjQUAJpvZhJlkZrnU2Fatqy3RlJswcqaAApQHotf6slxf5PfWpurz7+fvOrXg5Tq9\\u002fPOiA3nKehzwSJ7knfQbovgdX3u7G3L+rj5vn7wu0SvwzuipM\\u002fwTOv6VnrZpM2nnHPzg58n3\\u002fl19\\u002flhL8SRJzAcZq\\u002f6oPPNT+YkAjtc7o29I8gpy+rcQZEi6up9ltULXVK\\u002fg8tnxKSEFl2sAIC45mEXozg0IcbpUc2hpVH7y2rZzx4oBgMlimE2WmgppdJ2ctNmeLZlfvfMfhmE0GtXHT6hS6PIuVeddqj6xP++NL25jMJ2+Mnq18VAWzBUDVAFA1ZUhz0SOYNCVcdYusaj0CH6X1z9MWvu70bm0yFVUrmmv0NLNoHsfFl\\u002fI4HSfo+kGndp+ZY7jru8F9mI4XTLRqFQA8GKwiXkrR7kVG+Yi992N7p+9P\\u002fs9T9lgX4jtIl1uJ2Hz7QukIfaD8M4jyLVDwmq\\u002fyqruVHXKHb1N09uuZPdI2DS3hlZ5lqdevB2DQiOHkEHhxUoa4\\u002fcZ5UoKz4FKYaX4vGq2yZp1h+yRcuV6NxPuzfrDAODDmTDW70saIcnoQKfyQwUrydErzDZFqeJrx2qq5K0Q\\u002fvKO5iuoFGas6CE6hVsk\\u002f8geyW97SxI6lUrp4ePQuf5GmfKbMsU39mU6lT\\u002fO\\u002fxsxO8N5E7ISxRcmm8y+7M+dne73kcuh68H8pRhuymt73b5aofw+THATjdLeUdcVCoU2PuB7ESuN3ADgxUxKk7yT2fygfVVlyjfZWlk0p2SxB43yvrFOn1WjPQ8AperDFZoTccI5SaLFgZxkl3+vR\\u002fix4+wLMlNVtKC9t0ZladRbFQCQLFpaqTllxnQKc62YGW5vbTWV2xeICXcAGOtzG3HVgUnlzQ965deKW4w2NQ54gy473mseeaP+Odf6o30QfYxgxsyAJ0mt8cJ5Rqv6pPRrAMhs+y3RaxGt02fQoUJzgkf3XRXxLY\\u002fuQ4wzqdx4oYdfNoIg7jiRU2lfSI\\u002fruGHaTSmRAbvOFALA0exylHAfIe6KuLXJ0EIa3Fqtq63W1RIjPWJQGfdHrZ3kM47c0D\\u002fHW4rfyftvkiR6TeQkuVl3Wlrqz+7oJvq0aP8vFaci+L4rw8bgAKekpY9l\\u002fvl00sK1UVN6tc26gr1hfJ9xPlFHm4tezd5Wpm6+KXzcX9Xn38zd8fe0hx2bIchQdhU\\u002fy4mjwj7acA8AGPTmFRPfAoCn3l4ZEdNdys\\u002fu8zd3tDarnn33xkmzErl8lkFvzrtY5eXtNJYt+0Lll+\\u002fspNNp\\u002f\\u002ffCdfOXZ9DpNADIOlu+7pWtl86U\\u002f\\u002fjp\\u002foeeX9K3jYe4uYGJ22uzAGBj1fmbI8YJGE7XYN+XHrcvzAogdzESiVlXYWxjPzmG2\\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\\u002f0dI2XYEQa6iJln7eCZvYa+\\u002f5kT89u+gpja1c4uHdfnVjngcn857Nenpbyo2XJRnk9vcliiMvStidSi31304PXorb+fX428f5xNpX8UBp1z51ZKtqP2l4tRYn4hvxq9l0RgA8FjC3AfP\\u002f\\u002fpp0YFJkphYgb+b2wAAg0p7P\\u002f0mM2adceC9\\u002fY15e2Y\\u002fFcwVayzGbbWXTDaL\\u002fYEIMsQN8c+yS9VlLV\\u002f9\\u002fXBEbPsnkcNljp\\u002fuNPwEADZ8th\\u002fH8Dsfnbv4xo4+gIxJMfc\\u002fs+j95zfv3nxh7SNzePz2pGSvNh7ipvnHZniHZclrW42aG459c2\\u002fstDhhAJvGqNXJ\\u002f6g8ay8J4scW3BXT0XfYmcuyJEOco94x+8oAfJe6\\u002f9MONRV9VniwRtc+xKy3uuilcBl0oT\\u002fP3p\\u002f9rrX0blSp1XlsvkfeeQRB7FJE7ckdHPCDzbkrQ7vLmHQmZrb3eyWJQp5JvN65cagb1i8esaNT+b7s7gqncOjB3uyxMmP7vZWthlNuJtwjhbeTatS4r1G3x7EcLryV0OISJVxwc64p375Sr\\u002fm3x4Q7ndox5qNU8UW58nv7MovmMz7gRwEz1tHalSbdPkeXgD93FpvWfdKZ4s+dU6naYF+RGc72mHAPE95MDjkTs0Y5Eu6OgfYDhEqhT\\u002fP\\u002fv3iv+Zltv1Zrz9lnEFVbms63\\u002fZIp+z1OOGe87x1CRiD5Yf1AAYqEHdOoz5OZqhxBez0ZCSuGSqH7sKKkxpJmQ2Gi10J7a\\u002fuMqaxYRw7BHYIrvQUWT08JW6+\\u002fjOE2APBhRXkxXN9JT6eyfFkx9sL0DYbcbhLuLJrAMdIfQZChQK5pH5ZkMludW3pmNFvsCwrtwI5t6u5KD\\u002fE4Do39dNzDearCbfW7SzTl7k+3zaaxxohHzfGbnihsv73L4+YGJDmy7QBA\\u002fKbcUZcFAA\\u002fGzXYkxDk05gOxsx46\\u002f+vWmosvpCxxcxsASBIFAwCTSg\\u002fmiDVWYzBXDAABHC8ccJXF4IeSC8gwMZQ\\u002fyy5NmBnvyLa71NqsKs1vAICZi8jDeVLHRACA1WIrzq0fMzkGernx0EcBymfjb3343B\\u002f5yoYmg+rt3F2kDcL5Pp+Pu1XE7HXn+RDHuXLKNWHd\\u002fUyxFyt36ceyk58Xtd\\u002fnLmJyp\\u002fjFJHgFBHFEQgaHS2eyaYw\\u002fKs\\u002fZR5EPhH4+e3\\u002f2u2NUabIoKE0c4tzoQqRzuaH+v\\u002fMIgjiE8ySxgsAyTRMAbKk9uyxkHO1KHSp3JHuFbKsDAChVN5Hbhrxh\\u002feIROy9mYjfD2+18OBMdCXe1qcC5sUtuFp\\u002fpzGyTO1LJFKBKOFOdml3x482CtvZllTnfhhtIY9hJ6NT2vqISxecVyh\\u002fsy2y6\\u002f4SAn3iMCMdm3ZAbLzmWJRwXXeMkQmbHQBOFKbujwRUuI7THl8GkeTuWLTa3xkhSKXRfVhQ5SsChd9dB4seOWxLyrsYiLVbtK1YdUFkaAQDDbcWqA+Xq41P9H+6+CnlvSdhxjfo8tbnRghkZVDYANNkT7uw4APBjx0uNJS2GIvvGNtyiMNeCvfh7bziKobt7KeW2FkOxfcGH1ZHi6MwxaF1raXFuceLHjutVRwKCIANNwGXJ1XoAqGqSk9t64ngI372pVvush293ZCCkeiWleiUpzMosRW61vq5O3yAzK4w2o8FmxHCMTqWxqCw+nSdmevmxJKHc4Bh+ZAw\\u002fkjbAdRWTRK47fgGgWNUEAKQa6\\u002fbVAlWD+9sAgPDKPftsGsNRlpdFpUOnAYAIMvQNzc+ySzGJXX7A7SpLmu0La+Z+6NzSQSXX2hd6tfGwIGZyf5p85y0n1ldp21hUuhXHqBSKF4MT7xUwNzDpupC0EXn\\u002fjfBK4QWFSefc4qSriV5L1S1fFh+2L6+Jmvhk4tzO7xKpTosHeeTZ+7zfvVntQ\\u002fNSRSEvpfa6elI\\u002f33kEQUhWhE74sHAHAFRqW36uPHpvdM+1HRzG+8RSKVQMxwpUdQ16WTD3qlUMIJZQ11lNjo69bgydF4\\u002f0WY+JXQDgMzqytBpLR4mPbjCoQh6jvdZHb9mLw9jxGBE0CovQ6BqTKmLT\\u002fIw2KQDguE1lKvRmjyFvRGBPuFer\\u002f3Rk27n0kAmBP3Ho7t76qTTlOZZ5jO4yqnYMmpdj2THfaVeEjJ6zxlTC\\u002fJ84dFfCzoFDE62K+I4c7SUBw2+c79pxvmvr9dm5im2VmtMAuBU3HWv+FAA8mHO3l3HHAZebq\\u002f3ZCWZMLzdVAYCEHQMA\\u002fpyEfOVOmanKno6Xm6rtw8lJBdyvwFsMxXX6SzJTlcbSYrSpzZjehputmNmGt48z9Ti9rT2hVqo+XKpu\\u002f8XYDaNNQw4RcAjHD4IgQ0GAt8CecD+QWfLQsklMhrvJbbPVdiCz1L7sLxY4N3qYu68J8TgxUzTHfwjdlyRmdtzZR6KzGukUGo\\u002fu9GPLi8mhUiiaK7fVu7MNABDHHNFcVxJAkGFmqH2WXWL31Hmr0xoBgEKlBId1ebnu+Ed6tfGwYLJZ7jv7a5W2LdEr8OsJayTsgf3qHSKiBBL7gmMOz850VpOsi6TwnoY8DMcBINEr8PmUhS4H\\u002fgxcCXKPPHuf93uqOPjv6kwAKO7TqNJ+vvMIgpBcFzx2a+3ZCm0LAPxYfohDY66JmEbeqAsStnBeQNr+pmwMx94v3P7pmLvoV6NfHADYNAaHxrSXnCrXNPmyej4jDZ0Xj\\u002fQZMRHcFRat4zYpC+bWYGri+OveMmNKxzKb7t\\u002fR0C023d+ecAcA85VcZ1foVF6Tbn+R7ANHJNHnWfez7eCcND\\u002fbdBuhpWcYbrFiOmJZGxIW3Y8cGnpCuKNDuKOlxtKDje8pzDUAcFr6XaxgJovW86nDHY7UucxY6c9OaDYU2O\\u002fo9eckAoA\\u002fOwEAcMCkxuJg7mhHqffOCfcKzcmzrT8ozfWk+EAz2Xr3IwrrtteE2L+CDCmnT5a88eIWYmTf8RdptF7c6IY4ZF2sev6JjeRoJ2w2479Dz5Gjg25SckRhdQsANMs16\\u002f4+\\u002fuKaOW7mFz\\u002f750STrP2bdGJyH3um3YQS7kjP+HS2FZdrrUY+vWO0oMpswHDcMX7QnW0QBBmyuDwWAFCA8sOOxynUHr6serXxsPBb5dlcRT0AfDDmRvezrp5FnClUZzVx6QPeYzHqSi2UBr2iUtPqyAITXWir7qpiUoNeYV\\u002fI8Al3me8GgMuyWnLIQzzy7H3e75Mk0fZRpbmKhjqdPJTXu7xGP995BEFIGFTa66mr7j3\\u002frRmzAsCXJXuOteSviZg+wTeWOE68yaAoUNVlyspXR0wjTh\\u002f6cNzC87IypVmXKat46ML3j8YvThM5XYAZbZY8Zc2p1mKd1fhKyo3EJs9KE4Wfl5UBwDdl+xOEwaKuR8M4DJ0Xj\\u002fRN96VX7IipYSvm1h2E9D7NlWpHzOl3k5UmcVSJAeeUvUtqU1G99l\\u002f7TLB2+W1vC4OSOHS3CpFjuMlRwL1vMNwM0OWfRqf0\\u002fd0bZH7suBVhn\\u002fxZdYfJprVghmrtuXiveeSN+kTEDGVSuWZMb68V02IsAQA+w49PlwCAmBVub20xlgRzR8tMlQDApHLFTKc6e5dkG8+2\\u002fmhfZtOE4bzxvuwYAcOfReUzqBwGlZ0j31qo2kt8iKfYy+AAgB873t5J0D0xM4wcQhBkCFswPv7nPRfsA7C2Hs9tU+qeXDU91E9E3o6gSab+dPOJw1ll9lUqhbJ4Qs8nh\\u002f5ACXekZymikEJVY4GycYJvx\\u002f2M9kIxjhoy7myDIMiQZa\\u002fwjmFYRUlTj\\u002fVnerXxsHC2tQIA6FRaeC8zpx5EHNtYqm7pVf63byL4volegUWqJgBYX3r8gzEuEjG\\u002fV54hh65gUdsH+8i7GIi9sy67b9OZusMjz97n\\u002fe7HFi4KTtldn4vh2Js5\\u002f3038TY6ob+kR\\u002f185xEE6SxOGPTF2HueyfpVazUCQJ6y9oXsP2gUqg9LIGRwLJhNalQ55iteHjqe+Fh\\u002fttf7o2+zPzZPWXv\\u002f+e9ETF4o14dNYxptFplJ3WxU2q\\u002fopkgSiA\\u002f0uFXhk+0J91J1400n1432jvRlCU02i9piaDWpbgybdF3wWNJDhs6LR\\u002fqq575VYmLa\\u002fXnF+4HwFHjPL8+O2EncVUe4Q63mH1LEZGu72PLI5MA\\u002faG50FWB4dzOg9B9lWN0pwqWLI3iTStQHAUBuriE39xUFKL7smEZ9rv3fbDOWA0AAO8nR6sdJqNdlSQ0lACA3VQOAhB1LPHhkpspzrT\\u002fZl0eJV07yu4\\u002feqTwRk9bRT+NZHJrIvuDPSZjh\\u002f5hTG4Igw190kM9Ns0b9fSTbvno8p+JkXuWo6KCJSeERgd5+Ij6HxaBSKHqTWabSVzbJMovrLpXU2wiFrFdOT40O7vJ+fY9ACXekZzeEjd1Sm7m+9OgocSibxgAAo83yfdkxClCWh2a4vw2CIJ7CZLWfvVVy19nG3goIFscmBZUVNm7ecPKlj24mNzvr1cbDgj17a8VsHxXsvz1qkj9H2Ks59zwl3Tv0TGsFAHxedChJFNhNpS9PuTd22tMXNwPAnoa8UJ73w\\u002fEzHbNr6K3mdYX7M9uqidsTJYuC\\u002fq27DADHmkvKNdIYQcf91xiOb6vNei9vT8fWnuaRZ+\\u002fPfn8ice4ZaYXCrDvfVnnn6Z+fTp6f7u00Nspos2TL6463lGitprdHLyc2Qf\\u002feeQRBXBotjvh50iNflew9Li20R2w4JjWqpEaV84YusoGjxRE\\u002fTXz47fwt+cpaAFCadUqzi69XCUtIDnnUFEnCPdFzfqo4DAAaq\\u002fGktIjYqrO6HtI7RF480jc2rIfqZ+C8DYMwkHyAMKlejmUrrie0dMeGdRx1DMK\\u002f0I1g\\u002fvURwtWZLQ+ZbQoA0JhLL7c+N8b\\u002fCwr08F1Mp\\u002fKoFAZ2pfx3mu\\u002fbDFrvDm\\u002fiePwRgE1v\\u002f\\u002fNxvLu6KL3lx45r1OcqTLVwJeEeyE12tAawE+t1Wa3GUgBonzHVuZ5MqfqwvRtGwo6d5v+Iy74iU7eV0\\u002fvDXmUerrxyBEF6JJEI58xPUasMKpVepdKrlQaDoX2kwtD06MqpZfWtWaUN9lUMwy+XNVwua1\\u002ft3qiYoMdvGvCywCjhjvQswSvw0fi5nxcfvPnkN1MlcTjgp6RlNbq2h+NmJ14Zve7ONgiCeAqNRg2NlNRVtf7z88nYpGAuv33AiNViozP6OCrn\\u002fmcXv3DvhhP78+h06s33zgiP9gMAtVLfXK84d7yYRqOueXBW3zbukcykVZoNWqtRYzFpLUat1Wi0tV9EHWoqrNXJeHS2gMHi0VkCBjuALSSNJlaZDXKzTmMxaq0mrcWosRilRrW96VxrJYbjfAabT2fx6Sw+g+3PFnSeCXNleMYpaRkA\\u002fFF57o\\u002fKc8QmFo3hy+KniIJXhKVP8YshNnncmqiJ9oR7kappyeEvxvqE+7IFJptVbTG0GNS3Ro5fEebh\\u002fsv5Qcnzg5IONBYCwPrS49trs8b4RPDozFaj5pK8Vmsxxgj8wvk+h5uckj52S0PSvik5qjTrDTbzzcfXzwtKiuT70inUBoPytLS8Ua\\u002fk0Vn3x03\\u002fqvgI+ZGe4JFn789+D+B4fTbu5kcubNRajDmKurWnfhIzuWE8Hw6dYbRZWo3aJkP7qNLp\\u002fnHkB\\u002ffvnUcQpCuhXN8P0m8v0zSdlBZlysubDUqlWWfCrGwaQ8zkh3C8k0Shk33j44UufpqG8yQ\\u002fTngoU1Z+tKUgR1HVatJorUYWle7N5IfxJKPE4dMkidGCAPLDPO2+mLkTfGO31Z3PU9S0mtQ44Dw6W8ISxggCUkVdVjwYIi8e6QMT1kO5cwAw2locy27msvuDSesY8WewNhFaumOwNjuWe6wgz6b5jZK858OZAABj\\u002fL4833w3hpsBQKo\\u002fViz\\u002fJNH7GfIDOmFSxY6S8UJWkpDplOq91thz4gDAY0icW\\u002fpFwo4DAI2lxWTTqCxNABDISXG0+nOSAEBladRbFRpLK1yZZ9VBbWk\\u002fJII4qS6z7QDQZMgnh1yhEUqomzE9g9pzIaYw3lgKUHHAmg1FKkujF8PFaR9BEKLQcJ8XXltGjFgsNpVSv3vn5T9+PkmMDxEcFuOLx1a88fP+Q5faq8S4aWZ69Nv3LOKyyDkBj0MJd8Qtd8dMj+BLfq08taU2kwKUeK+ARxNumRfY0cXt5jYIgnjKrffP\\u002fPDFfy6eLrt55nu+fkKLxapS6O\\u002f4v7k33jmVvKl7UsdEPP\\u002f+TZ+8tv3I7pwju3PodBpQwGppHykze8moPm\\u002fco8cubLKX0u7s5\\u002fLTpMivU+\\u002fJcB5K\\u002fF7+nt31ucSIw7912f\\u002fWZRMj76QvXxaaTowAwHS\\u002f2MXBqXsa8khxADDZLA16RYNesb8xf3lY+pujllHdnJCl96b7xz0YP\\u002fO7kmMAoLEYjzaXEFu1XYxt7Kf3M26kwLb9jfkAIDVq9hLehESvwM\\u002fH37qvId9l2lfAYH8+7pZHL2xSWwxmzEraBf4c4boxq2KFft+UHLXnnT3LI8\\u002fez\\u002f2e4RO+adp9L1\\u002febj96FWa9wuxiJKAf2\\u002fWwuz6\\u002f8wiCdC9WEBgrCLw7eja5wQ3jfGLG+bjoY+vKOJ+YcwveI0c7+XvqU+RQ19JE4aQ67G7q7Yu3uyt69l19eq8Qj9CZK8mhTrSWCscyn9nrXdxbIlaqPVMJAHpLrQ039Fho3mxTmGyt9mUKhebF7KEqrog9yp5tBwAxe3Sa79vZrc\\u002fbV6tUv\\u002fAZkaGCGzq2dsWLlWzUtyfcNeaykZpwt2CGHpPLjfq8Ot0l+3IYb5xzY7\\u002fYR6zjgFXrzgHgdArLlxXtaA24Uhjd3urY3sFRQEZvUxLjDsWqA25OpkqnshhUtgUzAoDMVMmj91wFgkf3jRPOLlEfwgE72vzJ9SHvUyko94UgvcNg0HwlAh+foXtLEIfF+ODBpcdzKn7cdd4+h2r34kMl9y6dODtjwL9J7dBJB4FbIybcGtH+i6cbswMSZwf08OOpm218WfycpW87Vv+Y+oBj+Y7oqXdE9zFFiCDXrNlLRnG4zO1\\u002fnCkvapQ2Kbl8VmScf1hUvwa2TF+Qmjgq7N+N5y6dKWuul9tsmH+QOCBEPH5a3LT5HUNa+rDxUPZX9YWvio+ozAYahRrK8\\u002fZnCxwj6K2YTWM11Whl9hv5d9ReTvQKXB3Z8wmzzx6JnzVZEr25+mK2vLbVqMEA59PZfmxBnNB\\u002flDiUvLUnMKi0dWNvWi5N3157KUdRLzfp+HR2pMB3cXDqyrAMBpXmckpPuwyf8B2zHvmj8twpaVmdTmHFbV4MThjfZ4Z\\u002f3M0R43h0FgDECPxK1T3\\u002f+umDfj67R\\u002fZ7BN\\u002f3z2n3nWutPNRUeElW02rSaCwmFpXuw+JF8H0zfMJm+sfHCv3JDwOA\\u002fr3zCIIgyMigMhf3mNFuM3Tcg+XFbC+iPXDoVL6QlaAyFQIADphUfyKQt4C8kbMW\\u002fVHHspCZ6E4ddqIg\\u002fhKdpaZM+Y19NV\\u002f2NpcR5sPuLnfsw5noeFKp\\u002flgwf6lz+whxWvqd1FgaI5gewsvwZUWTUsZ6q6JYtS9T9ru9dyRaME3M9ORvRTEzxD4zaqXmNAD4ceKIL4BDEwkZgWpLU7X2LACwaHwvptMocj92XJFqHwBUa8\\u002fITdXerAhHEw54oXLPiZYvHJEeBXJSanUXAeBs648SdqyjRHs3JknurdVdNNiU9bqsbbVPTPF7kDhCHwCsmKnJkF+tPWfGdHMCnyM2IQgyjMwYFT1jVHRFg+xUXlVRTUtlo0ytN2kNJhzH+RyWkMeODPBOivCflBIRHzqol1cUvOuRXwiCIAgysm0oP\\u002fVp4UEAmOYf+2radYEcF3dq23Ds86JD9rH28cKALTMfIm+BDDdovyMIgiBXS4n80wpV+2SSADBa8kEQfwmh3YneUnu8fqlj3tSpwVu7Gs2tMhWcbrzZvuzFSpoStNm5vRdq1JsKZO\\u002fal73ZYycG\\u002fuLU7AwH7EzjLfYEPQAk+bwYIVzjvAkAQKvhVGbzg\\u002fblAN68DL9Pndshu\\u002fX5Ru1u+zKD6jU5aCOP0eWtHkab9FjdQnshGgpQp4fs5DEiyBv1RrlyfaniS\\u002ftyjOj+OPFjzu1kpYovypXf25fjxI\\u002fGiDoGk5FUa8\\u002fuqn8ZAKgUui8ritzs7PrQD9mEevTHmj\\u002fNV\\u002f5nX6ZSaEJGIIcmolNZNtystbSpLc2OSXd9WJHLwz52Jw3dK9tqn2jU59IoDBtuyfC5dbLkPmLr\\u002fsZ3ytRH7K0h3PTlYR8TW0027e+VtxltagCgURgxghkiVhgVaBpLc60uU21pZlK56d43n2\\u002f7GQA6\\u002f+Mk1dpzu+pfsi+zaPwgThqP7mPFTSabRmttSxUvT\\u002fJa5PwIAIBGfd6u+pfMVyYYYNO8RMxgBpVjxYw6q1xjabZXmY\\u002fgT1wa8j+nRwIAQKbsj\\u002fOtGwAgwWvB3MDnyc3IEHD6ZMkbL24hRvYdf5FGoxIjSD\\u002ft2pH1+bq9jlU2m\\u002fHfIdRB5RY0wh1BEAS5Rhltlu9KjgOAkMH5aMxN9jHRndEo1AfjZv5SfgYHvFonIzcjww3a7wiCIMjQUar40p87q4tR4XiR\\u002fCNHtt1rsIqVhwhWlim\\u002fsc9lKjderNVsDhOsIm90RZXqN0e2nUEVhgpWOre7K833bYO1QWHMBgALprrY8sjkoI0MquuybGyaX6jghhr1JgDAAbskfWJi4C9Mqoi8XScYbqJeKXUyyDDcKjWWkqPOMNxKXKVT2Y5lDLcpzfVK6FyDhZLotWCq30MsmoDc0m9+7PhGfa4NtwBAIIdcKjaAk1imPmJv9eOQj0wWjb84+O3dDa+YbBobbilRHyK28umSBcGv+bAiL7T9Ys96dy+CP3Gc79rMtt8AwGTTVmnPEFstNhc1\\u002fQAgiJt6U8Q3h5s+aDYUAoDRpmo2kKfRBgAe3ZccQhAE6TeUcEcQBEGuUeUaqcFmBoB4L\\u002f+usq52bBqdSqHYcJxNQ9+bwx7a7wiCIMjQobfWX2h5YLTkQw49kBjHcFOh7ANitZYYUfsI8YFGo7BSfF7NkrbPPVDQ9o4V00cKb6dQnGatx3BLpeqnUsXXjkiiz3Pdl8fpBpXCHOP35ZnGW\\u002fTWBgDQWaqzpE+N919PelKHWNHDUv1xg7URALTm8lMNNyZ6PxvAnetye5NNJjOcb9btt2CaCYEbyM1D1VS\\u002fh6IF0+p0l6TGErWlSWeVWzEjhlsZVA6LJvBmRQRyUuKEs4UMpyPHg4jzoAZwyOWM\\u002fNkdtWRJBdztgripqyM35Ci21WjPqy2NGG5l0YQiZnAEb1KK+HomlQsA3qxImannmQwAYILvnWG8sfmK\\u002f5oM+TqrDABjUvk8uo8PO6rza3MQM0NvDP+qTnepQnOyyZCns7aZbDo6lcmhicXM0EBuaiR\\u002fkk9Pdx4gCIL0gecvIM+er3jx9S0A8P5bN04cH22x2nbvyz1yrLCuXq7VmcQiXkpS8KL5qePGRJIfCQAAly5XP\\u002f3i3wAQGuL9+49d3lV0+70\\u002f1NXLAeDj924ekx5hD27fmfX5NwcjIyQ\\u002ff3d3m0z75XeHLl6q5nCYM6bG3XvndA6Had\\u002fmn+2ZbTJtSJB41Q3jFs5LJfyr\\u002fX3xV8uGootvZR4mRwHO3vhwINfzHd3XpovS+i9yz2S3NRqs1mCecElEwqNpkwc0BaOzmpM3ku+1dMei8PhvZywnR4eqJr1m0pb2io1EL42ZdX\\u002fyeHLUEwZ\\u002fVyJDFhXap8HUWc3OLWT7GvJtuL0+ph+5DRlurtn9vmPHpS++PEiOAgCAWMzbuuVRcvSasWnTuR9+PEaO9tKsmYmvvrqMHB16jhwp3LM3t6ys2WAw8\\u002fnshITA5cvHjB+Hcg0I0m6QT5U+nIkywzmF8fLx+qX+3JlidgaL5mPDTVpzRZNur8Ha5NgygDffnzt409sG8OZHeq2tUv0GADhgxfJ1tepN\\u002frw5fEY0ncq3YhqNuaxFf4T4CkP4y0P4yx2rfcCkiccGfHumcbUV0wKAzHCuQPZOiu\\u002fr5O0AAIBJE4\\u002fx\\u002f\\u002fJs0+02TA8ARmvzZenTTKpIxB7FpYfQqXwMN1sxncHapLNU2pP4ACBmpzv9KwMvgj\\u002fp\\u002fxKOkKNuC+SkkCqPD6Y44Zw44Rxy9IoATlKPfxqP7jNZcl835WJujfyRHOpan9+NUN6YUN4YcrQn43xuG+dzGzmKIEhv6E2WCS99BQDbn10bE9DzjMdEp0tqnvlt16S48E\\u002fu6O9EHf15GX02gDmmmlpZZITk5Te3lldIHUFpq\\u002frIcfWR40VzZyW98PQSOt3zxZXq6+UGg\\u002fnZlzdXVbcCgE5v2vrvpWap+t3XV\\u002f628cyG307aN6usbn3\\u002f4z0AlIXzXJyyr9aLR4amCy11qw\\u002f+ZcXab+es1ii+zjubK2v+be6q9rQNMkygXYkQRQkkXDpTbzUXKhv\\u002fqr5wS4SLPh4TZt1Re3ldwX776g1hGc7tyPCD9jtyzVq3bu+evTmOVaVSf+5cxblzFffcM2PN6kmEDREEGQw0Cme8\\u002f3e5ba82aP\\u002fDcFOTbn+Trv17h8SbPXaUpL2o+qBJ9H6ORmE7KpXrrQ32\\u002fLtL4cJbk3xeJEd7j8+IyvD7NLPlQRy3AUCt5h8eIzLSay15OwAAEDLjJwf+eUn6uN5Sa4+YMaVUf9x5KwRBEGRYOltaozWajxZUkBuGiQFMuBcUN+4\\u002flF9Z3crjsVKTQ3x9+Hq9OSevTibXAsCho4UYhr\\u002f24vXkh\\u002fWbxWr7av2RhkbFrOkJZrP19LlyADh9tuzwscJf\\u002fzzt6yMYmxFRVt5cUdUKAD\\u002f\\u002fftJlwv1qvXhkaPog67gjRetwsrHqdFP11MAIUhwZytCuRIjYNMbdMVO\\u002fKj4CAO\\u002fm7t5UdWGCb2QAx4tOoZowq9ykq9bKsuW1WqvJvv38oOTrQ0cT\\u002fwVkOEL7Hbk2nTpVSsy2E23YcGLihOjo6JFwJweCDCMidhqFQk+TvMtnxpQpvsHw9u8dIgpQw4Q3J3g\\u002f1edSLf0RJ35MxBpVJP9IZ6kmt13BZYTGix8P5C0kN\\u002fSVL2dSss\\u002fL+W1v2VeL5et4jAg\\u002f7nTnrdoJmLFTgzZXqH6qUW+yj4vvBo8REcJfQY4iCIIgQ9KkuPCt5\\u002fLmpMaQG4aJAUy4nzhVAgDXLx790H2z7OVcAADD8F\\u002f+OPXbxjMAcOR40czpCdOndNQF85Q9+3M+\\u002feDW0WlhAPD9huMbN58DgHc\\u002f3BUS7P3tZ7fzeCyL1fbgY79VVEpbpOrq2raIMF\\u002fSv3AVXzwy1Ois5sttjeQoAACcbqpBWdphBO1KpLP746ZbMNtP5aesmK1S01qpaSVvAQAAfAb77pgp98RMIzcgwxPa78g1aPce19l2AMBxfN++3EcemUtuQBBkIHmzMgCAAtRor3uCeIsbtbuk+mN6a6MZUzCoAg4t0Jc7JZi3hM+8mrkGP+4MCWeq1HBCqj+mNOWabG0WTMOgClk0HzFrtIQ71Y8zg0LxcFYhTLBKZ6mpUv0KADhg2a3PTgr8Q8CMJW8HAAB0Kj9e\\u002fHiU191S\\u002fTGZ4ZzKXGi2yc2YigI0OpXLpvnzGBEiVqovZ5KAiS7eEcSJRm04c7K0pLipsrylVarW68wGg5lOp3K4LC6PGRgkDgn1joiSjEoPDw3zTBUODMML8upzsqpLS5qbGhQymdZoMNtsGJvDZLMZPr6CwCBRSJh3YnJwUnKI0KvXHY0USseN62az9czJ0osXKivLWpqbVQa9iUanCYUckYgbnxSUOips0tRYR7qvz7Qa45mTpTmXa6oqpNIWtU5vAhzn8lgBgaLYuICMcZETp8QymZ45SQ7mcw0FU+LDz777CDk6fAzsnkgfFfbUYwuIESqVcvfaaU3NyoNHCgFg49\\u002fnBiJnnZQQbM+2A8B1i0fbE+4Yhq9eNYHHYwEAg06bOS2+olIKALW1ss4Jd7h6Lx4ZaqR6HYa7njm9Sa8mh5AhDO1KpDMKUP4vYfbysPRddTkXZdVVWpnKYrBgVhaVwaMzg7iiaIHfREnUdP84frezayLDC9rvyDWouNh1l7NdUXFHIWYEQQZUvPeT8d5PkoIcemC06L5oUZelrnvkxUpeHJlPjnoChULz587y584iN\\u002fSGhDO1Vy8v0fvZRO9nydGuMaiCYP51wfzryA3uiRE9ECN6gBztWpz4sTjxY+QoggwftdVtG74\\u002fdv5MmdVKvv\\u002fbZsNMJqtSoWusV1y6UGkP+vgKpkyPn78oNT4xyHlzd6mU+m3\\u002fXNizM1up0JHbAPQ6k15nksu0ZSXtP0goFEhKCZk6I+GGVeMpVLfqvzIYNCqVAgA2G7ZjS+bG306rVQbiBlYr1mq0tErVZaXNu3ZkCYScFTeOu+X2yQyGi8mWe9TSpPrjl5OHDuRbLTZSk1plUKsMpcVNu3de5vPZy28ad9OtE7ncvif3B\\u002fO5EE8Z2IT7TSvGkUMAALB29RR7zrq4tKmuQR4a7E3eon8S4gIcy4EBXlwOU28wA8Co1PYsPAAE+HvZF2SuPu1w9V48Mow4Zt4bCDw689yND8uNBrlJrzAZ5EaDwqSXGw1yk0Fu1NuDUr3Wdf4Y6aUB3ZXI0BfCFT8YP5McRUY6tN+RaweO42q10zUniVLp+vcwgiAIgiAjzK8\\u002fndj022mbjZxq74asTbNz28Wd2y6++Pry2fOSyc3dwnH4d2vmLz8e12ldFM7qCo5DQV49huE33jKB3NYFLo8FAGqV4Y2Xt+Rlt0\\u002ft0A2N2vDbhhMXzlW88b8bfHwF5OZu7dx28ftvjpiMFnJDJ1qt8Y+fTx7Yk\\u002fP8q8vSRnfkJN03mM+FeNAAJtwpFBg9yvUODg3xDgoUNTYpAaCouMnjOWs\\u002fiZC4KhCw9QYzlUrx9++Ic6\\u002fcOWI2WR1Bh6v44pGhxp\\u002fLp1IoLkdGh\\u002fDbu20GSABXEMDt7ryf\\u002fvcXClN3188I0VXclQiCIMiQNWtWYlCwSK0yqFQGlUqvUhlUar3qyqrRjcubEYBKuAUbQRAEQZCR6uP3du3b3WWVue5xucxJU10Xd+qKyWh5940dZ0+Vkhvcs\\u002fj6dHKoazwey2Awv\\u002fDkxrLSZnJb14oLG154atNXP9zNYrmVIMUx\\u002fPN1e3fvvExu6Ja0Rf38E38++dyS+YvTyG1dG8znGmg0KuViRf2Goxfza5s1RpMPnzs+JvSBeRPCJWLSlk\\u002f9uutgbpljNSbAZ\\u002fuzawntZP9mFmw7X1Da1Ko1mklNY6NDfn74JmLE\\u002fZfRf24dT33j4813JLU7iwz3teesq2vbyG39JhCwias0GhUABHw28VqCSqXaF1x2613FF48MNVw6Y4wkOFNaT24AmB+GagoNJ2hXIgiCIJ0FBHgFBHTZ7Wo2W5UqvUplUKsM339\\u002ftKy8hbzFcEChULy8uEqlntxwhW8vB3YhCIIgCDLs7N+d0znbLhLzUkeFBgWLBUIOk0nTaU1qtaG2uq2qolUu1xK3nD0vpVdFz81m64tPb8rLqSM3AAAAn88OCBJ5eXG4PJZeb1bItHW1MguhZAqXy5w5J4nwiB6w2IwvP95HzLbT6dS00eERURKxN49KpSoVuuLChsL8BlIasLqydf1Xhx572q3Jn7\\u002f6dH\\u002fnDDidTh2VEREe4evtw8dsmFyuqyhvKcitw7CO0X5WK7bu\\u002fV00OnXO\\u002fBTCQ7szmM810I4WVH62+yQFKP4iPobjLSrtf5eKjuRX\\u002fPn4LdH+TjMEzE2NEfM5Sq2hokVe0SIjNnX22t8Htl8oYNJpE2LDBBxWfm1zbZsSABKCJWOjQtIjyRWQ3H8Z\\u002fTeACXc+3ynrTSLy4toXtBqjc4sHMBgu\\u002fq5eVWW6ii8eGYKezZix+sAmK+Z0Ur47cWyKtz8xggx9aFciCIIgvcJk0v0kQvvdkyJx+y\\u002fA4SgxIejsuXJy9IpR6L5jBEEQBBnRMAzfsP4YMeLtzX\\u002fkyfnTZiR0VSS9vk5+6ULluTNlWZlVGIYvWdaL8eYAsO5\\u002fuzpn2zkc5vUrx0ydER+XEGQvue6AYXhVhTTrYtX5M+W5ObUz5yaz2QziBt2rqpBWVUjty0wmfdWaSTfdMsFeZ4aosUHx9WcHLpx1+lG097\\u002fLq9dO8ZX0MP7g5LHindsvESN0Bu2W2ybfePMEHp\\u002f8RCqlfuNvp3dsyXSkwnEM\\u002f\\u002fi9XaFhPnEJgc7bujCYzzUIPtt9cnF6wrPXz\\u002fARcAHgclXjk7\\u002f+J9Pov9p75tM7nabfWJyRsDgjAQC2ns9\\u002fY\\u002fNBYhPJ+bLa7RcKWHT6pidujQ30BQCrDXv+jz0HcsskQv7zy2eSH9Cbl9F\\u002f7aO8BwKti0+sHfPKzRqGTmP++49Gc\\u002fnULoOuXcUXjwxB4\\u002f1C\\u002fpp\\u002f6\\u002fSgSAGTxaDSEsSSDyYvem3cHPJ2yJCHdiWCIAhybVq6dDQ5dAWXy7xuae8uoREEQRAEGV4uX6wijlhnMunrvrxt+qzErrLtABAS6r3shrHvfXzrXzsef+HVZTGE6RJ7dPRQwdFDBaTg9FmJv\\u002f\\u002fzyL0PzU5ICiZl2wGASqVEx\\u002frfdOvEdV\\u002fetmnbo7ffNY20gZsEQs4X6++8457pnbPtABAULH7ng1XTZyUSg1YrtmtHFjHSmVym\\u002feyjPcQIn8\\u002f+cv2dd9wzvXMGHAC8RNyHHpv30Re3sQjdBhaL7YN3dhIH8rs0mM81OJJC\\u002fP+3eqE9zQ0A6ZFBD8ydAADnyshdMu47nF8BAHPSYuzZdgCg06j3zh0PAKdLqm3O4yztBuJldMXFSHBPMZld1EZ3MF2pnM5h9+KGFAfHwwfIgL54ZDga6xfy29xV5CgyDKFdiSAIglyDJk2KuW7p6P92ZZPiHA7zjddXeHvzSHEEQRAEQUaSyiujv+0mTY0NDXe3hobYmzdnQS+Kk1gsth++OUIKrrhp3EOPzXdz1pjezmLqQKFS3v3o5ujY7m5hp1ApTz6\\u002fOOtiFbFqxaXMqjvvm0HYiuy3n06oVR1T6FGolLc\\u002fXNVjJ0Ta6LBX31r5ynN\\u002fOyK11W3b\\u002f8lctXoiYSuywXyuwbFyfAppxqCkEH8A0BpNJquVRe9Ldlqq0gJAiLdTZUj7KobhbRq9vxef2AQD8zK6MoAj3FWEg6MzuUJnXyDVW3eTWtPdP95\\u002fA\\u002friEQRBEARBEGSQPfnkwldeWZaeHi4QsGk0qkQiWLJk9I8\\u002f3D12bCR5UwRBEARBRha5zKkgu3\\u002fXE9j038G9ua1SNTGSOjrsoUfnuZlt74+lyzISk4PJ0U74fPb8RU5zipaXNXczGFyrMR46kE+MLFwyKiUtlBjpyoTJMdNmJhAjmzeeNXc9zHcwn2vQOAahO\\u002fCujGC22Tqqz\\u002feKmMcBAJmmPUNr16rWAQCFAnxXI6QH4mV0ZQAT7hqtUdH11ExV1a32hcgICTFOp7dXWu\\u002fmQG+Rqo1GCznqUX178QiCIAiCIAgyZM2elfjxulv\\u002f3fHEwQPP\\u002ff3XI08\\u002ftTAwUETeCEEQBEGQEcdRG9muqVFJXPWsvbtyiKs0GvXZl67rpnaNp1AocMttk8nRLqSPjSCuWi225q7fkz3\\u002fZZsIeUgKlbL27umE9h7cea\\u002fT2HmVUn\\u002fqeAkxQjSYzzVoXKa\\u002f+2l6UiQA7M8pa1ZqHMHfT2QBwNioEB7LxTMOxMvoygAm3AHgYlYVOQQAANU1bS1XOruSEpwmjXWMGZfJtF3l3M9nVpBDA6APLx5BEARBEARBEARBEARBhpTgYG\\u002fi6rnTZTXVbcSIp8hl2uLCBmJk4pTYwCARMTJAklND\\u002ffyF5GgXIqP8SBENocIMyZmTTjnrlNSQHmdYJQqL8I2Kdnq640cKiatEg\\u002flcg4YyAHc3zEyKnpUSrTWaVnz024sb9\\u002f1v25FbPtu45VyekMN+aeVs8tYAMDAvoysDm3DfvLVjhlyi3zaesS8kJwYHOQ+rCQ32ZjBoAGCx2k6eKSU22ZnM1r+2XCBHB0AfXjyCIAiCIAiCIAiCIAiCDCnpYyOI85RaLLbnHvvz4vlKwiaecfliNSmycOkoUmSAZPSmSl7nGtHaLopXWy22spJmYmTytHjiqjsmT4sjrmZfqnaZchzM5xruKBT4ZO3SMVHBWqP5YE7ZP+fyFFrDjRNT\\u002f3l6TUyAu\\u002fMTDJyBTbiXVbS89d5ONaGPCMPwn349eeR4kX31tlsmOZrsGAzauDHtn5AvvztcWt5CbG1uUT3\\u002fyj+NTUpicID04cUPNYPXcYMgCIIgCIIgCIIgCIIMSRI\\u002fIam6t1yuffHpTc8+9selC5W45\\u002fKxpSVNxFUKBVLTwoiRgRMVQx603g0Wm0GKWKyuy2yUlTaTyqD36onsSPO46vXm2hoXdxgM5nONAOv+O3GpsuG91QsvfvDo5Q8f3\\u002f\\u002fKPa\\u002ffNDdI7O5dDgPKkxOwkqSlhLRI1cdOFp+7UJGWGuonERgMlpy8ujZZe22dBXNTJk2Idn4QAMDa1ZMvXKy0WjGFQvfgo7+mpoSEhnhbrVh9o6KouNFmw+LjAny8+WfOlZMf6Tl9fvFDCo0ysB0qCIIgCIIgCIIgCIIgyND30GPzsi5Wa9RO47izs2qys2pCw3yWLs+YsyDFy4tLbO2DuhoZcTUo2JvHZxEjAyc0bEDGNVdVSEmRiMheT+gY3ukhNVVtnf+dwXyuEWDr+TwqhRLs7WW1YXTa0EqBDmDCncNhrvvfzS+\\u002fua22TnbhIvkulQVzU557chEpaJcQF\\u002fjiM0s++GSv2WzFcDwnry4nr87Rmhgf+PZrK\\u002ffszx3QhHufX\\u002fyQQqMOraMNQRAEQRAEQRAEQRAEGXw+voL3Pr7l1ec3K+Q6UlNdrezbLw7+8M3hiVPiFi4ZNW5iNLH+TK+0trbPemg3QElwl\\u002fidqsR4BKm2O4VK8fbhEyPukPiRh123Xpkekmgwn2sIOlpQcaGsTmM0a42maqkCABrk6od\\u002f3CFgM\\u002fkcFp\\u002fNunf2OAGno\\u002f9mwaj4fzML1n71tyPCZtKDxMKp8RH3zBnnze9v71F\\u002fDGDCXa02hIZ4\\u002f\\u002fj1nf\\u002ftzTl6oriuTqY3mMUiXkpS8JKFaWPSI8gPIJgzMykpIXjHf1mXsqubmlUmk0XkxY0I950zM2n+nGQajRoeOrCf2P68+KGDRaWRQwBKk\\u002fFkU9XZ5toyZVudVqWxmIxWC5vOEDLZEQJRvEgyJTB8cmA4jz54U\\u002fciI4DBajnTXHu6qbpY0VqlUajNRvtxJWCwwq8cV1MDI3gM8nHFQN1CADYcz25rPNlYXayQVqrlbQadzmqxYjYWjS5gsgK4giihd6pPwJTA8HjRgHdKW3Esu7XxQkt9oUJao1FIDVqN2WSyWdl0hpjFEbM44QLx5ICwyYHhEQIx+cEIgiAIgiAIgiDIUBWfGLT+l\\u002fu++nT\\u002fiaPt1ZKJrFbs1PHiU8eLJX7C61eOWbo8g8\\u002fvdQpbrXIaQc8TDNLwdgDgcMkJB48g3RPA4fTlWdhsBoVKwQm11NUqPaG93WA+1xB0sqj6n7O5xIjBbDlZVOVYvXlSmiPhXtkixzCMSqUADlyW\\u002fY3CDWZrZYu8skV+ILds6zO3CTm9PoA9ZQAT7vbiR0wm\\u002fYZlY25YNobc3JPAAK+H7ptFjl4xY1r8sX3Pk4Irrs9YcX0GKQgAG39+gBwCmDQhuvO\\u002f4NDPFz9EMGlOCfdCuXR9wfndNcVWDCPGAUBnMess5iad+mxz7S\\u002fFl3h05rKopIdTJobwvUhb9sr2yoInT+0iRwH2LL0rybvXVaiICuXSxbt+JkcB1k1ZfGN0Kjnak7uPbDlSX0GOuqF6bZeH0LWjRa\\u002f9ruDclop8jdlEarIfV816zfmWut9Ksjh0xrLIpAeSx0cKO6ZH92zXztDZlZ0P\\u002fniRZP\\u002f1dxMjACA36n8rydpYmiM1aElNAKC3WvRWS4tem9PWtL2yAAAihd63xY1eHTeaQyfXm+u\\u002fGo3yl+KLOyoLFSan73g7+96s16ryZM27ql38OOvelMDwP+fdQo4iCIIgCIIgCIIgg0jszXv17ZX5uXW\\u002f\\u002fHA853INuRkAAFql6p++O7rpt9Mrbhq\\u002fas0kbm8S2WaTUwlyHm\\u002fwMp5M5oAkOXVap1xH35LgAMDhMPW6jn\\u002fK5PxG2Q3mcw0CLouR9\\u002fGT5CgAAMQE+HRueu3GOa\\u002fdOIcUdKm8Wbb68010GvWru5dNigt31JPBcShvbnvkpx1NCs1\\u002fF4vWTEuH3r8MjxiQY9HOg1MuDL5h\\u002feLt6BQq48oId53V\\u002fN6lYxtLszH3\\u002fjCd1byxNHtLRd4DyRMeHzWFjmrBI65gOL6+4PyXuWf0Vgu5zRWD1fJXWc6Wiry7E8c+M3q6vUOIQ2cwaTSzzfXkJCNJhUpmsllZtI6z7u8llz+6fFzdqaOiG1Vq+dsXj\\u002fxQmPnWhHnzQ2PJzX2ltZjezzr+V2mOFSf3xiEIgiAIgiAIgiAjTEpa6LovbysubNi2+cLJY8VWq4srQb3e\\u002fOevp\\u002fbuyn78mUWTp8WRm7tAyjvRaH0sTTN02GxObw7uXmKtM+KQcwBgMFwUpRjM5xrWfj+RZTBb\\u002fm\\u002fh5GmJkcQ4hQKxgb4zk6I2nc5pVLRPw3lVoETqiMVhtA+ArdEoV+z5\\u002fY+Sy25m2x3MNtuXuWdu2vdnq4Fc3gtBVGbjbQf\\u002f\\u002fiDruJvZdgcrhn1fcGHl3t8bdO1FxHxYV7Ou1qCx4liJsn1mcL3V8vDxHa+eP9CrbLtDs15z\\u002f9Ft\\u002f7t0tHcf6S4UyFvm79zwR8lllG1HEARBEARBEAS5diQkBb\\u002f0xopN2x+796HZIaEdd6ITyWXa11\\u002f855cfjpMbukDK7ZKGbA9HLLbT\\u002feVGg5m46iYcB6PR6YEuK84P5nMNay1KDQB4cV3\\u002fXTVtSgCQCHnkhkGEEu4jFpfOAIA6rerm\\u002fRtLr6T5+uBya+NN+\\u002f5svJIbRRAAkBn1N+\\u002ffeKbZ9d1n7siXt9y4789qjQIAvNnXRMIdAArlLQBgslnvPrJlT00JubmXvi+48FbmIXK0lzKl9egDjiAIgiAIgiAIcs0SiXk3r5n086aHPvnq9llzk2lXqnMQ\\u002ffnrqZ3bLpKjrpDKvusIhU2GKS8vDnHVYLT0cjgrAIDRaCY9ymUSfDCfa1iLC5IAwMZT2fbcuoNUpf3f9qNnSmpYdPr8NI9VBeiDASwpg1xdPDpTazHdfvDvZn1\\u002f76Go1ijuOPTP9sW38RmDN9kFMmQZbda7j2wpVrSSG3qpSadec+Cv\\u002f5bc4cu5mr2Og6lQLgWAp0\\u002fvPtdcS27rk5+LLiV7+\\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\\u002fl7kfwLxqNTRYamjw+57ePbmjed2\\u002fZtltTiVfv3uy0OTpsZJ\\u002fITEYGc+vvyGerljtalR2dE2PJH+ZBzD21rVPb4PJC3NKlJEKHTKrdsN5nMNa3fNGns4r7xKKl\\u002f63s\\u002fhviIxn4vheKta26TQAACbSX\\u002f3lgVB3r173zwLJdxHLCqF8tyZvfYRxEQcOmNyQNho3yAfNteHzWXR6CqzsUotP9tce1Fab+ui76xM1fbkqV0\\u002fzLqB3DAifDJlaYtBozAa5CaD0mSQmwxyo96+oDAZ5EZ9q0FntF2dKSaGoJfP7c9payJHr6BRKOP8QycFhEUJvYUMlhXH1WZjrVaZ3dp0vqWWlNtt0KkfOLbd9THXJ0N8VxYqpJvLc3dVF5MbAPy5\\u002fMkB4QliSQjfi0dnWjCbwmSo16rONtdebmvsJsH9ZubhmcFRvuxed1pgOP7YiZ2dp7oFADaNfkfCmHuTxklc9YVoLabfSy5\\u002fX3DB5dyqYhZnrF9IuEAUIRCHC0RhAnEwXzhS54FYt27vnr055CgAADCZ9K1bHuXxBjzjU13ddvc9P5KjV9x++5S77pxGjvZSW5vmQmZl5oWq3Lw6haLLCmNWq81qtRkMZqVSDyAjNvn48CdOiJ42LX7cuEiKO9ffvfHgQ7+UljaTglGRkh9\\u002fvIcUdCgoaNjx76WzZ8v1etd3aFqtJr3e1NamKS5uOnGiBADodGpaWtiihWnTp8cPZgFEDMPPnC3b9V\\u002f2xUtVmHMdRjuNxqjRGJualZeyqgEuUSiQnBxy\\u002ffXpM2ck0DvljLh9nXOpD4b4YTOSGAzmJUs\\u002fIUd7b+e\\u002fT5DGo10tVit27nz5yRMl5y9UqtUuvmjsTCaryWRVgt6+WlDQ4GiiUCAqyi89PTx9dPioUaH9Sb5fyKx84YXNpODbb90wZYrTgCmDwfzNt0f27MkhFlpVKvVKpb6iQvrff5dDQryffGJBeno44UFOcBy2bb\\u002f466+ntFojMa5Q6BQKXX5+\\u002feZ\\u002fLkRH+z3yyNzRo8KIGyCATpUePVVeI8f8EHzne8VqtZ05U37gYP6lS9Umk+uhM\\u002fYXr9ebmpqVxDNk32RkRKz76BZytB9wHPLy6g4eKjhxolijcToGHAwGs8FgVih0VVUdI70iInxnzEiYPz9liPR5jFQSP+EjT8y\\u002ffuWYd1\\u002ffXlHW4ohbLLbt\\u002f2Te\\u002f8gcwrYuhIT55GZ3jDCrqWo1m60DNJ3p4AiPlJAiNVVtvU2C11aTK0\\u002fExLoYlDaYzzWsiXmcv59cs+l09rGCyiqpvE6uolOpXlz2uOiQCbFhKyekXN16MoAS7iNYuUpWrnL6WRDC93ohY+a80BjitI0OT4yCeq3qi9wz\\u002f5TnuvihCnCwrnxLRV6fx9IOZSIWW8Tq7iLz3YtHfyi8QI5ekw7WlW2tyCdHr7gxOvXJ0VODea6\\u002fDHRW89aK\\u002fC9yzrQZO37XZkrrCZv01xDflRel9Rc7\\u002fb3j\\u002fUIeTZs8JTCC2sWP9Wa95pu8c3+WXnbZH6Yxm77JO\\u002ffauB5+9HS2uTw3V0ZOUwJAAFfw4+wbUrz9yQ1X8Bmsh1ImLotMuvfoVvuYfSIbjr07cb4fh0+Kj0iLFqV1lXA3m63HjhUtWTKa3OBp+w\\u002fkkUNXUCiwcEHfz9hKpf7o0aKjx4oKCupdHXq9IJNpd+\\u002fJ2b0nJyDA64Ybxi27Pr1zgsOzautkVqut87MUFzd99fXBwsJGUrxHViuWlVWdlVX93Xr+HWunLl48ahAGiJ09V\\u002f79+qM1tU5f5d3DccjPr8\\u002fPr\\u002f\\u002fhh2P33zdrzpwkYqtg4O8kHdaHDXLV6XSm7dsvbdt+UalsT6P3DY5DRYW0okK6ZUsmlUpJSQlZMD91xowET92nUlEpJSYfZTLt089squ32o1pfL3\\u002fm2U133jHt9tunkNsATCbL669vv5BZSW5wVlEhffrpjXfdOf222yaT265h6FQ5CKfKEXPMD7t3vjOr1bZz5+W\\u002f\\u002fj7f1qYhtw0TOI4fP17y+x+niWl091VXt1VXn\\u002fr999MzpifcdtvkyE6pScSDQsN81n1528N3\\u002f0Qcon7hbHmPCffoWKdrSZsNKylqSh01GPesDJCoGD8mk242dwzdKy9rHjshirBJz8oJXRcAIBLzgkLExIjdYD7XcMdnM++bM\\u002f6+OePJDUODi8QrMiLdmTDmhTEz2a5S7Q4hfK8PJy+aFxrz+Mn\\u002fXFaZ+N\\u002fFo4vC4nkMz1yuIMOO2WZ77YLrouFsGv2zadctDOtu4nIenbk2PuP6iKQnT+062lBBbr72MGm0N8bNXR03mtzgLIAreGvCvJnBUY+e2KmzuhiQ+3dZ7jPp0+3TNrgJw\\u002fHv8s+TowBMGu3nOTcmiv3IDZ0E8YS\\u002fzV21cOfPxO4TAFCbTW9eOPz1jGXE4EiVnBwcGupdV9dxvyTR\\u002fgP5A51wx3H80KECcvSKUWlhgYEictQN2dk1O\\u002f+7fOpUmdXqdA9p\\u002fzU3q77++tCOHZeefWZxWtoA\\u002fua2WrHq6raYmI7f+lar7etvDu\\u002fcmdX\\u002fC+xPPt23\\u002f0DeSy9e17e31x1arfHjT\\u002fYdP+7ibhg3tbZq3v3fzt17cl55+Xpv7\\u002fbBHQN6J+kIOGyQqwjHYd++3PXfH+1mSHvfYBiem1uXm1v3xZcHp02NW7FiTGJiEHmjXqqo6Ohs1mqNTz75Z32DgtDuGo7Dz7+cZDLpN988gRg3m63PPb85L6+OGOwKjsOGn09QqJQ1qyeR26496FRJNKCnyhFwzA\\u002fTd56koKBh3bo9vepeGmrKylvWfbSnrNwpFdgHGIYfPVZ0\\u002fETxiuVj7rpruqf6U5HO+Hz26jumfvzeLkekvk6O49DFULF2ozvd3XJwb+6wTrgzmfTU0WGXLnR0E545WXpLL\\u002fsCz5woIa4mpQQTVx0G87mQAeX52\\u002fwnTYg+tu\\u002f5Y\\u002fue\\u002f+mbu8htQ96wfvHduC0+\\u002fY3xc7vPtjvMC41dP2ulyxIQcpPhKo4ORq6630qymlzNrkmlUL6esbz7bLuDiMX+YfbKuSEx5IZrDJNG+2XOTT1m2x1mh0R\\u002fNm0pOQoAADqr+WBdGTnarQstdZ3rTQHAI6mT3Mm22\\u002fmyeW+Mn0uOAuypKa7VKMnREWrhwjRy6Ir8\\u002fPoGNy5H++PSpWqZTEuOXtHNa3MJx+HI0aIHHvzlqac3HTtW7PErUoeGBsVTT2\\u002fc\\u002fM\\u002fAfptUVHZkB+Ry3RNPbvz33\\u002f5m2x0KChoeevjXPoyUd0dNrey++3\\u002fuTwrJITu75v4HNuTnt99YMxBZpBF22CBXhVZrfPW1rR+t2+PxbDuRyWQ5dLignJA37LOKio5U0Xvv73In8+jww4\\u002fHioqcTh2ffbbfzcyjw4YNJ3JyPDMZzPCFTpUuDdCpcvge88P9nSfatu3iE0\\u002f+OXyz7TiO\\u002f\\u002fLLyYcf\\u002frX\\u002f2XYHDMO3brv4wIM\\u002fl3vu30Q6i41zKkVis2HEwdcuhUX4Bod4EyNHDxWoVQP4LT8IZsxOJK4WFza0NJHrpHejurK1xrnMy9QZCcRVosF8LmTguEiqIiPM5IDwt8bPI0e7NS0w4vFRLu7+A4Bfi7NMV68ENnIV2XD8p8JMchQAAB5InjAnJJoc7RqdQv182nVdVZ65Rrw\\u002fadHkAHLPf\\u002ffmhcYuj3K669nheEMP9+SSHHCVoGfSaLfHpZOj3VoUHt95P+IAm8pcF1oZeebPS+mmtMiBg13WX\\u002fKIburJcLnMGTPiydFuUSjw11\\u002fnyspcFBryOAzDv\\u002fvuyKZN58gNnuMYjqdSGZ5+emNhYX+rl5Ko1YZnnt3UuXx8P5WUND\\u002f++B8tLb34Sd09uVz37HN\\u002fX75cAwDdTBLYZyPssEEGn0Khe\\u002fyJP8+ccfGt5HEcDnOuc\\u002fGQvmlqUtpngDhypPDs2XJyc7cwDP\\u002f4472Ozr8jR4v27e\\u002fyTN4VHMe\\u002f\\u002fPIgsXb2tQadKrsxEKfK4XvMD\\u002fd33uHnX05+9fUhmw0jNwwTer35lVe3\\u002fvb76YH4ExoaFP\\u002f36O8nTzqN50U8SKczEVc5HCaL1fNQzgVLRhFXjUbLl5\\u002fsI0aGnRmzk3j8jllhcBx++fE4ob0Hv\\u002fzgtLHYmzez698kg\\u002flcyMBBCfcRjgLw2rg5XdWG7saDKRMiBGJyFEBhMric8hEZ8Q7VlTXpXdQKDOAKuuqe6QaPwXx57Gxy9JoxLzR2ZVQyOeqG+5Oc7sl16G0p\\u002fM6l5AFgon+YN7t3F5k0CmVeqNMkWnanmqrJoRHKx4c\\u002ffnyXBfUOHszv\\u002fbWhu\\u002fR686lTXaaoZs1MZLF6UWXI7uZVrg+wAfLjT8d6e93uvsrKVgAwmawvvPj3AA0HMxotL7+ypZ\\u002fFponqGxTPv\\u002fC3xwf5mkyWl17+p7CwkctlDsR0VSPpsEEGmdFoee75v\\u002ftWxrcP5s5J4nhiPkwch6qqVovFtv77o+Q2N1RWtZ4+UwoAer35228Pk5vdU1nVeubMNfpBQKdKd3j2VDmsj\\u002flh\\u002fc7bbd128fffT5OjrnC5rJAQ78TEoPT08Li4gMBAkUcqy7NYdD+JgBx1m1ZrfOrpjR5\\u002fW4jMZuubb+04OMAjXa5ZF5z3XUCQiLjaleuWZxBTxgBw7HDhlr\\u002fOEyPDC5fLXLnKqVb44QN5ly9VEyNdOXuq9LRzn9CSZRkMRpcfz8F8LmTgeP7HBNHu4pLD5ZWfLF1EbkAGy9SgyARxXyYSYVBp9yePf+ncfnIDwK7q4huiU8hRZKTbWVVEDgEAwH1J49ysVkSyMCwuUuhdpXZd\\u002f3oEo1IoL2TMIEfdk+Ttl+Tt13mq0nqtSmcxuzm\\u002fAg5QonS6xcwu3TeIHHJDuiTol+JLpGCRXGqwWji9KSs\\u002ffC1ckHbuXAU5CgAAzc2q3NzaUaPCyA2ecOJEscnkYrINu0WLeldPxm7mzISfNhxvbnZ3zCCLxfDx4QmFHBaLYbXatFqTVmuUy7VudjPgOHy0bs\\u002fvvz3A4zn9HPcI+\\u002f3v69cfKSnpYVwbn8\\u002f28xNyuUwqlaLRGNVqg0KhwzC3\\u002fgaZTPvpZ\\u002fvffGMFuaH3tFrjiy9udj+F5OPD9\\u002fXlc9hMi9Vm0JtbpGrS+CMik8n6+hvbvvv2TrGY58ExoXYj6bAZjlgsxttv3aBS61Uqg1ptUKkMKpVjWa\\u002fVmvowJnTQfPHFAWJt6O6x2QyJRMBmM9ksOoVKsR85Op2pmyOfZOl16eRQX1VUSKurW1tbnQYiCATsgAARADQ0yO3DgbuyffulqVPiNm8+37ksmFDICQkRA1CampQKhdMsKSS792QT57G8RqBTpZsfaI+fKofvMT\\u002fc3\\u002fnCwsbvvjtCjjqLjJRct3R0WlpoZKSE4jzSzmy2lpQ0ZV6s+u+\\u002fyyo3CnrweKygIHFwsCgoSBwcJA4KEgUHi318BL0fv9dOrzc\\u002f\\u002f8JmN+8IpFCAz2cLhRyhkEOnUdVqg0pt0GiM7oyLxzD8gw93C4WcCRN6ceP1NcVktLz\\u002f9s5FS0eNnRDdzR26JMWFDdv\\u002fcbrNffJUtwrJ8gXs2+6ctv6rQ8Tg+q8Oydo0d947g8V26zqxsUGhURvi+z35iqesWj3pwN5cR3UXHIc3Xvzng89WJyR1VyE9N7v23de3EyMBgaKbbp1IjHQ2mM+FDJC+pMncd7i8sllD\\u002fk5FBtPyyL7fObIiKvmdi0c6z556urna\\u002fdQeMjKYbbZjjS6KljCotJti+pLXAwAqhbIyKvnj7JPkhpFuZnBUtJcPOeq2MZLgzgl3HKBOq3Kzd01m1LksDBXl5VRoz00ub4Wx4liDTh3Tjz9zGJk8OdbLi9PVNcz+A\\u002fkDlHDff6DLUTxhYT5J3f4U6wqNRr3xhnFffe30y5jIy4uTlhaWlBgUFxcQFubj48MnbwGg05mKi5uys2v27suVy7u7cgYApVL\\u002f+x9nHnxgFrmh31Qqw48\\u002fHd\\u002fxbxa5AQAABAL2jBkJ48dHpSSHdK4eoNeb8\\u002fLqLmVV79mTq9d3mZqxO3my5FJW9ZiMCHJDL332+YEei\\u002f5TqZSpU+Nmz0rKyAjn89mkVplMm19Qf\\u002fpU2anTpUYj+btbJtM++NAvWq2RFO+\\u002fkXTYDEdUKqWbDBSO4+3Jd7UhJ7t2w88nyFtcPXn59T1WlggN9Z46NS59dHhMjH\\u002fnj6qdRmOsrJRWVbdVVUpz8+pralz0KANAQkJgLGEi5X46eqyora0j8zhlSuytt0xKSmrPCJjN1j17c9evP2Iyufi2BYCcnNrGRuX2HU7d1ePGRa69fWpycvvZG8fh4sWqTz7d11Xm9+LFar3efK1NFYhOlVfrVDl8j\\u002flh\\u002fc7bbNgnn+ztJt0sEnGfemrhlMlxXSXEmUx6ampoamromtWTt27N3PDzia6GFHh5cdavv8tPQq4V2R84Du\\u002f+byepiH9ngYGiaVPjRo0KS00N6fyZtVhsBYUNOdm1J0+VVhJm6OkMw\\u002fC33\\u002fn3qy\\u002fXRkT4ktsQAAzHTx0vPnW82MdXMHFyzLiJ0eljIrhd9wxp1Iad2y9t\\u002fPU0sWI7nUGb7\\u002fawnhtWjT97qjQ322kChi1\\u002fnT9ysOCmWydOnBIbEuri8lOtMhQXNeZl1166UFlW2nzzmklDJ+HOZjOef\\u002fn6Zx\\u002f\\u002f0\\u002fGp1OvNTz78202rJ626dSJfQD56VSr9X7+f2bb5AvFzR6VSXnhtWY+nssF8LpcwDNfrTTqtidRRiuN4Xa2Mx2NxeSy2ex0n16wBTLjbcPxMTW2Ut4uPEDJoZgZ3WeugRxw6Y3ZIdOcCMmab7aK0fkY\\u002f\\u002fmVk2Lnc1qizuBi3MjUwQsjs8ku6R\\u002fPDYq\\u002fBhPvKqH7dIJLqE0AOAQBAq0HrZsJdaXJ9Gdm3XdnVo5Qm1wnokYdOp86dk7x120VyAwAAnDhR\\u002fPhj8\\u002fpQ3aV7LS2q3NwuZw9buDCVHHLbkiWjfvv9NGnwYHi478wZCRMnxsTFBXR1RefA47HGjIkYMybijjumHjxY8M23h7sZTggAu3Zlr719St9+CHZv48az5BCAQMBes2bysuszuqk+yeUyJ0yInjAheu3tUzZvvrBx09murk7tfvnlZD8T7idOlBw5UkiOOhs3LvKxR+cHB7vo4rLz8eHPmJ4wY3qCRmP8669z\\u002f2y5YLU6XaJ3HlfoKSPpsBlhKBSKSMS1p6p7nOJskP3xx2lyiCA4WPzo\\u002f80bNy6qx4NHIGCPGhXm6Npsa9NcvFh18WJV5sUqjabj++66pemO5f4jzt94xx1T71g7ldAITCZ9+bKMqEjJM89uIn0M7TAMf+rpjcSXd\\u002fdd02+7bTJhE6BQYNy4yK++vP2++ze4LF1ltdpycmonTYohN4xc6FR5FU+Vw\\u002fqYH77v\\u002fL79eZVdF92KipT8772b3EyRs1j01asnJSUHv\\u002fLKFpe3I6hUhi1bMh9+aA65oR9+\\u002f+N095VkIiMlq2+dNGtWYjcDrhkM2uhRYaNHha1dO\\u002fX0mdLffjvdzRSper353f\\u002ft\\u002fPabOzxSS2ekkrVpdu+8vHvnZQoFAgJFkdF+gUFiPp\\u002fF5bEwDDcazNIWdVVla2lxY+cfwGvumBoU0uUJloRCpbz+7o1PPPxrXY1TdUe5TLv+q0PrvzrkKxFI\\u002fIReXlwGk2YwmLUaY2ODYojPrZo6Ouz\\u002fnlrw+Ud7HRGrFdv02+l\\u002fNp4dlR4eEeUn9uYBgFymrayQ5l6u6fwe3nnfzOTUEFLQpUF7rnOnyw7uy7On13U6k15n0mlNBoOLEwUAmEzWu1d\\u002fZ1+mUilcHovHY9nz7\\u002fbl2+6cGoY6vQCgzwn3fwuKNmbnVsoVeovFl8tN8JOsSE5cGN8+vmZjdu7WvIKS1jaj1dqm08d8+KnjgSXPPE6jdhSO\\u002f7ew+I+s7OLWNgBI8fe7b\\u002fzY2TFOadzfs7LfPHQ06\\u002fGHaxXKj46fymlqxgCP9vZet2RhtE97Kj+nqXlrXsGFuoYGtRrH8QixaEVK0l1jM4iFyzEc35ybvzE7t0ah1Jmdjpvy5550LGfWNXxz7nx2Y7PJao0Qi25ITb5zbAatx2\\u002fgoSpK6O3Ty4rMJFMDIzon3AHgYmsDSrhfUy5JG8ghAACYFhRBDvVGnEjizebKjS5+Uo9UNAplRlAkOdobvmweOQQAABpXPSIuuRzeDgBcel+uCroqKOSyh2akWrQorauEu15vPnGydN7cZHJD\\u002fxw8WPD\\u002f7d11XBvn\\u002fwDwz8U9geDuDm2hTpUadVm7duu2bp11+p3bb+7+nVvn30llta1dvaXeUkqLu7skhLjn90cghCMJgQQI8LxfffWVfJ7LcblcLnefe+7zWLu1mUDAFi0c\\u002fEUdKpW8enWysVQoj8dYuDB+aXpSaKhd13JwSCTi0qVJkyYFv\\u002fLqPhvjlcnlqtMZRcuXTcA3DIHk5JDnn1thsfOaRSwWbevWOQkJAa+\\u002fccBGV\\u002feCgobKytawMC98g300Gt03357CR3vbunXObZt7JSZsYLNp9947b9782Jdf3mutn6Bzje3NBhkKAoE0K6sKH+2WnBzyxuvrBldv3cODnZ6elJ6epNXqLl+pOHGi4PLlCjKZmJYWi5\\u002fUGRYtSsBlHk2SkgI33jztd0tX\\u002fgCgtVVserxyxURc5tGEz2dtuz\\u002ft3fcO4hsAACC\\u002foH4QycdRCu0qzY3grnI0bvOjdM0bDLBr1xV8tBuLRXvjzZvszLabTJwQ9PTTy157bT++AQAA9uzJSl+SOOjjGZySkqZffjmPj5pZt27yA9vSiMSe7JBtGAazUqOmT4v49NOjh\\u002f7NwTd3q6ho\\u002fe23i3feORvfgPRhMEBTo6ipUYRvsGLhksRbbrf8zbWGw6V\\u002f\\u002fMXt\\u002f\\u002ff0ztLiJnwbQHubpL13oapRYcXqZL1O\\u002f8UnxwxmCW6tVn\\u002ftatW1q1aPbYzu2DpnQOtweP5WRXnL2dNF+Kgd9HqDVKKUml1MBYD0FRNQwt3I3r2bue+uZD156IhKp9s0IfHuKSmT\\u002fH2vNTTkNfdcZgx3d984IfGFtLkAEOru9nb6ItM\\u002fglm2\\u002fb2Mc08ePNypVG5MSrg5KaFNJrtv74EfruJrAQPAqfLKW\\u002f7czaCQt6RMWh0XK5DLfdg9Z8vbM7MOFBZH8N23pEy6LXmCQqt95\\u002fTZLy5eNpsBvJdx7sWjJyL57u8uXfT64gUBXA4APD9\\u002fzpG77zBN83dh8eYduxs6xesT4++eksKmUt85ffaR\\u002fQet5DRGgTh3R38sU7wsFyXIE1g9vEDGpFyBhR9IAEjxtLyF2AkDSHD3xkfHtGg3T7aVLuF2staj3FoavS9rpdUlGqv5RBvEVl5Fs\\u002fJXxqSwMK\\u002fISMt3HgDAsWP91EwYBBvznDY13P6EskVr16QkJgY+\\u002f9yKXTsfevCBBYM7IzXx8eG+21\\u002f3q4sXyvChIbBwQfx77948iJUzdWrYa6+ttX3x\\u002fZj1Cj\\u002f9OnAg23Zt2W33z7c\\u002fhWQSGeH9+We3+dk3sJXjxupmgwyRq1crrV01dHdnvvLymsFl282RSMRZqVGvvrJ2z1+PvPvOzU6\\u002f0wgAGAyq7aoRt9wyo9+\\u002fy+Mxtm1Lw0fNpKXFcTh0fBQAAGx08xx70K6yr+HfVY7ebX40rvmiooa6OiE+2u2hhxb4+vDwUTvMnROzYEEcPgoAAAaDwVoPkoHS6w0ff3zE2iAiGIY9\\u002f\\u002fzKhx9aaH+23YREIjz55NItWyxf9THaaWm0AARX339ASGTibXfNfubFVYP4yHhuzE+\\u002f3rLhlumDeK3LWrVu8tsfbHIfyGkFh0t\\u002f6Y11t28d8KWg4fxbiHNZ7plo29+FRW50+p7bNpn6quv0erVOZ5pgWlDAtKAAlVb78rGTnkzmzUkWutplNzRuz8yaGhjw44a1NBIJAJ6ak3rnrr3vZ5ybFRIc7dnresiLR098v37N9KBA41MDgPmu4rVFaQwyhU7uei8Pz5ye9t2Pu3LzH02dYYyotNrfrufEe3t91D18a5QHf9Mfu1qksgh+V4nhDoXixWMnpgT6\\u002f3rzTab39eTBwwcKi0+WVSyMHJUjbzheQDmMw6cQieYfrlHl+Bvocpyr6LTwiRMwLIrn6KXLaJ7n2cZ+rs2OJQnuVtOydrLWD11nsHADr0U8Cg0fAgCADuVgbt8TWnmVO83ymdJYtXRpkrWOTtnZNW1tEk9PNr5hsAoLG+qtV7B1pJ6MEY\\u002fH+PSTzfioA9zcmC+9tPqRR\\u002f+Hb+h2I6fWYDA4chrQrylTQp99dvmgD\\u002fRTkkM2bJhmo6PZlSsVttMH1uh0+p3WZwsACxfE33zzNHzUPh4e7Lfe2vDAAz\\u002f3rVPsdGNys0GGTpn1rNmK5RPZfSqTOoLJpJqKRDvXokXxbm6WbzszYjAoc+ZEHz9u64LcunWTbV9dIJEIkyeHWiylUltr4fBsTEK7SmuGeVc5erf50bjmz54rxYe6eXiwF6QN\\u002fu7JTRunnzxpYfUCwMmThdvuT3N8J3zw4A0b+\\u002fmtW+c4ePfnHbfPKipszLxaiW8AAACVSvvrr+cffzwd3zC+0Wjkz76988CerPNnS1R27+6oNHLq7Kg77p7jH9BVYWIQSGTifQ8tWLpy4u4\\u002fLp86nm9tsAccJos6Ly3O\\u002fpLxw2zytLAffr9\\u002fx\\u002f8u\\u002frPvmsUyTSZ0OmXZqkm33D6Ta2U0mn4N599CnGgwCXcPJrNcILze2DQ5oOvglUgg0M26rttjd14BADyaOt2YbQcAOpn88Mzpd+3euzMn7+WFva6cL4mONGXboXe2HQD4jF5bEotCifH0vFRTqzcYjFVlWqUylVYb593T3TvWyxMAakUiU+RQUalcrbkzJdm84s2K2JgDhcWnK6tGacLdj2nrqrs9iBgWwOT2Ta\\u002fXSzs1eh2ZgCqjjQt6g6FaYiG758tgW+srbb8AFhcfGtNCOW740LBzpzHcqXRhnxrr+cLmDTDgXK3Fux+IGBbI4uGjY9qCtLivvz6p0eAvTwKAwWA4caLgFueNDm9juFQulz5jRiQ+6gLi4\\u002f1TUkKuXavGNwAAgEKhrqsTBgU5epHYGjc35gvPD6ZLjrk7bk\\u002f955\\u002fr1qoZ1tQKBALpILrPnztXaqMTFo\\u002fH+M9\\u002fFuOjAxEcxL\\u002frrtlff30K3zAajOxmgwwpG12VExN7jvld3KxZUfhQH1OnhNlOPi5ebKFnEk5sjK\\u002fF5GNra6dOp3dw5zYqoF2lDcO5q0TbvLmhXvPmdfNxli5NIpEGvxLCw72io31LSiwcw6vV2uzs6rlzY\\u002fANA6HV6iyOo2M0a1bU5lu7ekYOGobBs88uv\\u002fOu7eYDA5g7djz\\u002fnnvmOX7lYIyJjfePjffXanTFRY35uXX1dcLmRlFbq1guV6uUGpVKQyASmAwqg0nx9XMLi\\u002fCKjvWbNjPC9hUy+wUG8Z94bvmDjy2+llmZl1NXUdbc1CQSixQqlYZAwKg0Mp1O8fLm+Pq5hYZ7JSQFRsf69budp86OPn7+\\u002f\\u002fBRuxGJBEdezmLR7nkg7ba7Zl88V5qdVVVe2tzS3KmQqzECxmRQvX25EVE+E5NDZsyKdHxw0SH9W5u3zNps864RZHAGk3B\\u002fbNaMLbuaNv2xa3pQ4PrE+PToSFPS3H6FLa0AkODTq5pEgo8XAOSaVafpipvlyvtSarW7c\\u002fMzKquqhCKxUinXaIw9sg0GA2AYAHgwGSQCoaZDZHqJ8bEXs+f6vLEkzgP7\\u002fjZFTNplcnxolPCmD\\u002fjMvy9\\u002floWEu95gaFfKfRlO67CJuLIOlUKjt5BG9Hf4ig4A+DHH11bk+GUwp0jy8M1owHcJudhk9bDehkvNFl4V6+Zlrbb7WMVm02alRp3OsFz87uixPGcl3DUaXYaVvwIACxcm9HtgOlLW3zTF2kkpANTUtDtyUmrbAw+kcbmO3nLBYFAWLYr\\u002f++\\u002fr+IZu5eUtg0i4Hzp0Ax8yc+eW2Uym5SpS9lu7ZvKev7Ja28T4htFgBDcbZEgprPfPMg7x6vqIREJCfAA+2kdcnB8+ZCYiwtt2VQqjoCDLNxQaDCAQSu2Zw2iHdpW2Dc+uEm3zfQ3dmtdq9RUVrfhotySHL0wmJgZYTLgDQH5+vYMJ96NH86x9lUgkgrPGZXVzYy5fNnHHzl6VhE1UKu2Ro7kb1k\\u002fFNyAAJDIxISkwIcnRrWhwaDRy6pzo1DnR+IZRi0Yjpy2KT1vk0E0bdhrOv4U4bjCn5RP9fI\\u002ffs2VLyqSCltanDh2Z9dX2765k6a3U57JGqlaTCAQWpde1Mh6dTsAwiUplHgQAd7rV82SxSrX21z9eO3GaSCDcNXnSO0sX\\u002f7B+bWLvPD6dTN44ITGzrv6tU2cu1tQeLil96tARGom0eVLPMCZilRIAtk5JeWrOLNy\\u002ftfGxPfMaVbhUJ1zRdadaXvmC8TTQ5TjXrpThQwAA4EG3dUupnbhWNrCxytoXapgtDrTQCbqss\\u002f1icw0+alONRGSxItD8gFF5V5CD0tOt3vBYWysotjRS0CBcvFRmrS8PACx11ZsuAWDChCAbXdJah2zEpNBQzwVplmuVDtSCBbaObiur2vCh\\u002fkgkyhvW+6\\u002fxeAzHCwQBAIlEuGn9ZHx0lBipzQYZahSK1Yuyos7RcYQZHMSnUq2+CxNfXx6DYbVvYGJi\\u002f+lLAPDytppe7BBaPkgbS9Cusl\\u002fDs6tE23xfQ7fmW1o6tVoLHZ4AAMMgNtYXHx2guNiuWgV9FTl8yPrPwRv4ULf09CQfH6fd37xy1UQbRXsyMorxIQRBkGFk9efBNi8W66UF8y49dN\\u002f7y5bwmYz3z5x7N+McfiKb2FSqVq\\u002fH5dZFCoXeYGBTB9BD4Zdr18vaBVunpHy3bvXtyRMXRYbPCA4k9alv82La3AUR4T9nZW\\u002fdve\\u002f1ExmBPN5ft99iXimeRaECwJKoiG3Tp+D+pUdbyEyNCizyANakNVwr5Z7FaqsZH2SM6VTjr4EZcaxsGwPCtTIE6FjFJFs9AxlOy4JjqJZ6oL+ddbrvmA02vJl1su\\u002fVVgKGrQvr\\u002f2bhsWfy5BAbXa5sDHM6IDYG54yK8glzbBCwIUWjkSMje10ONycUWq0V4KC1a1JsnIwNSFSkj43z6pYWqyUyrLlwsUyn0+Oj3RYvTrSRlByQBWnxBIJzVsIwG6nNBhlqNm46uXF9YJd+R0pQsL0dVz09rN7MZ2PAbXN8d6tdHKQyywdpYwnaVfZreHaVaJvva+jWfJuVHuIAQKdTGQxHT6D4HlbvyXNksQGgurq9tLQZH+22aaNz7vg08vXhJVnvpl1c3NjR4UKXZxAEGW+snjfag0YirUuIO7BlcwCXszsXnwIwFkO3NjL1BF8f6C7kYmJ8aiwsY6fydiEALDXLiev0+iohvt50Zn1DRkXlf1cuK37qP5ceuu\\u002fbdatieo\\u002fLmujrDQBXauvMg6MdxRk11i1m5QBgQFk5ZFRT6SyPasJwuIA7WN\\u002fAxioXGfmAR6VtjpqIjwLkC1uevHDInm+3AeD962eO15XjGwCWBce4Qqn64YdhmI2ypCdPFVrrqWQ\\u002fkUiemYmvBWSy1HoXexfh7W21T5OdoycNFIVCWrDAOd3bAYBKJdm4pCEUDPik7to1CzeImMyf59AN3ebc3ZkJCXZ1KnRBw7\\u002fZIMPAx5eHD3U7fCTXxn08rsPPz95fOk8vq9diQ4J7nY9Yw2TSrF03lEldKPk4RNCu0h7DsKtE27xFQ7TmbVxXcEpdcjbL6kzEYof2wBaL7xuFhnr6+fHwUcfExVotYWQwwPVRcgUXQZAxacAJdwNAfWevy61EDCNgBOPwpOZIBIInk1nV0WExd7MxKYGAYZ9fvKzQdP0OKTTaLy9ewQA2JFpNWPTlzWYBQJO453atb69cFSnxPxIXq2t1BkOIG69vZ0yjVbExLArlx6zsOlGvHmrtMrlKO\\u002fhfypFF7tPTfxDIVnrzqS0V9UbGJItfYXBS7tgpMxlFiJjlL9Tweyhxhpul+jb\\u002fVBetP\\u002fJbZms9vsFMRafg3lN7vsq7jG8AYJIp\\u002fzd5Pj46bqSnJ\\u002fX5MewikSgvXbJwfWJATp4qtNbLj0wmpjmpcMrQ4Vg\\u002fRXTkpNSGSZOCnTXQk5GNm9wlUvyxR7\\u002fy8xvwoW48HiMqytEbxs1NmhSMD40Sw7\\u002fZIMNggvU+iUKh7N33Djp+hXKo2ejDi8NiWe2LamdpBQwDCsVyLweLg3WPMWhXaY9h2FWibd6iIVrzarXVt0mnW14zA2JjWEWFQmUlZWKXzKtWu4bMmBGBDzksJsbWHqDYSp16BEGQYTDgvqV6vX7etz8k+XrHe3t7sZhSlfpMZXWtSPRo6gz8pAArYqN\\u002fysq+9c\\u002fdc0JDdHq9UKF4Y3HXEBlx3l5PzE798Oz51b\\u002f8Njcs1GAwnKmqrhJ2\\u002fGfWjHibQ6TirImL+SUr+9UTpyqFHTQS6XJtXVZ9w5QA\\u002f6v1vY7M1ifG78zJW\\u002fPrH8anFCIxgMvZNCHxrikpxtwIj077aEX6IwcOrfj5txWx0T5slkAurxAIM2vrT963NYBr9QTblTnwQ9nD2s+tlZwSMgYZrGxKTtkG+l6rQ4YHn8Z4Z0b6tox9+AaAXEHzzUd+j3P3SvUJSfLw4dMYLDJVqdWI1MqijtbLzbWXm2stbxMAH8xcNp6HU\\u002fbz4yUmBubmWr5Z6uix\\u002fNmzo\\u002fHRgbBRT2b2rCin9HgaUizrS2jQW9umHDJ5cig+5BgW0+pbUKsHdl4tEEhtVKGZkBTk3L1j4qjttjn8mw0yDBISAhgMitzK0KmXLpU\\u002f\\u002fczO559b4WW9n+yIc7de8gLHWsETCoVk\\u002fwixFApRpdLgowBaK70ixgy0q7TTMOwq0TZv0RCteQrZap8ka3vOAZFa7yVAIhEH\\u002fbUSixVlZb1qGJhz+lEZAEREWC3pAwA2FgZBEGSoWf4ttIFAINw7dfL56poDhUUqjdaNQQ9zd\\u002f8sdfmymCj8pABPzkmlkoiHiku\\u002fvpzJIJOjPHvVfds2fUo43\\u002f37zKwdObkAWJyX55OrUwdaMD3O2+u7m9Z8cv7it1cyyQRiSoDfzs0b81tazBPutSLRIwcOujPomyYmGmu1y9Tqy7V1b58+SyQQtqRMMk62ICJ83x23fnvl6umKSqFcwaPTgni8x2enejDtPSxwNRq95Y6QA6Kx0pPdKfVqBk1vJQWMDAWqlc9aY3DCBoaMoPSgqCcmzv74huUROAqFrYXCVnzUplemLFwW7FBCeQxYujTJWsI9M7NCJJLbf6qJU13dXlbWjI92szFkq+sw1pobTjZuNB4cG+fVA+1zV2VzkNXoGB98yDFhYQPozeBShn+zQYYBhUJavmzi7r8y8Q3dcnJqt9y5ff1Nk2++eZprXk3kcu3dmVOtJB9tFLLvy9oAEtZuexoz0K7STsOwq0TbvEVDtOZtHG\\u002fYyJXbz8ZMmEyrNyj0q6CgwVpJYQAIDrJ3GAD72f6BaGoU4UMIgiDDxfJvoQ0YwLPzZj8Ls\\u002fENltBIpKfmzHpqzix8Q7dFkeGLIsPxUTO3J0+8PXkiPtrb3LCQuWEh5pFoT4+bEuJNT\\u002f\\u002fv6Ik2mfz0fVsZZjem6fT6GV99d6SkzJRwB4BoT4+PVyw1PR3trJXeHhC51kLPAhjp0ttqZ7w1xE4UK5+1UzYwa\\u002fVqkOHxaNJMMoHw4fWzOusHx\\u002fZwo9Lfm7l0ceDArpiOSXPnxHz++XGLnY+0Wv2pU4Xr1k3GN9jnqPVhV708OSkpIfjouIdhEB7u5NSJjR5nNs4wLaqtFeBDZsLDbPXYGgQej8HjMUQiOb4BQUbITesnH\\u002fg728atISqV5vc\\u002fLu3dd23p0qS1a1L8\\u002fd3wU4wo+3NS1vKGbPYAko\\u002fWBn8e4I5n9EG7SteBtvnh5GG9gI9crnakA4dRXX0HPtTN1\\u002foYG\\u002f2qqLDaWYfJpLq52XuThP2YTBqGYdaOwdoFEr3eMEoHQ0YQZLSz\\u002fFs4xlxvaIr39jLPtgMAkUAgEYgSldXRSMYAsdrqhWv7WZsJz1L152GjGLWF9UcjDsXy4XWnyvK2MSAKK1d0kGHzQML0P5fcGsm1awwri+b5hx1ZuRVl241oNPK8ubH4aLej1mvC2GYwGE6cKMBHuy1ekmDtvHQ88\\u002fTkWLur3RXU1wvxITMBAc7PLXpbL0CPIMPPy5Nz373z8NE+FAr13r1Zd2z59rnndp2\\u002fUOo6nVvtTz5aw2A4c4SJsQrtKl0H2uaHk7+fm40VnpNbiw8NUJ6V2zEBINi+gW0tqqi0mnAfim8rAGCYre1Krzd0dqLrZwiCjAzXPRF1Ig6NVikUqrRaKqnn\\u002ff5bXNoqlaaFO7+OmOsQWcmVD0ibQoYPAQAAn+bQRXUHdagU+BAyZKx91k75FJwyE8RBU70Cjq7aui1j37G6MnybdVQiaVVo7NbYybFuTu5EPNqlpyf+ezgHHwUAgLKy5qqqttBQT3xDf65dqxYIpPgoAABgGCwdiXoybW2SlpZOgVAqaJdKJEqZXKWQqxUKtUqtVau1KpVWpTI+0BgfGP\\u002fh5zKU7ByZbaS0W\\u002flAAQDDME9P52d8vLy4JSVWqxIND9ffbJDhtHbt5GvZ1fYMKG0wQObVysyrlW5uzEUL45csSRzEjtS5HL+eR6U6OofxAO0qXWdXOR62eddZ8xgGsbF+WVlV+AYAALhypXLunBh81G5are5adjU+2i0pcfAjGTQ0WO04X1LSnLbgXXx06CktjQSAIAgyDFz9N88ptk5Ofjfj7PrfdiyLieJQqUKF4lp944XqGh826+GZ0\\u002fFTjyHNMgk+NHBNcgszIREI7laSsMOjXWn5MgAyFNxpDBJG0Pap2N4gE+MigyBAH6UL6FQrnzh\\u002f8GR9hXmQSaIEsXnNcolMq9bq9UwyhUuhcSk0HwZroodfipf\\u002fRA8\\u002fBqnXnUOIUUJCQGCge12d5U55R4\\u002flb7t\\u002fPj7aHxv1ZJKSghy5+dd+NTXt+fn1xcVNpWUtdXUCpdLVT2AcvNt6qHV0WN37cbl0Esn59yAOqHius4y6zQYZThgGr7y85tXX9l2+3OsHyIaODtmu3Zm7dmeGh3stWpSwaGH8UNQosIfjqUMSyWqJKsQE7Spdx5jc5l15zc+ZHW0t4X7iRMHWu2bbKDtj27Hj+da+WQQCNmVKGD5qt\\u002fZ2C6mDkaVSDskVEQRBkH45+qs5KtwzNYXPpP95I+\\u002fry5lKjZZJoYS48R6eOX3L5Ek8mq1BNka7emknPjRAKp22wdJMgtlugx+83BnqLC0VMkRIGCGQza0S4zss1Es7tXo9ybGRgtBHOeKaZOJbj+\\u002fAfb4zfII+nrXClzHI43gkfUnS9u8z8FEAADh5suC+e+cNqJqkXK4+f97qzQdD2r3dYIDc3NpTpwqvXKlsbXPCNbbhxOGMQNLEfp3WawQP0ZKzmMN0zDOqNxtkmFEopLfeXP\\u002frrxd++\\u002f3igMrFVFS0VlSc2r49Y+rUsGVLJ8yYETGg\\u002farjHE8dWqtzjZhDu0rXMWa2+dGy5ufOjfn8i+MWh2TXanXffnf6\\u002f15YhW+wg0Si\\u002fPXXC\\u002fhot8kpoXw+Cx+1j1arc8H6LVqthRWIIAgyDMZFwh0A1sbHrY2Pw0fHuhJROz40QKWidotDKYZx3PEhS6wl5RU6R\\u002fsOlDn81pABCeO49024a\\u002fS68k5BjJtD93RXii33AkaGh0StuvX4TtyHuzYs\\u002fsPU5da+v4g9Fi9O+OHHM3q9hf2nQCDNulY1dSC9h86eLVZZuR+WwaDMmRONjzqDSqU5eChn756spmYRvm2UcPzm9yGlsN6NjsWyWrbVEcyhma25MbDZIMMPw7AtW2bNnBnx30+OFhc34Ztt0un0ly6VX7pU7uXJWbFi4qpVk4YoCduX4\\u002fl99DNrD7SrdB1jYJsfXWuezaYtWzbhwIFsfAMAAJw8WRge7rVp48Du19dq9a+\\u002fsb+11eplhk23DGyG5sRipaXMAYIgyDjl0ueiiIPyBI7WH8xqbcCHAABggocvPmQJhWh5A5OoHR2r1vG3hgxIIt8HV2\\u002fEKLutwcGEO\\u002fooR9Yrmcerel\\u002fzmODh+8HMZSjb7iA+nzVlStiVKxa+NQBw7GjegBLuNoZanT8vlkZzcmEfgwEOH8n5\\u002fvszIuv9CkcFx\\u002fviDSkbVV\\u002fJZMu\\u002fng4iDWXXwjGz2SAjJTLS58svtpw4kf\\u002fzz+cHkQtrbRP\\u002f+NPZ3\\u002f+4tDQ9cdMt072GoLQ3DhqqenigXaXrGNXb\\u002fChd87fflnrkSJ61XhfffZfR2Ch6+KGFdvYwaG0Tv\\u002fba\\u002fqKiRnxDt2nTwidOCMJH7aZSW15OBEGQ8cmuXTMySrUrZRWdgnAuH99gt\\u002fNNlsvGpXj640OWsMiWRwx3sAJ7obAVjbQ5zKx94uebqm+NmoiP2q1TrUQ3K4ygss72vZUFuOALKfMdLBOEGC1NT7KWcD9\\u002foUwmUzGZdvWha2npzM2txUe7pTu7nkx7u+SNN\\u002f\\u002fOy6vDN4xCRIf74g0pizeJGw1RuodEHqorEGNps0FGEIbBokUJ8+fHHT2Wt3PH5Xrrg+9Zo1Jp9h\\u002fIPngoZ8XyCXfcMWtIB3IYzbnH0QTtKl3H6N3mR++ad3dn3r11zldfn8Q3dDt48EbmlcrVa5KXpifZ2OPVN3Ts33\\u002ft8OFchUKNb+vGYtEef3wJPjoQarXVbyuCIMg4hBLuY9yxurIHBptwl2pU55qq8VEABomc7OmHj1riRbdcAC5P0Lw+PBEftdvR2lJ8CBliKV4BVCJJpcN3MjrdUCnXagY9cuaZhkqLNYuQ4XGgshAXYVOoU70DcUFkcGbOjOBy6Z2dFq4OqtXajIyi5csn4hssOX68wNq3JDDQPT7e8sWwwSkqavy\\u002fF\\u002f8aXOcvDAMWi8Zm05hMGpNBodHJNBqZRqPQaGQ6jUyjkWl08pkzxYWFVvtVjTfWPlYAwIbmUsEQXYFAmw3iXCQSYfmyCcuWJp0\\u002fX7pnb1Zu7oBzZFqtbv+B7OMnCu68c\\u002fa6tSmjulsugnaVgHaVjhnta\\u002f6mm6ZkXavKzKzEN3RrbRNv357x\\u002ffcZAQHusbF+fD6LxaLR6RSFXC2RKJtbRAUFDW1t\\u002fYxlSiBgL7+02sF7gwY0DgeCIMiYhxLuY9yeivxtCdMHd9i4qzxPrbNwmXqefxjVSq0YnAAWFwPoe5yc3Tb4gxKtQb+rIg8fRYYYg0Se5Rvct6qMQqvZX1kw6E7ufbtXI8Mpt089Hx6FNrjdBdIXiURcuCB+z94sfAMAABw9lm9nwv3YMat7POd2by8sbHzm2Z1yuV0lv4hEQnS0T1SkT3i4l7+\\u002fm5+fm7s7s98SLpUVbUN6Ujq6UChEazeJa7Wj5pQVbTbIEMEwbPbs6Nmzo6uq2g78nX3yZKFMZtdmZiKTqb788kRGRtFLL6728nIoi4SMILSrRLtKR4yBNY9h8OL\\u002frXr6mR0lJfjjdnMGA9TVCevqBjM4FoGAPf\\u002fcysmTQ\\u002fENA0SxeXeIjw\\u002fXz88NHx16DIZdd5QiCII4nV1pU2T0Ku8UnKgrWxQYiW\\u002foj0qn3V54FR8FAIBVofYOP8sgkYPZbtUS\\u002fB3BeYLmElFbNG8wtb93leU2yawO84IMnZUhcX0T7gDwTcGVDRGJZIKtAyyLSkRtZxqsdtZAhoFMg7+rtEkuEaoU7tRhGnFuzFu6NMlawj0\\u002fv76xUeTnx8M39FZY2GCtqAKBgC1elICPDpZAIH3p5T39npGSSMTZs6Lmp8WmJIfQ6ZaLhiF2Ils\\u002fL9VqLVztdpzO0ii+jkCbDTIMQkM9H\\u002fvPkge2LTh7ruTw4dycnBobXZ77KihouO\\u002f+n95846aEhAB8GzIaoF0lMmhjZs2zWLQP3t\\u002f03PO7Cwstj6\\u002fmCCaT+sILK2dMj8A3DByFaiu5NHtW9AMPpOGjCIIgY5etfSIyNryRdSrVN2SgRT8+y71oMa\\u002ftw2AvDBzA73GKl3\\u002ffhDsAbC+4+mHqMny0P81yyfvXz+CjyLBYFhz9etZJoRJ\\u002fP2atRPRl3uXHJqTi4v164+opJ5\\u002fQIAPkxcAXfdLq9c9c+PezOasGusdALAoL84qM9Ckrs9wj6dixvDvvnI2P9mZjuNSpU8P4fPwnOGjvvX+oo8PW6BpEImHlykm3bZ7p7s7EtyGDQqVY\\u002fZbJB9iT105KpeVeooOGNhtk2FCppEUL4xctjG9p6Tx+vODw4Vz7B1YVixVPP7Pzk\\u002f\\u002feGh3ti29DXB7aVSKDNpbWPItF++S\\u002fm3\\u002f66eyOnVcMA7rqaNOECUFPP7Ws3\\u002f4fdqLTbF2uUCjxHX0QBDHXqizZVf0APtothDV9RcDb+OiAGTKaPy2TnCJi5Mn825Pc1uDbh8tVwW9X2n40Pp7Mv22659be7WPEkAw1g7iUWono8fMHB1Qp+3RDxdf5l\\u002fFRAADYGjuZhA1gs5nvH4YPAQDAXxV5pxssdJe2oUUu3Xx8p0ilxDcgw4JCJN4RnYyPAgDA57kXzjVaHl\\u002fXmreyTp+3NEIAMpzCOe74EMCJ+vIF+7f\\u002f98b5S821TXKJSqcdwL4D6WNputXxKo4dz7e9Y9ZodBkZRfhot6XOqydz4UJZVpatr7CHB\\u002fvTT2579JFFrn9GOopweVZvJRFLhuSXTml9qLRBQJsNMiK8vbm33Tbzt9+2ffDBplmzogj2ldtWqTSvvrZfLLYwqAbi4tCuEhmcsbfmSSTCvffO++brLTNnDvjm9b6Cgz3+74VV\\u002f\\u002f34Vmdl2wGAxaLZuCVlcGX0EQRxojpZdr7ob5VOKtd2nG\\u002f9UqET4adAnAr1cB8XjtaW3n9670ezlnMpNHxbH8fqyv5z7h+9pTyQL4O9JcZyytWahYGRLDJVqrHQA+WRs\\u002f98M2\\u002fNLN8QfIMl+cKWh88csNhZHhk298ZN+bUku28nd53BcF\\u002fGvs9mr7SneJFGr3sv+8z3VgoWIcNpfUTil3mX+n7Vm+SST3MvfJp7Ad\\u002fQGxHD2BQqh0LjUmiRXH4i33eih+9ETz+7kh\\u002fjxoIF8V9\\u002fc0qjsXDbe3NzZ15eXVKS1VFqr1ypkFjJJnC59BkzBnCzkW1\\u002f7riED5nh81mffrLZ15eHbxg4g4VBPcYvN57VM3yRSG4wgNMHehRZGsJ30NBmg4wgDIOU5JCU5JDWVvHevVn\\u002fHLyh6C9J2tLS+d\\u002f\\u002fHn3llTX4BsS1oV2lndCuEmesrvnISJ+NG6eVlja3t\\u002fczDqpFTCZ1xvSIRYsSJk8Odfp3B8PA05Pd2CjCNwAAQFubhbvnEQQxoRLZgcwUha5Tqe1U6Dp1hn4ObAahU9Mz4ITeoJNq2uhEXk8z4mwo4T5enKgvX3Tgh0eTZq4Nj2eSLN\\u002ft1SgTf5JzYXd5rrWjhlenLrRzuFQTGpF0a9SE7woy8Q0AUo3qzpO7t8SkPJgwnU9j4Ju7lYjavsm\\u002fsr+ywNpSIcOGSab8X8r8Jy8cwjcAKLSa+07vvSk88bEJqQEsLr4ZAAC0ev2J+vJ3r2WgCycuIoTtdkvUxD9Kb+Ab7KMzGEQqpfGmkzxBs3EI3GA2b1PkhE2RE9xQIXgAAGCzaampkRkZxfgGAAA4fjzfRsL99Gmr3dsXLkzodxQvO1VW2RrsyzhUl1POSAFApdLiQ+OYu\\u002fWKQFqtTiiUOrFkkJHtO+sHBG02iIvw8uJs25Z2660zdu68smdvllpta2s5c7a4rLwlMsIb34C4MLSrtBPaVZobq2terzf88OOZHTsu9+0ax+MxfHy4bW0SqVSpVusADCQSkUol87h0vgfb388tNMwzLtYvOtrXzhuDBsfLi2st4W4tjgxCm7KsUZ5LJFASeCvxbYh9XHAdcsl+qwM\\u002fMD3V6BVl4tOnmj80m8RRXLKf6TERo3DIPmaNY9CIf8oDS54io8gcv1CRSpEr6Kkd3KqQvnjl2NvXTs\\u002fwCZro4RfE5nEoVAJgEo2qUtxxubn2Skutjcoza8LilgRF4aN22BY\\u002fbWdZbqfaQj9NrV7\\u002fQ+HV\\u002f5VkT\\u002fYMmO4T5M\\u002fk8Kh0vcHQoZJ3qBTlnYLzjdVNcgtX71+esuBAVWFOexO+YSCa5BKRSiFRqyQatUStkmhUxv\\u002fFapVEo5KaPRX06dNtNHnXF2wKlU2msClUFpnKJlPZFAqbTGWRqV1xMpVFobLJVE860878o1avr5OK+i6SaXkkapVYo5KoVVKNWqSy0AfnRF35jL++Mi4Di0zhdC8bi0Jhdy0klUWmGB\\u002f4MzkDuohyU3jCkdqS43Xl+AYAA8BfFXn7KvOneAdO9w4K47hxKDSNXidQyoVKxfX2xovNNX1H6bwtelKjTHzK0nCs9nPNj3JUeH3qoiaZZKD1nWyokYjeyz6zvSDz9WmLVoTE4pvHpaXpE6wl3DPOFD\\u002fyyCIKxcJ3UKXSXr5i9XOxUalmoC5fsvB1NpkzJ2bChCB8dLD67YI6rvj78\\u002fAhM42NHU7PIjU3d+JDg4U2G8SlcDj0e++dt2LFxPc\\u002f+DcnpxbfbOaPPy698vIafBRxYWhXaSe0qzQ3Jte8SqV95dW9mZmV+AaAxYsTHn5oIYvV\\u002f73sQy08zPPGjRp8FAAAJBJla6vYy4uDb0AG7rpwV6n4JI3IGak04hjg+uuQTKDTiE7+vgQyUxLdVpeKT5IJjFTP+6hENn6KsWXEP2ULJ\\u002fnI2HB79KREvs\\u002fKQ7+0KXp105BrNSfrK04OML+ZyPd5Z3o6PmofdxrjtamLHjv\\u002fD76hm1qnu9hcc7HZ8m9zX4sDI7fGTq7oFDqYcN9yYlepqB0fHYh2paxdaVcvmI0RSe\\u002fNXIqPWlLY0brq0C\\u002f46EBo9LomuQTAwoWKvn5ZsGGulTr71nycumLd4d\\u002fKOi2vOp3BcLm59nKzrdNdkwkevi9PXvBV\\u002fmUHE+6u+VGOCiQC4ccF638tzn7j6kmtQY9vHiyhSvHw2b8vNde9NX0xvm38mTw5xMuT02rpRlqZTHXpcvncOTH4BoDMzAprp3BRUT5hYV746GDl5tXhQ2bWrknBhxzQ2mphJYxbQYF8fMhMRUVrYqLVux8Gp6HBaXcXoc0GcUG+vryPPrzl3XcPnjhZgG\\u002frduVKhVarc9YdQsgwQLtKO6Fdpbmxt+Z1Ov2LL\\u002f51LbsaF8cw7NFHFq1ePbCir0MnyubY1EVFjSjh7hT18mx8CBmgcbsO53r\\u002fZ673f\\u002fDRMWrEP+UBjH6JjCIkjDDTJ9iHwf5t0UYb1VrsFM3z\\u002fDFtPZ1ExjfYbU1Y3H3xU\\u002fHRQZnqFfDZnFUAEO\\u002futHwTMiBsCvWXhRtC2G74hgGK5nn+vGADhUiMd0c3d4+Y803VT1049GnOeSdm201+L73+VtZpfHT8wTBs8eIEfLTbyROF+BAAAJw5Y7lTPACkO2+4VACoqRHgQ91YLFpCQgA+Olg6nb6pSYSPjmPBwR74kJnS0p4b1JyioaFDpdLgo4OFNhvENREI2HPPrbBRqkup1OQXNOCjiAtDu0p7oF0lzthb8998e7pvth0AHnl4oetk2wEgNranYEVfWdeq8SFk4ASqSrnWadcFxye0DscDV\\u002fiUUcJ9bJrk6cckUwAgmuf5V\\u002frmCK6tviG2TfcJ2rHkFk+61QGL7PRCyvyHEmfgowO0JCjqp4UbaEQSAKAs7QjyY3J2p2+e6GHriMq2GT5Bfy65xVibBV07GREn6svT\\u002f\\u002fnxtuM791TkCy3VJnKK7YWZA72fZkxKT0+yNjLVlcwKqRRfcUut1l66bHm9kcnEBWlx+KgDbPTMCgvzdGKhz8qqtuGsc+r6\\u002fP3deDyrV8Sv37DrPiH7lZQ4dE8YDtpsEJdFIGBPPJ5ubZcLAFWVbfgQ4sLQrtIeaFeJM8bWfElJ0969WfgowLRp4Wuc2lvfcQH+bn5+PHy026VLZXq91QK2iJ3qZNfwIWSA0DocD1zhU0YlZcam2X6hpsehHPe\\u002fl29559rpP0pv2CjR3heNSHo4aeaDCdMJNs5aBuLpSXOSPf1fvHKsSWb1GMgaDoX6xMTZW2JSTIsS7eZJxLABvSPEiTzpzL\\u002fSN3+Sc\\u002f67wky1Todvto5KJD2YOP2hxBkkrOuCnx+T40aldwxZzhfBkWs1z1068neV5Y7VTBIl1t3Lg8ZgU6hkguWb7nV6vUavk2s1QpWiSSZukIn11r+Jb2WdmusXSiKM6+u7fn68xMTA3FwLNzhrNLrzF8rSlySaB69erbJWT2ZWahSb7bQanUqlxmD9s\\u002fPwYONDDrhqqeroOJcQH3D+Qik+CgAALS2dtbWCoKDBXy\\u002fHyc62t25bv9Bmg7i4oCB+bKx\\u002fYaHlnuwdIrsqyCGuA+0q+4V2lebG3pr\\u002f7feLFt\\u002fR7bel4kMuYMaMyD17ruKjAAAgFMoyMyunTw\\u002fHNyADUesCacTRDq3D8cAVPmWUcB+b5viFmD9lkMhvTFt8W\\u002fSkb\\u002fOvHKwp7jc9yiJTbwqP35Yw3ZfhzCMSAFgQED7L994\\u002fy3J+K7le3inAN1viQWPeEjXhrpgU9961cWhEUjiX72DlbsQRJALhqUlzNkVO+Cr\\u002f8oHKQpnWcn7QhEEirwtPuD9+WiCLi2uKc\\u002fe60OS0MxzEBrFadefJ3dlt+DQEiUC4OSJpY0RSIt9noNfY5FpNVmv9XxX5\\u002f1QV9j0bqBQLT9aXD27I5bEkPT3JYsIdADIyinAJ97NnrdaTWbrUmfVkNBpbPwcUsuUrLoNzOqMIHxr3kpODrWWRAOD06aItW2bho4NiMBiuZFbgo4OFNhvE9YWHe1lLuIvF6AL\\u002fKIN2lf1Cu0pzY2zNy+XqS5bGgGWzabbrt4yUeXNjrCXcAWDXriso4e4IpU7cqMjBR5GBQOtwPHCRTxkl3MeCrbGTt8ZOxkf7iOZ5fjxrxStTF55rrL7YXFMmaq+ViiRqlVKnpRFJXAotmOMW6+Y50ydklm+wIxXbbaMSSXfGpNwZk1LW2X6usTpf0FwhFjbLJWK1SqXTEjECk0zm05hhHLdYN69ZfqHJnn6mrtA4x1bdjQ8NhIMvHyJJfJ\\u002fqO57FR11YAIv79vQl\\u002fzd5\\u002fvnG6ovNNcUdbTUSkVitNG5XbAo1mO0WzfOY5Rsyyy+ESaLgXw8AAL8v2oQPDYQLfpRrw+LXhsXjo84Q5+7lyBby+Pl\\u002f+mbbw7n8L+esjnHzxMXtxCCR5\\u002fiFzvELne8f9vj5g\\u002fhmgFP1FSjhPm9uzOefH7fYbz07u1oqVbJYXf3WtVq9tXoyXp6clJQQfNQBVKqtwwCpVIUPDdaNnNqKilZ8dNxLTY36\\u002fIvjlnqtAQAcPpx7220ziUTLv4ADcu1atUAgxUcHC202iOujUKxupWQ0Yupog3aVtqFdJc4YW\\u002fMlJU0Wy7C4uzEH2ENmmMTH+4eFeVVWWl4zN3Jqr2VXpySH4BvGKLVeViW5WCvLEqqrJZpWjV5uAAOZQKcTuTxKAJ8aFsCY5MtIIGFU\\u002fCu7daob2lUV7aqKdmVFu6pComkxNSl14i+K08ym7WWS+82pXtvw0W6typJd1Q8YH2+N+ItBcjc+VumkxZ1Hq2WXhapqpa6TgJEZJHcWydOfMSGIOcWbHoPZUZW6TVlWKb1QL8uWalvlWhERI9GJPHdqaDBrSgR7Ho044IFzHZzhEK1D16c36L4qWYSPdmOSPO6K2IWP2keiaS3uPNqoyO1Q1ar0Eo0eXx8VJ4CZvCbwQ3y0G9a9L1PqxOWSM5WSc52aJrlWYAADlcB2pwb70ZPieEuZJFtjurjmp2zr1wgZk7gU2oqQmBUhMfiGYRfJ9Yjk2vrOIKMIk0RZEhSFkqoubnd5Xt+K6sbRlZ1yO8vasPhT9RX\\u002fVOM7+5xrqsZFxiEajTxvXszhw7n4BmOG\\u002fVL5okVdA6tmX6\\u002fuW9XdaPGSBNMRiVNQKCQqlWSt\\u002fKiwwzmFFwwG2L49Ax9FADw92dHRvsXFlksGt7aJT54stDHirv327XPmPZVos0Fcn40Kzqarm8hogXaVNqBdZV9jbM23W7kIJOyQGQwG5x4WOsua1ckf\\u002f\\u002fcIPtrtk0+Ofr\\u002f9btvXRcYAAxiyBX9eE\\u002fyh1stxTSqdRKWTiNT11dLL1wR\\u002fUAiMCPbcuT7\\u002fIWIWuqbtqL6332ymg6TadmPCvUR8\\u002fEzzZ2p9z3dEZ9B2qhs61Q0N8huZ7b\\u002fcGb6DRbY1+lqHuu5i6zdV0kvmQZ1BrdbLOzWNVdILF1u\\u002fS+ZvSuHfak\\u002fiHpw0w2FYh+OHzqC+0PpNvugfvcHWvUQDQsboAFAiPnG25TOVrtceT6tXybTtdbJrWYL\\u002fJfNvneqxBQPLOz3X\\u002fJStbpQIgiDIWGIA+Cr\\u002fMj4K8ELKPKdk243WhVvo198il9go8j5+LE23Wg3m\\u002fIWynsfnLd84j2GQvsTqHAbNy8tqr5Dy8hbb92Xbad++rKKiRnwUAQCAZUsn4ENmfvjxjFKpwUcHqLCw8dJlC3ejOwJtNogr0+n01urJAEBAQFc\\u002fPmQUQbtKa9Cu0qLxsOYlEuWpU\\u002fg+Li5iyZJEHx98BVGThoaODz44hI+OLQbQH6p\\u002f8VLb932z7X2p9fI2VbnFbPvwkGkFAJAl+O144zvm2XYcPjXMdra9Vpa1q3obLjmOo9bLL7f9eLDuBXvWjNNnON5gGCGQmeJJi2SRvUgEq3dR2E9nUO+vfTK3Y78p244BRify6ESexTw4lcj2okVHcuZHsefj28xQiMxrgj+ON76Ny7ab0xm0V9t\\u002fPd38Mb7BtY3x64oIgiCI0bXW+iqxEBf0ZrBWhsbhgo5I4vviQwA6g0GkUuCGYRiHEhICAgLc6+vxnwIAZGVVabU6EoloMBgumCXfzSUlBfn58fBRh4WGetbVWVgkAFCrtdev10ydGoZvGIjc3Lpvvj2NjyLdFi2K\\u002f257hrV7GtraJF9+dfLJJ9LxDXZTq7UffvQvPuowtNkgruz8+VIbhUEiIr3xIcTloV2lRWhXac1YWvN8dyY+1O2jjw9jGKSlOfNI3inIZOLdW+e+9fbf+IZup04XcXmMhx9a5JId9J0gS\\u002fBHtVma2JeeEMBM5pB9KASG1qBWajsF6uoWRZFAVWmcIIG30jQxzi2hPxgMetNTlV5qKgVDJbJvDv7K1IRDJdrboUqmbS\\u002fqPHK57UfjUyaJ70tPZJL5ZIym0HUKVJWtyhK9QRfKmtH7db3Uya4drH\\u002felIelEBhh7FnetFg6iavVqzs1jdXSS23KrnOcGlnmscY3VwS8BZaytEZOnOEwrEPXhAG2OvAD01OdQS1QVZne+yCcbfmiSVFgfEwn8mZ63RfOnkMhMABArZdXSM5eatsu13YYJwhhzVgR8JbptTbUybIqJeeNjz1pkSGs6RyyL5lAk2s7GuQ3KiUXDND18RWKDgUzp4azZ\\u002fe8uJtrfsoo4Y4gCDIuXG6xMGLnLN8QC0clDuBR6fgQAABINGqUcAeApelJ27\\u002fPwEcBFAp1bm5dcnJIUVFjh5WbndPTE\\u002fEhZ4iL8z97tgQf7fb77xcdOSnNza174f92a7VO6Eo2VlGp5DVrkn\\u002f77SK+oduhQzfCwzzXrEnBN9jBYDC88+7B6mrnjy6ONhvEEd98e1ohV61alRwebquv3OC0tUk+\\u002fewYPtrNy4sTHMTHRxGXh3aVfaFdpQ1jac1HRvoQCJjFMu5KpebNt\\u002f7+7feLc+fExMX5+\\u002fnx3NyYVCrJKUMaOCgtLe7QoRs3cmrxDd327bsm6pA\\u002f+eRSBsOZPbtFIvmJEwVHjua98\\u002fYGT88hzKPZYAB9Xsc+42MMCMsCXg9lzew9SRe5VlghOVchPRfFWYBv68Yh9+rMpNR1mh5jgHEp\\u002fmaNg1Qru1oruwoA7tTgVM9twaxpuAnUelm5+Iw3PRYXN1HoRMeb3jYlxyM58+d5P4bLY07zuLNEfPxU00c6gxoAqqWXc4R7J7jfZD6NiXNnOAzrcFQgYhRTsf5B6FQ3FIq6rkyTCfSbgj\\u002fjUQJMrRQCI5ab7s+YsKPqPuN9EtXSSwJVFZ8aaprGmgrJOQCgEJgLfZ8NY\\u002fcaBT3JbW2bsuyf+ufl2q5rqNcEf1hMuLvmp4wS7iMsK6fmiZd3AUCQv\\u002ftvX43MwI+\\u002f7rr0\\u002fe9dF5TMff3+5vhoP3wUGTe2PvZLeVUrALz74rqZU5w8mrxzt7od+67+vPOiuxvz7RfWhgSiE2nLykQWziTDOU5eXXqzC8vm6CT0cwMAsHhxwg8\\u002fnrF41pSVVZ2cHHLhouXu7QwGZe6cIRl7Y8b0iG++OYWPdsvLr9+\\u002f\\u002f9rgUhh\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MAKmvaDhzJuZ5X2yaQ6PXg7sYMD\\u002fGclhy6fGGicSU7bv\\u002fhGx9\\u002fcxwAYiJ9vvvwdnxzb7c\\u002f9GNNvQAAXvjP0vQ0fF2IxmbRuSvlN\\u002fLrKmvaOjrlGo2OQad4e3KiI7wXzo5NmWB5gPisnJonXt4FAG88u3ruzCidTn80o+DkueLqWoGoU06lkny97N1ssm5UHzqRl1\\u002fcKBTJmAxqkL\\u002f7gtkxKxYlkclEMrmfHd\\u002fgFt4pTp4rfu1DC7V3X3x8+eJ5cfioFQP9wu49lP3JdydDgz1++eyudqH0s+2nrt6oYtApc2dE3Xv7bOPEew9l7\\u002f77WptQGuDrtnH15KUL8J\\u002f4yHp8wqyD1cVSDb4YrgHgj9Ibu8pzp3kHzvMPi3XzCuO4e9KZZELPNqDR68RqVYtc2iDrLO8U5AmaM1vq25UWrmyb0Iikb+atRd3bLVqwIP7rb071W1eUy6XPnBmBjw6lmTMjf\\u002frx3q+\\u002fOXnqVJFhsLU2SSTiqlWTbr8tlcu10GFheEqdjmru7sz33t14+nThDz+cbWoW4Zvt4O\\u002fv9tCDC6dPx9+tzOMx3N2Z9lc0thPabJABWbQoYebMyCNH844cya2ocH4iyc+Pt2Z1ysqVk4aozyniItCuEt+GdpVWjMY1f\\u002f586Ztv\\u002fa1WW00+enlxEhMDoqN8\\u002fQPcWEwqk0Ujdfe4t0in02u1OqVSI5EoOzpkzc2dVdXthYUNIpEcP6kZvd7wv98uvPzSGnyDA+bNi0lNjTx5smDP3iwn\\u002fgTw+ay0+XHLliUF28weDD8MsFDWjFDWDLm2o1R8oqjzqEBVaWqtk13bVf3AuuBP7ClW7oIoBAYRI5my5Gm+T9MIAxvZhUzodWnE6TNEnGiB71N\\u002f1Tws0bQCQLuqYk\\u002fNI3Qij0PxxQATa5rNC+h70iLTfJ\\u002fqeeV4hQ5DnUOvN3z9c8buv6+Zks5GHZ3yjk55YWnT30dz6DTKru33cTkWfqEBgMmgtgul\\u002f\\u002ff2\\u002fqKyritCAKDR6sqqWsuqWvcdvvHsw0sWzbWaSczOrX3388PNrT0dVDUaXVWNqqqmfe\\u002fB7PUrUx68a56z0s0mew5mHzlluVvrgDhl4duFUnc35k87Lv6y46L5p9DQpG5o6iitaFm52Gm9RBfOif38h1Maja64rLm2QRjk746foltJRYsx206jkefO7FXGurm18\\u002f0vj\\u002fUtfC+RKiVSZXlV66HjealTwl99ZhXV+gDxNfUC25vNc4+kL5wTa\\u002faKHlqd\\u002fr3Pjxw93XNZUtQpF3XKcwvr9x++8fYLazjWe4I4ZeFHkCNf2PqGDoVS\\u002feSru6tq2gFAJlf\\u002fdTC7uU389gtrf9l56Yc\\u002fzhsnq6xpe+ezwxgGfa+yjCBvBuv9mUsfOrPf4qmGVq+\\u002f0FRzoannbIGEEagkEhhArddp9P2khnHoJPL2+evi3Z18U\\u002faYwWbTUlMjMzL6GaFr4YL44R\\u002flz92d+X8vrNp868z9B66dPl00oGqbAQHuixclLF6SYKNc8sRJwfDzOXwU6Q3DIC0tbs6c6LNnSw4evJGTW2tPigDDsKSkwDVrkmfPirb2uxke7iUUVuGjDkObDTIgTCb1pnWTb1o3ub6hI\\u002fNKRfb1mqKiRgerJfj68qZNC58zO2rChGBnF5BAXBTaVeKgXaU1o2vNnzxZ+O57B3U6Pb4BAAAmTQreuHHalMmhjpfKMRggP7\\u002fut98vXr1qdWs\\u002fd65ULFZw+pwTOYJMJqanJ6WnJxUXN509W3L+fEl9Qwd+IjswGNQJEwKTk0NSkkNCQlwrz94Xg+Q20X3DRPcNTYq8q+2\\u002f18oyjXGFTnS04Y2Nod9ho\\u002fMOcxqRK+seJ9OLFuV4bXqnzxBxFibJ46agz443vWsq1K7QiRQKkfk0AFgsd8kc70fGcKEY+2GDvsaLmPv217O\\u002f77lifOzrzY0M8+ayaRqtvrm1s6yyVSZXAcDkCcEfv35zr5eZdVVOiPHX6nTFZc0YBlFh3iFBHiQSoa6hI6+o3vgRkYiEr97fHBPhg5sDAJy\\u002fUv7K+39rtDoAYNApSXEBnnyWUqUtLG1qaOr66VowO+aVp1b2epkVq7d81SGSAcDX72+Oj\\u002fbDN5u5er26uq5rV6hSa777X9dBxrYtc8l9MkQxkT6JsRZutXNw4U093N976abisqafdlwEABqNHBLIp1PJbUJpc0unVqdfu2zS4\\u002fcvxL\\u002fYAa988Pfp8yUAcMeG6ffcNhvf3O2z70\\u002f99c81AEhPS3jhP0vNm5Qqzfq7vxVLFABAo5IjQr38fXlUCkkglF7Pr5N3DwO1bEHic4+mm78QzDabWdMi2oXSwW02r3908MTZrlEvPNxZE+ID6DRKa7skt7BeqdL4eHG9Pdk5BfUA8O6L62ZO6fU758jCW2T\\u002fVmekVmtb2yWdYoVIougUy\\u002f+3+4pxa7Gzh\\u002fvgvrDGHu4AsGJR0rEzhalTw9Vq3YXMcmPry0+ueOuTf915jMkTQ0orWiqq2wDAx4uza\\u002fv95jNxBb+XXn\\u002fpynHcxQbnCmLzvpyzOpFvYcPrV1nnruy2D4yPMSDcHNH1SVl0sfn5VkXWksA\\u002f6QMfa6VU9Of19o9n+LwVxFqMbxtKjizz8NPp9AUFDXn59eXlLY2NovZ2iVyuUqt1RCKBSiXRaGQ3N6aPN9fHlxsd5ZuQ4G\\u002f7HmEb7\\u002f1A1RISgbE8eB8ujhgJO9orJJMB4NifjxfmUaVSpXG0QDqdwmLSfP14wcH82Fi\\u002fqVPC+p4SdyrPVrQ\\u002fyKHNjvD4Gtc0OIXNa2TqG6anHFpqtNfvPc3O3mz60htk1+riASDB9yidbNfNqiZOXxvj1jtFP+WKSr9Med6dgs9A\\u002fdN49pvyv4yPCRjhn9mf9GruzXw+rW3immpBXZ2gqbmzvV0iFEglUqVMppLL1RqNTqfT63R6IpFAJhNpNDKXQ+fyGN7e3AB\\u002ft9BQz9hYv+EZ7mIoXBLkvlnw\\u002fd1ha9YF9BRI7deg1zO+bQwRieTZ12sKCurr6oQNDR0D2lUOv6HeVTrXYxu+LMnpKR86cUbEO7\\u002fca9Y+mji+5i+eLHz90d\\u002fveSp9\\u002fV1WTwMHrby85cGHftVqLfR0IZOJDz24cNWqSfgGxxgM8M03p3b\\u002f1ZX\\u002f7euZp5elpzut75pFAoG0qKixpKSpqUnU3CJub5coFGqVSqvR6KhUEoNBodMpdDqFzab5+7sFBroHBLgHBvL9fHnWrpm5vlLxqRNN7+gNXR\\u002f0msAPA5jJvSexQKnr\\u002fL5srfExjci5J3J\\u002fr2a7tSpLdlU\\u002fYHzsRYu6OeSb3u0DcKj+xSrpRePjRX7PR3MW9W4fMKfPEMdZ63AYVErO\\u002f9vwsvFxCGv6ioC3e7cPmFTb9nP5RuNjJsnjrohdvdvtUivLOtLwmrGGO43IUemkBIxIJbJ5lAA\\u002felIsdwmXYiHvh3NV8NuVth+Njyfzb5vuubV3O97lth+zBL8ZH0\\u002fz3DqFf1vvdjwX+ZRdsfPpqCNXqI1JVQIBe\\u002fnJFWmzYsxb9XpDUWnTyfPFUyeFmMdx8osbACA4gP\\u002fyUysiQ3vqBhaXNT\\u002f7xp6OTrlWp\\u002f9lx8V3XlzX8xoAAGhu7Xzrk0PGhPXq9InbtsxlMnrqYBzLKHz\\u002fy6NqtfbkueJJiUGrlkzoeaXDpkwKmdL9pqQylSnhvnbZRBvFc8w5ceH\\u002fPZl39lKpJ5\\u002f14F3z586MMt1SJ5OrL2SWhwTye0\\u002fuqKVpicaE+7EzhXdvnm2xe4FOpz\\u002fZndHuW1qERiXfvHpyeWXrsoUJKUnB5vVbZHLVm\\u002f\\u002f915jJPXIq\\u002f+7NqZ58ds8rzZy\\u002fUg6D2mzOXyk3ZdtvWz\\u002f9ns2zTAcrYoni\\u002fS+Pnb1U2tza2fOC3pyy8I6gUEgBfm4Bfm7Gp0dOFZguz\\u002fTL8S\\u002fsoRO5n7y5aVJCIJjl7t\\u002f876EAP7dvP7iNyaBqtLr7n\\u002fqtvKq1uVVcXSdw+ubnoM1Rk\\u002fyZ3CcvHBIobd1DOjgkAmFLTMrjE1JZ5EHW5w1kLeRQQtW6zhzBZ\\u002fL+axoaSBgdw2zdQusUtdLjjbKz073fwDcMxjAts1MQiQReWHG499nNt4679+5SeDw6SAAAnngifaApZrHynE4vFSmO4xsGy4N1E0Mdp9UL5epCldbC8C1EIiEpKTApKRDf4AKcvjbGL4OBRqQSLHXHm+05KZDhI9HIfqo60KYS4ZtxzObj5cnx8uRMmRKKnwaxZNDreQzj8Rhp82PT5sfiG1ySK+8q+1q4JjksxlfcIasoamyu66kbMBq58prXaHRvvfW3xWw7hsFzz62YP8\\u002f5mzeGwbZt82\\u002fcqCkrt3zgnZtXP9QJdz6fNWtW1KxZve4IH9uiOGlNivy8jv3Gp83KInsS7r3HWRvC7lP2C2Amm\\u002fLj1dLLjufHnT7DPlxuHY4iuR37zrZ8DgBMEn+J30t+jKHdMzjAJT5ldNLrBLX1QpVaCwBRYd645B0AEAhYfIzfo\\u002fekTU8JwzXhsFm0T97caJ42BYCYSJ9H7unq7ZJ5o9qYmzb33f\\u002fOyeRqAJifGv3kA4vME9YAsHhe3P23zzE+\\u002fnnHxb4vH1lOXPgzF0s93FnffXTHgtkx5gXsmAzK4nlxUeFOrmsxdVII340JAM2t4pyCOnwzAABcvVHd0SkHAB8v7sR4C4d0d2yY\\u002fvqzq6anhOGqpTMZ1FeeXMFh0wFAbzBk51pIZ5gMbrP53+7LxgfzU6Pvu322edcADpv+2tMrw0M8TRGLnLLwI8LxL2xctJ8x2w4AputAer1h87ppTAYVAMgk4rzuCkI13XeBuJR5\\u002fmEnV997Z0wKlei0y64sMvXOmJSMNfe9NDlt0Nl2AKAR3b3pUwJZCykEHr6tj5k+764I+ZtGdMc3OFuj7Kxc24qPDsqwLbOzjOf3PjZwaLOJBBafsRrfMFherNtD3N+O8PiGz1yHb3N59q8Nta65puNljc45G\\u002f\\u002fY83zc1p+mvsqjWLimziOzJ\\u002fKiZntOYpP6r6NqYz6IbWg9jyuCls6vXtsvbJPgG4bLis0zHn1j3Ytf3L5gjT05QWSQTpwoqKm1fO6wbu3koci2G2EYtnHjNHy0W1UV+ikcEp5mxVJ0hq57xG2jEBimx2q9wtRBfgRFsOcSMbLxcbn4TIfacm7Efk6fIY4LrsPRQqCqPNfyhfHx8oA3XTjb7iqfMkq4OwGR1LUaWwUSGwOb9OvmVZONOVycuTO6+mtrNDpcdTNhh+z0hRLj4\\u002fusDEm6ckkSjUYGgHah9FpODb555Dh94R+5J83iChwKBAK2eF688fHRjILejV1M5dHT58db7AJvA41Gnp7S1cOrtd3WsfUgNpuGZpGp5vsdN88wbzIiEgmb1kzBR+1m\\u002f8KPCMe\\u002fsLFmJXp8vbkMeteFoondWXgA8PHquvlUKJKZgi6FR6W9OnXhhZu2PZcyL8Hde4BbaA8\\u002fJmd9eOI389Zeu\\u002fnhV6cuDGDh77odAwygb5Fbvc12bBvP733M4NLmJAfkh\\u002fI\\u002fwjeMS\\u002favjU7FqVbJr1q9vbdPIQiCDJ3MjOJ\\u002ffr8kdmycA8T1\\u002fbXnKj4EAAA0Gnnz5pn4qFOlpIRaO2Ntbu7EhxDr7EydA0CrstT0mEXq1X\\u002fOGgJGohG7zrb0Bm2jIq93+whgkvhxvOXGxwbQH254RakT957EMmsryukzxHHBdThalIszDGAAACJG8qBG4JtdiYt8yk7r2ziehQV58LgMUadc2CF79s29T2xbFNhd6WJA5qVavoWKTCZ68FnGMUXF0l4DvGTn1RpHUwkL9vT34Zk3mdCo5MhQr7yiBgDIKai30W93mDl34dks2pwZllfgEFm6IOHPfZkAkHGh9PH7FlJ6jw4qV6iN9V4wDNLTulLzA+LdPSyPqSS6RYPYbAqKG40PvDzY1nqy2yioYg87F35EOP6F9fLoVf+Uw6bJFWoCAfP26ombsvAq1WBy+sPGg8bcFj9tW\\u002fy0dqXsWmtDvrClSiysk3YKlHKRSqnSabV6HQHDSAQilUhkk6lcKs2DxgxgcQNZ3EieR7y7ty9jBLrL1UqPXWr+P9PTdWGnyH3GrzeAvlJ8oKJzj0RTp9XLzZs2RvSczGBAKBX9WS7eI9M00Uke3vSpSfyHqESesbWic0+l5J9OVbnOoFIqhDvLey5E3Rx+GcOI0F0LfnXo0RrJ4YrOvTJtc9\\u002f5gB3LbOd87Hxf9qiRHC7v3CPR1Gj1chqRz6NGBrOXBbIWGFvtee\\u002fQXXN\\u002fXdgpqaY+R\\u002fCFUFlgAAOHHDzN+zUOpevCW7\\u002fv3aLr7R+VinZEcjcmez5lCrYprhd2\\u002fCRQ5ukMajY5MJSzIop3K4Z6DyBDoFN5Hh9ySXvrT\\u002f1QuX\\u002fHjHeONl863HShXSXyorkv9U1dGzDffPi1UknN8eYr+Z3lrSqh3mDwp3umeU81TSPXKTdf+j9\\u002fuucXKc\\u002f1zBoAALZlvdWm6vh9+ts0IgUAzrZlv1f0s6l118z3mKTB1MK2cz6nW7MONp6rljUAQDgr8KaABdP4PTX6DjRkfFex9\\u002fcZb51uyTrcdKFVJXSncCe5RW8JWckhW+iLYJH97932OgS7l8cAhr8bzhxqPN+iFPCpvMU+00OZ\\u002fqZWo37\\u002flp3sXs9X\\u002f228UK9oUerUPAo7lOmf5j1llsdEAOhQi2+7\\u002fOL7E\\u002f5zsPHcFUH+JLfoZ2Pv3N+Qsbv2uCfV7YmY2yJYPb0NkKGTfaEMHxq1FDLV3l8vXDhe2Fgr0BsMbnxWzITAO\\u002f+zyDeg6wa4krz6Y\\u002fuu5WVVtzaK9AaDfxB\\u002f4epJ6+5Ixbpvyd3368Vv3zv0\\u002fAcb5y7r6V955t\\u002fcd57eef+zy9fe0ZOYPvXPjUM7M+uq25RyNY\\u002fPCov2XbBq4uzFvUp95mVV7\\u002fguozi3Tq3S+gfzF61JXnv7TILZDdMGg2H\\u002f\\u002fy4d3HmlpaHDw5uzZG1KaIyv2Qycpr5eWFXVho8CAMDMmZE8Xk+HzaHA5dJZLJrF4WRlMpc7oXNlV9p+alEWx3CXhLCm082O4XszFIj+LRAdMj7BAAtmTe09gVX+jKQKyTnj4\\u002fOtX60N\\u002fIhK7P+4ekhN89hSLb0k0bQAgFBVvaP6vlle28JYswnd5wvm5NqOevn1ckmGWiddE\\u002fQxvhkAhmCGOC64DkcFPXT1E9cZtCeb3p\\u002fgvo5HCaAQGL3rt7gKV\\u002fiUUcLdCYhEwoN3zn3708MAcC2n5vYHf5iaHLpicdKMyWF9xw61hkIhBfp3HWT0RSF3fVK42iCFpV39lMOC8QPQmeO7dY0i1dJm17XB4eHchY8O9yZYuyg\\u002fNEIC+TGRPsVlzTK56nxmOa44ScbFEmPdkqS4AD8rlxNsMy+MY83gNpuq2nbjAxtr3o3HZLNokt6ZevvZs\\u002fAjxfEvLJtNM39KJBAAgM2imW+Bpio9Or3eFHRlHjTmkqCoJUGWr9+4Gh\\u002fG9Pn+X6t0ogLh953qCnwzAADktH9eIvotmJ0e63anWtdZJPpFpmma6PEfX0aq+WQlot871RUBzAWBrIVtiuxK8f5OdcXCgB+NrWxKSDhnjcGgy2p7l00OinG73fRCXCHyS80vCFWFAcwFgeRFfecD9i0z2DEfO99Xv4o7fs0RfO5GjQ3nrAXApJr6ZsUVDiXUlHC3\\u002f70DQIPs3LW2d73p0yJ5m1S6jibZRQapp5CXne\\u002fd3I32T\\u002ftm22skR660vMKiBIZxVhExaqsi+0b7p+2KnFTf913zOA8ZvQwGjUR5CR91YW8Wft+sFEznJ5IJpMuCvB8q94vUkq1hPZVz9tSdvNZRlOwWO52fqDXojNModKrNwUsBgEGkzfBIOtN6rVrWGML0M72qQlpXJ29Z4D3VmHEGgGS3mHeSHhFrpH\\u002fUHqmRdR3IDYI98\\u002fmx8sCe+pMBdK8lPjMNYMgSFr5e8F3fkUXfLfq5TFKT6jFxNnVSfmf5kaaL1bKmjyY+bj6NDfa\\u002fd9vr0KTf5fm16uCuuuOBDO\\u002f1gQvFGtk\\u002fjWfdyL0u5IPdf6tf9qznv+pO\\u002fFT1dwQrcKlvKgA0Kdqvi0oCGd7GhLvR9sp9fnTPJF7kZUHef0t+r5I1LPVNPdh47rPSPz9LfsY0GTJEtBpd7mW7fj1dX6dQ9tQd2+uq2mInBq2+bQaGYdWlzdcvlT\\u002f22lrTNH\\u002f9eC7rQlnKzIgZabE6rf7iycLtHxxWyFS3PdR1iGKn3T+c\\u002feHjo5Hx\\u002fss2TMUwaKwTXr9YHhTmaZ5wP30o5\\u002f3ndvsH85esTaHQyHlXq7Z\\u002fcLjges1Ln9yKdR\\u002fY\\u002f\\u002fzp8Z3bzwSFeW64e464Q\\u002fb3H5fdPIYka5ObV4cPdUtODsaHhgCXy7CYcNdqdQYDDO+p9ihmAEODPKdBngOAedDCPakR7tQQOpFLJjD0Bq1C19mhrq2VZnZquvrAAUA8bwWLZLkbXF\\u002fxvOWmNGK7svyPqq0x3MV8ahgJo2oNKqVOotAKpdr2CM68YKa9SXwH0Yjc5QFv7ql5VKNXAIBU03qk4XUakeNDj+eQfagEps6gUevlEk1Lh7pOrOn6MfKl97r0Zc7pM8RxwXWIozfo1Hq5Utdpimj1KrlWSCEySdjgarca1HqFXCvseQ56mVZAITDIBAtX4i0KY826JthhLIleIj5eIj6OnwIwCoHOILl70iLDWKlh7DlEbMRyzq7wKY\\u002fYmx9j0tMSqFTyJ9+d7BDJ9AbD5WuVl69Vcli0xfPiVi+dGBzAx7+gDx6HPoh8sbD7vsITZ4tMY2DaILb0CzpSnLvwPO7QXvO3aGlaQnFZMwAcPV2AS7gfPV3YNU2f4VLNGQxQVNaUlVNTWd3W0iYWSxQyhVqt1qo1Oo2m\\u002f57Rg9tsTGl0N56t\\u002fl8cNt12wt3BhR9BDn5hKb3L1hvZmaxHnIJC4HjRJwNApfhviwlcnUFd3rnLjRpjGuaUSw0\\u002fWX+vQttu6nZt1KEqXRT4M48SaXx6tvHRJvklgTKPT0sEAC96ihc9RWdQZ7W9SyN5hHHWmL\\u002fWXLsyz8Z8wI5lNrI9H\\u002fvfV79qJEeoRN6igJ9MfdUNBp35nZj2v3cAyGp9e47fJ8Y3CADgaTDPgNv53k0vyRV8WSL6LYq3aZLHk6Y2lU6U1faOJ33SPL8vTct8ueWlGsmRBtlZf+Zc05QjqKB5uVxd4M990o\\u002f7CL4NoF70QZP4SyZlQpzPAfO4QlPcLN4uVl3S6NoIGJ1BjvFgrvdgrbdY9w8DkkR1pUn8jUyVozOIyQRPNm2GH\\u002fcRGgm\\u002fAZS3P9AhP2x6SidHJfgeM2vHM4BWKDsklP8jU+dp9R0EjEIieDAosTz6Qg\\u002fmTfipB2JAb3BAnL42lNqqps6v5JpChabUYNAAQH7TEvMJfDkPBPCeNY+4iDZVxxcpz7FJDAC4JSj9sesf7q0\\u002ftcxvlg+t6xftgcgNdCKVSujKHd8SnH5P5uvHmi+ZErgLvKaeab12ujXrrtBVxggAnGrJAoA0757zEBaJkcSLBICjzZetJXDt0e98isRVe+pPJnIjXk98gEIgA8CWkJUv5X31U9XfyW4x5pnxYnHVfyc9FdodeTn\\u002f62vComJxVQwHvxlYY+d773cdGtlenlal8K\\u002f6kwEM70+TnzbO6pbgJY9mf2A+B7D7b\\u002fWr3\\u002fUMABmtWRwy8+NJTxK7L6bqDHqNvteBHAkjPhOzRaPX3nLp+bNt2T9OfcWbxpfpFEebLqn1GuMHNCbpdPqz\\u002f+aeOXSjPL9BLJKTKSQenxkW4zctLXbh2hTcxFdOFR3eeaUkt07aqWDz6LGTQtZsSU2cGmY+jUKuXjfxpeBI728OPZGXWbl7+5mS3Dq5RMnzYE2YHn7Lgwv8Q3p6wzRUt+\\u002f69nRFUWNNWYtWowOAB1b8t2deABvum7f1qV6bxGMbvizJqd155WU2l\\u002fHPH5dO7M2qr2oHg8HL321WeuJtjywynxjsW2bn+ubdQ3VVbVufWHLz3XNMQa1GRzI7un7opVV0BoVK69qubt02\\u002f66lHx3Zc22gCfdTB3M4boxP\\u002ftxG7O4JpNPpNWb1JMUd8s9e3Z+YEvLOD1tN07z\\u002f7K5TB3Muny6ekRYLAK2Not0\\u002fngsM9fx890PGRbp12\\u002fyHN3xlmokTlZW14EPdIpw9IJlF1k4rqVSStSbEJkO7srxdWY4P9xbEnDLL60F81Log5tRITlqZ+JTxqUwruCb4s\\u002fckAADe9Fh8aCh5UMPXB3\\u002fxb8PLneoGY0SpE1dLL\\u002fWeagCcPkNzLrgOjze906luUOlkar1UrZdp9Pg8TL38+o\\u002fl6wGAgBEpBCaFwKQQmRQCY6Lb+jD2LNzEAJAl+K1GmqnWy1R6mVonVesVxkS5iVwr\\u002fKl8AwBgQKAQGRQCg0JgUQiMMPasSe43m09p4k2PXeD79LmWL9S9b7M2Y1Dr5Wq1XKSuLxOf5lECFvv9nxctGj\\u002fVsHCFT9nRUx3EZH5q9I5v73nornmm2s1iqfKvg9l3PPzjS+8daG7tuTZlEY06mENVmVyFD9nkUp1tnbvwpJFIdy6cE2scMjTzetf4qEZtAklOfh0A0Kjk+alW9y9nL5VufvD7bU\\u002f\\u002f9v1v506dLy4oaaxr7BB2yKQylVqtNfTaH1o2uM1GodQYH1B7l8HBoVFttTq+8CPLkS8sgWBhz4kOQ12KQtumM6jdqD3fPh4lCgCkmvqeiQAAIIi1yJTdBgA\\u002f5hwAkGhqeqawz\\u002fDMx\\u002f731S8ayV2jlwpUeaYIhhFJdndwwAlgpfVk2wEG19+cRKABQL7w26KOn3HZdgColR7X6uVRvE2mbDsABLGWAECj7HzPdCPKk7kRANplf+GOaAEAQC+Q7QUAT9Ym82ib9I+CpuXtsj0abSuZ6GkwaCSqzCrhM6WtWwyWKlF2KI4Xt2zqVJwhEGhEjKXWNQlkewubVyg0+GoDbvR0L9ZmN8YyOrlni7JGo2svbrm5UvAfkeKERteCAabXK1Ta6g75YbHyLH7qgRjoGxwQp68Nra5Dpa0lYiwmZaIxwqQksanTTP+opMBeL3AZ872mGLPtAMAk0Zf6phrAcKk91zQBj8w2ZW8BgEGkhTL9BKpOYy1OAJjkFu1O4WS0ZpkieoP+bNs1T6rbBJ6tlTZEjjVfBoBbg5eakrk0IuWW4CV6g\\u002f5I80XzKed4Jpuy2wAwzT0BABoUlssyWGTne+93HRrZXp7Lgjy9Qb\\u002fcd5ZpVu4UbprXFNP0Rnb+LafgUThyrbJYXG2KEDGCqV+\\u002fUSQ7CADIBJI3je9JdfOm8QHAk+pmAINEY+2se9TraJc+fes37z\\u002f555VTRYJWMYaBUqFurBGcP5qXfb7UfEq9Tv\\u002fhMztf3fbzldNFKqXGw5erkKkvHs9\\u002f5rZvf\\u002fzgX\\u002fMpjRqq20\\u002fsu\\u002fbs7d9lnSuh0cl0JrW9ufPk\\u002fuxH1n5WW96TchV3yJrqhAwWLTqpa88TmRCQODXM9M830N00sbmWBtHrD\\u002f369esHyvIbdDq9Qq6uKWtp6j0U50CX2SkUzCCnDgAAI\\u002fpJREFUMtXZI3n+IR7m2XYAMM+2AwDPnWnKtgMAg0UNjfIRtIoN+oFt\\u002f24eLLlEVZxTZ4oQiQRad9VHADhzJFchV6+5faYp2w4A85ZNAIDMMyXGp5dOFel1+hWbppkWie\\u002fFWbBqYvfkziQSWf02cbmDPEIbkM5OBT4EAAB0s5WG9MubHssgueGjllAIzJme964IeJtEoOLbbFro+2wCbyU+OtL41NCNId+k8G81H6\\u002fSGjdKYCw3HR\\u002ftzekzNOdq67BRntusKOxQ18i0gr7ZdnN6g06pE4s1Te3K8kZ5rrHwTl9tyvImRb5AVSXVtKr1ckvnJl0MoFfppBJNq0BVaXwJfopuLYqievn1vvc6WyNS1++vfcrGDIfaiH\\u002fKthJqyEDRaZSNa6bcvHpK1o3qf0\\u002fmnbtSbsw8nrlYmnWj+u3\\u002fWzfJbExFpzAOKAoAMRE+sVG+vRstCO4ujecKRvXCG7FZtNQpERkXS3Q6\\u002fclzxetXJBvjxzIK9QYDAMydGUWnWT5A+e2vy9\\u002f975zxMYdNn5YcGhnq5e3FYbNodBqZRiX\\u002f9c+1Qyd6cmFOZMqkq9W63i29GCvsWzSCC+9Ew\\u002f+FRYYNjehOwEgSTc8plvExjdTTcczIPHkNABQiBwDUOol50B7DMx\\u002f731e\\u002fEtzvz2h8+GT9vV70yaGclYGsBcRB3p8IAOBOdULXADKBVSraUSD8PpyzFpdtB4AOVSEAnG96GhcHAJVOiA+NED5zTZ3obZW2RqLKZFOnmTd1Ki+odU0EjOHOWGUKipUXq4UvYhgpxO0ND+bNGEYCALHyXKXgyU7luTrRO0Fur\\u002fTMAgAA6kXv8ZmrA3kvkokeACBVZZW3b9Po2hs6P4rw+MZ8Sj5zNZ+5GgDapDuqhc+ZN\\u002fWhL2+\\u002fV6q6TiQw\\u002fblPujNWkIleBtCqtHWdigwW1fxSysAM4g0OiNPXBouaHOO9EwD0Btm1ungACOV\\u002fQCdbvXDuOgIYXuZPgxm+AFAnbzZF1HrN0eZLWcLCBkWrVCNX6tXGLsx6g4GIYQBAwAjzvCbvrT+VJyo39om+ISoVqsU3By4aaN1wp6iQ1gNABLvXD3EEKwgAyiS15sFwVoD5UzaZCQBSrdW8VV92vvd+16GR7eWpk7cAQCjL33yaYCb+MNjOv+UUtwUve1H85TM5nyTxIhd5T5\\u002flObFvj3VW9+UcKoFMIHctgHEyrcHWweToZdAbXn\\u002fwl+IbtXQm9Y7HFs9ZNsHdk63T6ZvrhFlnS+KSQ8wn\\u002fv2LEyf3ZzNYtMffWT9zYTyBSNDp9OcO53724p7d2894+rmt3DzDfHqtRvfxc7vnrZx473Mr3DxYAFCYXf3mw\\u002f\\u002fraJf++smxF7+43ThZ7KTg93+7H7r7xQPAE+9uCInyMZuTZZ+9tKetSfTU+xtnLIxjsGgKuTovs5Lr3uvG1oEus1NUljTrdPoJU0LxDb2plZoje69dPVtSX90uFSuUCo2xW7reYCAOZHd0x8MLn7\\u002f3pydv\\u002f27C1LDFa5NnL06gmOXxAaA0vwEAXn\\u002f0d\\u002fOgUYdAanxQW9kKAGHRvVZ7SIS3+VNnsVjOxYjBGPxBmp2EQplYbDnh7u3NxYcQ6yLYc0NZqY3ynAZ5jkBVIdY0y7RCrUGp06uJGIVMoLPJ3h7UsEBmSghrJplAw7\\u002feDkSMPM\\u002fn8US3NUWdR5oU+WJ1k0ovJQCRSmTRiFx3arAHNSKIOfjjt0GjEJgzPO9Jdt9ULb1UJ89uU5YqdCKlToIBgUJgMMkebpRAb1pMIDOFT7XrThqnz9DEZdeha9IbtBktnxZ2jzrAILkFMCaxyF7m9W0MoNfp1XJdh0BV1abs6vui1svOtHy6LugT02TDacQ\\u002fZZRwdz4MgymTQqZMChFLFPuP5Py5N1MmV8nk6pfePbDzu3uZTv2xdOuuoxIb5fv4\\u002fQt7N7q6Ub3wJksXJGRcLAGAY6cLTAn342eKulrTEkxTmquobvv+t\\u002fPGx+tXJN+\\u002fZW7fzuYspjM3FXNsVtfvukhs63QUN9SqycguvNMN5xcWGTYkAj2Ms6a886\\u002fr7R\\u002f7MWerdZ0Fwu+JGDWSux43JY3onCt5wzMf+99Xv\\u002fi0xGVBfxWLfq0SH2xtybre\\u002flEsb0u0222DG4DUfFjXQZOoa24o\\u002fgsAQlWRzqDCXQBQ66QAEM3b3Pdvsciucm2MSOC4MZYKZPvapbtwCfd26S4AcGeuIBJ6Uh71ovcA9AHcpz1Zt5qCHNrsILcXK9ofbZX+5s99gth7gFkmJTGM\\u002f7HpDkUWdbIf55GajlfEygvmkw2IUP6vVHUdgBDl+SuLmmIMYkCikUJp7H7SIrYN4g0OyFCsjVGK3LtEpnGIToVOZXwq0yqevvFJjbxpGj9htf88DyqPQaT9VPU3LnO90Hva3vpTp1uvGpPOp1uvAsACs5oqw0mhU5IwIoPYKxPBJjMwwKTaXikhHnnwm5BJv+\\u002fdznUI\\u002fS2PQqcEADqx1\\u002f4N9zbt\\u002f1tOEcMJ+W7Ki7vrTpxsycwV\\u002fe+7ij3rAxfeFLjA\\u002fGKDqdoMABAG9TMx6pw7kld8oxYjYG\\u002f+cHdcd\\u002flsIpHgH+JhXvUFAMQi+V8\\u002fnAWAR99YN2tJomnKeSsmajW6j57d9esnRxeuSab3PjaOiPd\\u002f+v2NplFA45JDNj244OvXD9y41E\\u002f1CXtUlzR\\u002fvv9RU2qezqBMnRdjPsHgltlxMokSAJgcW521pRLlk7d9W1PeOn1ezNo7Uj28OQwW7cePjpQWNOAn7U\\u002fMhMDvDz6264ezJw5cz8ms\\u002fObdQzffPWf9XbNNq924POu2pOKuRgCAXxDf+EAhUwEAjdGrBxXD2WvGyGD9HmGxWIEbRMrprlypwIe6BVm5lwKxhoiRApkpgcyuY6ohwqeGzvJ6AB+1jxct+uGYU\\u002fiok1CJrGjuomjuInzDYDl9hiaOrEPn2hL+Jz7kmKX+r+JDDjjZ9IGpYvt0z63J7psINouztyiKDtQ9baw80yjPFWuaOORefQum8G+bwr\\u002fNPGLbdM+t0z234qP2GcFP2dY6QhzEYdPv2DB9wazo+5\\u002f6TSxViiWKc5fL0q1kYAcnNtJ3\\u002f+EbAFBW2Ypvc3mjeuFNpiWHursxhR2y4vLm+saOAD+3usaOypo2APDx4kxKDMK\\u002fAAAATpwtMnaBjwr3fuSeBRb7KkmkXefJThcS2HUEaRo9tS+5Qt0hkuGjADDSCz90huELiwynSR5PKLStpaId5Z27KQQunxY\\u002f3ecNLiUCP53FLXgQhms+9r4vO9BJnpM8nkziP1wnPV7U8WuO4HOlTjjR4zH8dMNFo5dN9X5Fo5dkt32Y2fLaDJ+3zVvJBCYABLDme9AmmMddjSdrk0C2Tyg\\u002fHOT2uim3rtOLRYpjAODJ3GSaUq1rkqlzAMCdib\\u002fVkUWdCgAGg0aqvsGlzTZv8mBtxNUDZFASAUCnl+gNKsKgblPokB8BAC59rinb7hSDe4MDMhRrY5Qy5daNlDoVmGV1\\u002f244UyNvWhsw\\u002f56wtaZpiGbVmYyCmb7hrIAL7TkPRtxsrEgTxQ4OYHjjJhseDCJNa9DJtAomqSc3J9HIDWBgmUUAwDSwoSP6fe92rkPob3moRAoAiDVS86BarzF\\u002fav\\u002ffchZ3Cvf+8JvuCl11ru367rrjP1X9LdJIzP\\u002f6OHThWD4ATJ4dbcq2W3PtbIlaqWFx6bPTuzLXJvNWTvzq9QPSTsX1i2UzF\\u002fU6pFyyYYop7WsUGe8PADKJUq3SUmzWdezXtLRY2x3hB7fMjjNm8IVttu7\\u002fO\\u002fDbxZry1nVbUu97ZpkpSLQ0fhKO3FK1Ur4X54HnV9z9+JIzR\\u002fN2f3\\u002f2h4+PdgikpjkzWFQASF0UHz\\u002fJ6qdsLEEj7l3sRW1WCN6JGL3T+uba2sT+\\u002fm74qFP9\\u002fU82PtQtJqanTBaCIONNkyLflG2P5y2fbEei3JseG89bcV24y\\u002fi0VVmCS7iPE+Oih8LI8vd1WzAn1vi4sbmzd6OjJk8MIRAwACgsbWxoFuGbhxGJ1LMtyRW9ThuscZ2FdwSBgC2eF2d8fOpCCQCcuVhifLpkfry1c66mlq4tISnW39o0uUX1+JCTxEV3HTM1tXTW1At6N3a5nldrrY\\u002fFyC78UBvSLywynNoU1xtl52f4vLkh\\u002fNLq0COzfD8yr40+IN2dvq18JYaXE9+XERGjhrBXLA78H5PsWyk+gGsdzvdOJ3mGsJdFcjeGcdbUSo8XCn8wb3WnxQJAq+KaedAFsanTaKRQvUEulB80BQXyv\\u002fUGFZ0cyaImm4JydaHxQU7D9Ku1Ieb\\u002fchqmG5u0OvxlUUaf8iZmXeYHef6v0JQCAJs6Bd\\u002fgmMG9wQEZirUxStWaVY8BgEpZAwAEMroybsbWWR4TTRPoDPoGeYvpqckC76kyrSJLWHhNWKTQqRaOUPd2AIjmBANAmbSnghZ0F5OJYAWaB53F9nu3fx3aFsTwAYDq3uOX1il6zcdZf2ugKATyAu+pnyU\\u002f401zN9bQH89qypoBIH5yCL6hj5qyFgAICvcmmNUBNyKRiEHhXgBQVdzrGwoAob1LlEB3MhpsFnW0U0S8Pz7U2+CW2XGhUd4YASvOqdVbf4+1Fa0AMHtxT65fp9PXV7X1TAHA4tAAQNK7\\u002fkm19RFHKTTyotXJX+x+yNvf7ejengMJ40WO3Myqnkn7CAzzgj4zr6vstTzOYqNyy+XLVrufO8WJkwUlJVY\\u002f8cmTQ\\u002fEhBEHGjXJxhulxvN0l0fnUnv2GUic2axlH8D+xyFCQK9TGBxxn3wjmyWctmB0LAHq94cOvjmmtH7sMNRqVbKrJXlFtV49111l4B5nqxhhT7Wcvlxmf2ugcTe3ut2I+1Kq5I6cL6hs78FEnCfJ3j+oe6f6XnZd6N3bZdSALH+o2sgs\\u002fDIbuC4sMpxZFpgH0bHKgARzdtxAwEo3Il6hr9A6P8eg4J70vg0zTK92DAQEDYt96MiPy3lM8n\\u002fWkT8oTftsgO2MKBrPSyQRmiegPqabXTeVKnVA3jMtmDw\\u002fWzQDQLtttirRLd0Of4VJ1emMXPwKNHGbtHwFjmL8EAIgEFi7iOOOSkAg8fINjBvcGB2Qo1sYoldGa1aHuOplR6dX\\u002fNl3AAJvhkWSM8KlcAGhTibonh7\\u002fqTkgsVTmf5zWZiBEuCnIuCXJJGHGOlzNvehiQJT4zMcD+rDms0nd9wVV69Y7aoxhgi32m957WOWy\\u002fd\\u002fvXoW3T+IkYYIebLsi6C+PItIrTLVfNp3HW37KHAQwtyl7DYBAwAgEjjEjhfpdiLDbC7i6AaYOx5Iixr3RfxjS6VNIrNQwAjO4Cj0PBfFxQiwa3zI5jcegz0mIbagS7fjhrHtdqdVqNzviY78UBgDazXi+7vj8r6T2Yp7Gqz5WMYlOktVF0+mBOzxQABoOhpaHX+QiBSCAQMPN7UOYvn8BgUff9eqGpvte3oEMgVau6rtpOT4vBCNihnZnS7gLrUony1D83TBM7UWjvakXmTp4qVHUvktNVVrV9+ukxfLRbWJhXICopgyDjWIe61vTYjWJvpwe1vme\\u002fTRmvR+wO3a2GGP26+3JTsyhtVsyE+ABK72LWeoPh6KmCE2eLjE+nTAoxb3WK+++Yc\\u002fVGtahTfi2n5pEX\\u002fnzwznmJsb06NShVmvzixotXK+Ry9XOPpps3OVdirP\\u002fV69UA8O2vZ6PCvHl2HKG6zsI7IjTIIzrcu6SipayytaSipaS8GQCS4gL8fXj4SbvFRPgcPpkPABeuVlTVtocG9Rxd6Q2GQ8fzPvnuRM\\u002fUQ2DzTdNeef9vADhxtsjfl3fXplTj3QYAoFCqv\\u002fwx43p+rz5l5kZ84R00sl9YxDadQS1UFmj0Mq1eptaJAKBGcphEYJIJTA4l1Fjf3GDQCVR5Gr1Mo5cptK0AUCs5TiPxyQQmmxxMJ3kaZxXKWVUh3nes7g7jUwJGYZJ9wzlro3m3wsDzCEHsxaWiP081bPNlzNCDTqXrmOz5PH4i6+xcZns45X0ZDPqDNavcqXFutBg60VOjlzXJL0g19Qnu9+EnHYn3TsBIqT7vH6+\\u002f43LLywsDfuRSwgGAQuRO8379YvPzR+tuDWItZpC8VTqhWF3dqri2PHg\\u002f05XuUvRgbmgQfSRVZSm1VTRSqEJTJlPnYBiZz1xrPpmxLzYGWKLviYF0gLD3U7YfgcAAHWj1Tu57Mtg3OCDOXxujFI\\u002fCfiT7\\u002fZkeSWwS80L7jXp5y\\u002frAhT60rgpy872mHGg483X57npFC5VAyRWVFnRWxnPDCzrxXSa5ZNZk97gbHSV6g2EqP4HdPVSmkc6gLxZXy3VKhU4pUIsA4GxbthuFwyDS\\u002fOlexjSxWq8pk9TKdUq5VinWygwGw+nWqwwijUGkBTJ9jPXN7ZlPOCtgS+iKn6v+efTa+ynucQBgHEH0tuBl4UPTw932e7d\\u002fHdrmQ+OvCZi3r\\u002f70Y9c\\u002fnM5P1Bi0l9vzfOgezcqeOw7t+VvOWs96g2Fr5qtR7KBwViCfwpXrlFnCwiZF++bgpaa\\u002fNT7RGRToTrvbxmDTAEBupZqiMbXNdLE+HCO4zA\\u002f938rK4qafPz1+5UxJ7IRACpXcXC+8dqHsg1\\u002fuDYn0BoAFKyfu\\u002f+3Sl2\\u002f9U1\\u002fdTqGScjKrCrKrE1JC8q9Vm2YSOyEwZkLg1XOlT9\\u002f5fUJKiLBNcuFYfliMb+7Vnr7qer1hy+IPoxICIuL8+F5suVR19VxpU53wtocWmKbh8BjPvLvhrSd2PLjui3lLkzx8OCKB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+              "                            \n",
+              "var gd = document.getElementById('148a2bd6-1148-4c15-b543-66a376231dea');\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
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-          },
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-            "ticks": ""
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-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
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-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-        },
-        "width": 640,
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
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-         "title": {
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-        },
-        "yaxis": {
-         "anchor": "x",
-         "domain": [
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-         "title": {
-          "text": "words per description"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "text = df_train[\"Description\"].str.cat(sep=\" \")\n",
+        "\n",
+        "# Create and generate a word cloud image:\n",
+        "word_cloud = WordCloud(scale=5, background_color=\"white\").generate(text)\n",
+        "\n",
+        "# Display the generated image:\n",
+        "fig = px.imshow(word_cloud, width=1440)\n",
+        "fig.update_layout(xaxis_showticklabels=False, yaxis_showticklabels=False)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"peril_cloud\"}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7I4oINQQEVHB"
       },
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+      "source": [
+        "<a id='supervised'></a>\n",
+        "<a name='supervised'></a>\n",
+        "## 2.&nbsp;Classify by Peril Type in a Supervised Setting\n",
+        "\n",
+        "In this section, we will train classifiers to predict the peril type (labels).\n",
+        "\n",
+        "We will follow two approaches:\n",
+        "\n",
+        "1. We use a transformer encoder to encode the claim descriptions,\n",
+        "    and then train a logistic regression classifier to predict the peril type from the encoded descriptions.\n",
+        "\n",
+        "2. We train a transformer encoder with a classifier head directly.\n",
+        "\n",
+        "Let's get started."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# statistics of description length\n",
-    "df_train[\"words per description\"] = df_train[\"Description\"].str.split().apply(len)\n",
-    "print(f\"Overall number of words by claim description: min {df_train['words per description'].min()}, \"\n",
-    "      f\"average {df_train['words per description'].mean():.0f}, max {df_train['words per description'].max()}\")\n",
-    "fig = px.box(df_train, x=\"labels\", y=\"words per description\", width=640)\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"peril_len\"}})"
-   ]
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-    "To get an impression of the most frequent words, we generate a simple word cloud form all case descriptions.\n",
-    "By default, the word cloud excludes so-called stop words (such as articles, prepositions, pronouns, conjunctions, etc.),\n",
-    "which are the most common words and do not add much information to the text."
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+      "cell_type": "markdown",
+      "metadata": {
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+      },
+      "source": [
+        "<a id='encoding_classification'></a>\n",
+        "<a name='encoding_classification'></a>\n",
+        "###  2.1 Train a Classifier on Encoded Claim Descriptions\n",
+        "\n",
+        "We follow the approach presented in Part I of this tutorial.\n",
+        "\n",
+        "In this single-language case study, we use the `distilbert-base-uncased` model.\n",
+        "First, we load the model and the tokenizer."
+      ]
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+      "source": [
+        "Then we define a function that applies the tokenizer to the column `Description` of an input batch..."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text = df_train[\"Description\"].str.cat(sep=\" \")\n",
-    "\n",
-    "# Create and generate a word cloud image:\n",
-    "word_cloud = WordCloud(scale=5, background_color=\"white\").generate(text)\n",
-    "\n",
-    "# Display the generated image:\n",
-    "fig = px.imshow(word_cloud, width=1440)\n",
-    "fig.update_layout(xaxis_showticklabels=False, yaxis_showticklabels=False)\n",
-    "fig.show(config={\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"peril_cloud\"}})"
-   ]
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-    "<a id='supervised'></a>\n",
-    "<a name='supervised'></a>\n",
-    "## 2.&nbsp;Classify by Peril Type in a Supervised Setting\n",
-    "\n",
-    "In this section, we will train classifiers to predict the peril type (labels).\n",
-    "\n",
-    "We will follow two approaches:\n",
-    "\n",
-    "1. We use a transformer encoder to encode the claim descriptions,\n",
-    "    and then train a logistic regression classifier to predict the peril type from the encoded descriptions.\n",
-    "\n",
-    "2. We train a transformer encoder with a classifier head directly.\n",
-    "\n",
-    "Let's get started."
-   ]
-  },
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-    "id": "Djm3pEdfEVHC"
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-   "source": [
-    "<a id='encoding_classification'></a>\n",
-    "<a name='encoding_classification'></a>\n",
-    "###  2.1 Train a Classifier on Encoded Claim Descriptions\n",
-    "\n",
-    "We follow the approach presented in Part I of this tutorial.\n",
-    "\n",
-    "In this single-language case study, we use the `distilbert-base-uncased` model.\n",
-    "First, we load the model and the tokenizer."
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-   "outputs": [
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-     "text": [
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
-     ]
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-   ],
-   "source": [
-    "model_name = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = AutoModel.from_pretrained(model_name).to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LwjhsMVsEVHC"
-   },
-   "source": [
-    "Then we define a function that applies the tokenizer to the column `Description` of an input batch..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "id": "ubdj3ThPEVHC",
-    "pycharm": {
-     "name": "#%%\n"
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-   },
-   "outputs": [],
-   "source": [
-    "# define a function to tokenize a batch\n",
-    "def tokenize(batch):\n",
-    "    return tokenizer(batch[\"Description\"], truncation=True, padding=True, max_length=12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "R55XqaTHEVHD"
-   },
-   "source": [
-    "... and we apply this function to the entire dataset by use of the `map` function:"
-   ]
-  },
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+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "ubdj3ThPEVHC",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# define a function to tokenize a batch\n",
+        "def tokenize(batch):\n",
+        "    return tokenizer(batch[\"Description\"], truncation=True, padding=True, max_length=12)"
+      ]
     },
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-    "outputId": "8b6aec49-17bf-49ef-ebf5-c357cc1a4398",
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-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?ba/s]"
+      "source": [
+        "... and we apply this function to the entire dataset by use of the `map` function:"
       ]
-     },
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+          "base_uri": "https://localhost:8080/",
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+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/4991 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4026dcdddca340c38ce39e52b42af5b0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ffc413f82c644528a2ec8705f6db4048"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ds = ds.map(tokenize, batched=True)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = ds.map(tokenize, batched=True)"
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-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kKoI6OCJEVHD"
-   },
-   "source": [
-    "Next, we apply the function `extract_sequence_encoding` (implemented in `tutorial_utils.py`)\n",
-    "which applies the model to a batch and extracts the last hidden state, which is the encoded input text."
-   ]
-  },
-  {
-   "cell_type": "code",
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-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4271601665344bbdb3f3fdd21c35dee7",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kKoI6OCJEVHD"
       },
-      "text/plain": [
-       "  0%|          | 0/312 [00:00<?, ?ba/s]"
+      "source": [
+        "Next, we apply the function `extract_sequence_encoding` (implemented in `tutorial_utils.py`)\n",
+        "which applies the model to a batch and extracts the last hidden state, which is the encoded input text."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
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+          "height": 81,
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+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
-      "text/plain": [
-       "  0%|          | 0/65 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/4991 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1493d02ebe5146b59c14a9d21523b0d1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9158ec9a01eb48cdb910b060ad1185a1"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ds = ds.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = ds.map(extract_sequence_encoding, fn_kwargs={\"model\": model}, batched=True, batch_size=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "idrcPcJ4EVHE"
-   },
-   "source": [
-    "We fit a dummy classifier (which always predicts the most frequent class)\n",
-    "to the mean-pooled encodings.\n",
-    "\n",
-    "We also fit a logistic regression classifier and evaluate its performance on the training and validation split.\n",
-    "For ease of use, this functionality is implemented in `tutorial_utils.py`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 872
     },
-    "id": "w95xtNAKEVHE",
-    "outputId": "605ac363-4839-4b32-b334-0b6eb3b3bd6f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dummy classifier\n",
-      "accuracy score = 29.8%,  log loss = 1.977,  Brier loss = 0.835\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.30      1.00      0.46       310\n",
-      "        Fire       0.00      0.00      0.00        46\n",
-      "   Lightning       0.00      0.00      0.00       123\n",
-      "        Wind       0.00      0.00      0.00       107\n",
-      "        Hail       0.00      0.00      0.00        18\n",
-      "     Vehicle       0.00      0.00      0.00       227\n",
-      "     WaterNW       0.00      0.00      0.00        67\n",
-      "      WaterW       0.00      0.00      0.00        38\n",
-      "        Misc       0.00      0.00      0.00       103\n",
-      "\n",
-      "    accuracy                           0.30      1039\n",
-      "   macro avg       0.03      0.11      0.05      1039\n",
-      "weighted avg       0.09      0.30      0.14      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "idrcPcJ4EVHE"
+      },
+      "source": [
+        "We fit a dummy classifier (which always predicts the most frequent class)\n",
+        "to the mean-pooled encodings.\n",
+        "\n",
+        "We also fit a logistic regression classifier and evaluate its performance on the training and validation split.\n",
+        "For ease of use, this functionality is implemented in `tutorial_utils.py`)."
+      ]
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+            "Dummy classifier\n",
+            "accuracy score = 29.8%,  log loss = 1.977,  Brier loss = 0.835\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.30      1.00      0.46       310\n",
+            "        Fire       0.00      0.00      0.00        46\n",
+            "   Lightning       0.00      0.00      0.00       123\n",
+            "        Wind       0.00      0.00      0.00       107\n",
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+            "     Vehicle       0.00      0.00      0.00       227\n",
+            "     WaterNW       0.00      0.00      0.00        67\n",
+            "      WaterW       0.00      0.00      0.00        38\n",
+            "        Misc       0.00      0.00      0.00       103\n",
+            "\n",
+            "    accuracy                           0.30      1039\n",
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+      ],
+      "source": [
+        "x_train, y_train, x_test, y_test = get_xy(ds, \"mean_hidden_state\", \"labels\")\n",
+        "\n",
+        "# fit dummy classifier\n",
+        "clf_dummy = dummy_classifier(x_train, y_train)\n",
+        "_ = evaluate_classifier(y_test, None, clf_dummy.predict_proba(x_test), labels, \"Dummy classifier\", None)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "5CxDvvqHEVHE",
+        "outputId": "0d095cf7-bcdb-4497-dec1-7f5ad07586f8",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Logistic Regression classifier\n",
+            "accuracy score = 83.9%,  log loss = 0.531,  Brier loss = 0.243\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.85      0.95      0.90       310\n",
+            "        Fire       0.94      0.70      0.80        46\n",
+            "   Lightning       0.90      0.93      0.91       123\n",
+            "        Wind       0.91      0.87      0.89       107\n",
+            "        Hail       0.93      0.78      0.85        18\n",
+            "     Vehicle       0.90      0.92      0.91       227\n",
+            "     WaterNW       0.72      0.34      0.46        67\n",
+            "      WaterW       0.45      0.76      0.57        38\n",
+            "        Misc       0.75      0.60      0.67       103\n",
+            "\n",
+            "    accuracy                           0.84      1039\n",
+            "   macro avg       0.82      0.76      0.77      1039\n",
+            "weighted avg       0.84      0.84      0.83      1039\n",
+            "\n"
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+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# fit a logarithmic regression classifier to the encoded texts\n",
+        "clf = logistic_regression_classifier(x_train, y_train, c=0.2)\n",
+        "_ = evaluate_classifier(y_test, None, clf.predict_proba(x_test), labels, \"Logistic Regression classifier\", \"cm_peril_lr\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
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+      "source": [
+        "This result is encouraging.\n",
+        "From the classification report, we see that the perils `WaterNW`, `WaterW` and `Misc` are most difficult to predict."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_train, y_train, x_test, y_test = get_xy(ds, \"mean_hidden_state\", \"labels\")\n",
-    "\n",
-    "# fit dummy classifier\n",
-    "clf_dummy = dummy_classifier(x_train, y_train)\n",
-    "_ = evaluate_classifier(y_test, None, clf_dummy.predict_proba(x_test), labels, \"Dummy classifier\", None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
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-    "id": "5CxDvvqHEVHE",
-    "outputId": "daaa6970-f646-46a7-c987-6bc10ea7147f",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logistic Regression classifier\n",
-      "accuracy score = 83.9%,  log loss = 0.531,  Brier loss = 0.243\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.85      0.95      0.90       310\n",
-      "        Fire       0.94      0.70      0.80        46\n",
-      "   Lightning       0.90      0.93      0.91       123\n",
-      "        Wind       0.91      0.87      0.89       107\n",
-      "        Hail       0.93      0.78      0.85        18\n",
-      "     Vehicle       0.90      0.92      0.91       227\n",
-      "     WaterNW       0.72      0.34      0.46        67\n",
-      "      WaterW       0.45      0.76      0.57        38\n",
-      "        Misc       0.75      0.60      0.67       103\n",
-      "\n",
-      "    accuracy                           0.84      1039\n",
-      "   macro avg       0.82      0.76      0.77      1039\n",
-      "weighted avg       0.84      0.84      0.83      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jjCbT3UoEVHF"
+      },
+      "source": [
+        "<a id='transformer_classifier'></a>\n",
+        "<a name='transformer_classifier'></a>\n",
+        "###  2.2 Task-specific Training of a Transformer-based Classifier\n",
+        "\n",
+        "In this section, we train directly a transformer-based sequence classifier,\n",
+        "using the approach described in Part I of this tutorial.\n",
+        "\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 2 minutes."
+      ]
     },
     {
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+          "height": 172
+        },
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+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
           ]
-         ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1248' max='1248' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1248/1248 01:07, Epoch 2/2]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>623</td>\n",
+              "      <td>0.527700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>1246</td>\n",
+              "      <td>0.309900</td>\n",
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+      ],
+      "source": [
+        "model_name = \"distilbert-base-uncased\"\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
+        "\n",
+        "def compute_metrics(pred):\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(-1)\n",
+        "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
+        "    acc = accuracy_score(labels, preds)\n",
+        "    return {\"accuracy\": acc, \"f1\": f1}\n",
+        "\n",
+        "# train the model\n",
+        "batch_size = 8\n",
+        "logging_steps = len(ds[\"train\"]) // batch_size\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=model_name+\"_peril_epochs\",\n",
+        "    num_train_epochs=2,\n",
+        "    per_device_train_batch_size=batch_size,\n",
+        "    per_device_eval_batch_size=batch_size,\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    logging_steps=logging_steps,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "trainer = Trainer(model=model, args=training_args,\n",
+        "                  compute_metrics=compute_metrics, train_dataset=ds[\"train\"],\n",
+        "                  eval_dataset=ds[\"test\"])\n",
+        "trainer.train();\n",
+        "trainer.save_model(model_name + \"_peril\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6732WFTuEVHF"
+      },
+      "source": [
+        "We evaluate the model on the test set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
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+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Transformer-based classifier\n",
+            "accuracy score = 84.7%,  log loss = 0.539,  Brier loss = 0.237\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.90      0.95      0.93       310\n",
+            "        Fire       0.88      0.80      0.84        46\n",
+            "   Lightning       0.94      0.94      0.94       123\n",
+            "        Wind       0.94      0.90      0.92       107\n",
+            "        Hail       0.94      0.94      0.94        18\n",
+            "     Vehicle       0.93      0.93      0.93       227\n",
+            "     WaterNW       0.00      0.00      0.00        67\n",
+            "      WaterW       0.35      0.89      0.50        38\n",
+            "        Misc       0.71      0.72      0.71       103\n",
+            "\n",
+            "    accuracy                           0.85      1039\n",
+            "   macro avg       0.73      0.79      0.75      1039\n",
+            "weighted avg       0.82      0.85      0.83      1039\n",
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+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "predictions = trainer.predict(ds[\"test\"])\n",
+        "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), labels,\"Transformer-based classifier\", \"cm_peril_transformer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
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+      "source": [
+        "The performance is comparable to that of the logistic regression classifier, with an improved Brier loss and accuracy score.\n",
+        "It appears that the model struggles to tell `WaterNW` apart from `WaterW`.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fit a logarithmic regression classifier to the encoded texts\n",
-    "clf = logistic_regression_classifier(x_train, y_train, c=0.2)\n",
-    "_ = evaluate_classifier(y_test, None, clf.predict_proba(x_test), labels, \"Logistic Regression classifier\", \"cm_peril_lr\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8SonrXfCEVHE",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "This result is encouraging.\n",
-    "From the classification report, we see that the perils `WaterNW`, `WaterW` and `Misc` are most difficult to predict."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jjCbT3UoEVHF"
-   },
-   "source": [
-    "<a id='transformer_classifier'></a>\n",
-    "<a name='transformer_classifier'></a>\n",
-    "###  2.2 Task-specific Training of a Transformer-based Classifier\n",
-    "\n",
-    "In this section, we train directly a transformer-based sequence classifier,\n",
-    "using the approach described in Part I of this tutorial.\n",
-    "\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 2 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 522
     },
-    "id": "eWXNvLgPEVHF",
-    "outputId": "c6a7f947-8f53-467f-b115-c23eaea70a71",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss. If Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running training *****\n",
-      "  Num examples = 4991\n",
-      "  Num Epochs = 2\n",
-      "  Instantaneous batch size per device = 8\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 1248\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CoYTxK22EVHG"
+      },
+      "source": [
+        "<a id='zero_shot'></a>\n",
+        "<a name='zero_shot'></a>\n",
+        "## 3.&nbsp;Zero-shot Classification\n",
+        "\n",
+        "There are situations with no or only few labeled data.\n",
+        "\n",
+        "Zero-shot classification is an approach that is suited in this case.\n",
+        "Zero-shot classification is about classifying text sequences in an unsupervised way\n",
+        "(without having training data in advance and building a model).\n",
+        "\n",
+        "The model is presented with a text sequence and a list of expressions, and assigns a probability to each expression."
+      ]
     },
     {
-     "data": {
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-       "    <div>\n",
-       "      \n",
-       "      <progress value='1248' max='1248' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [1248/1248 00:51, Epoch 2/2]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>623</td>\n",
-       "      <td>0.530700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1246</td>\n",
-       "      <td>0.304900</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table><p>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VLx2AElsEVHG"
+      },
+      "source": [
+        "<a id='zero_shot_demo'></a>\n",
+        "<a name='zero_shot_demo'></a>\n",
+        "###  3.1 Demonstration of the approach\n",
+        "\n",
+        "In this section you will learn how to apply zero-shot classification to perform the classification by peril type on\n",
+        "the claims data described above.\n",
+        "\n",
+        "First, we create a dictionary mapping certain verbal expression to peril types:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to distilbert-base-uncased_peril\n",
-      "Configuration saved in distilbert-base-uncased_peril/config.json\n",
-      "Model weights saved in distilbert-base-uncased_peril/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name = \"distilbert-base-uncased\"\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
-    "\n",
-    "def compute_metrics(pred):\n",
-    "    labels = pred.label_ids\n",
-    "    preds = pred.predictions.argmax(-1)\n",
-    "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
-    "    acc = accuracy_score(labels, preds)\n",
-    "    return {\"accuracy\": acc, \"f1\": f1}\n",
-    "\n",
-    "# train the model\n",
-    "batch_size = 8\n",
-    "logging_steps = len(ds[\"train\"]) // batch_size\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=model_name+\"_peril_epochs\",\n",
-    "    num_train_epochs=2,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    metric_for_best_model=\"f1\",\n",
-    "    logging_steps=logging_steps,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "trainer = Trainer(model=model, args=training_args,\n",
-    "                  compute_metrics=compute_metrics, train_dataset=ds[\"train\"],\n",
-    "                  eval_dataset=ds[\"test\"])\n",
-    "trainer.train();\n",
-    "trainer.save_model(model_name + \"_peril\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6732WFTuEVHF"
-   },
-   "source": [
-    "We evaluate the model on the test set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 961
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "JmrKLlMKEVHG",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "choices = OrderedDict({\n",
+        "    \"Vandalism\": 0,\n",
+        "    \"Theft\": 0,\n",
+        "    \"Fire\": 1,\n",
+        "    \"Lightning\": 2,\n",
+        "    \"Wind\": 3,\n",
+        "    \"Hail\": 4,\n",
+        "    \"Vehicle\": 5,\n",
+        "    \"Water\": 6,\n",
+        "    \"Weather\": 7,\n",
+        "    \"Misc\": 8})"
+      ]
     },
-    "id": "FkkgHZcaEVHF",
-    "outputId": "981b7946-b71b-45e0-c1a6-40ac534ae614",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss. If Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1039\n",
-      "  Batch size = 8\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IpkNFlZ8EVHG",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "We set up the zero-shot classifier using the `pipeline` abstraction.\n",
+        "By default, the `facebook/bart-large-mnli` model is used.\n",
+        "By specifying `device=0`, we use GPU support if available."
+      ]
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+          "height": 244
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+        "id": "kmmi1hs8EVHG",
+        "outputId": "bd4f77a4-d6ce-45d9-8511-59639b02eb33",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
+            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1cc54f2db67b43299c32ac3d80a14a05"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "545125171280483eb39001760dbe64b8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b7343b6eba3a4c6fbfb41de9a2f1c2a5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+          },
+          "metadata": {}
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       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "classifier = pipeline(\"zero-shot-classification\", device=0)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transformer-based classifier\n",
-      "accuracy score = 85.2%,  log loss = 0.520,  Brier loss = 0.219\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.90      0.94      0.92       310\n",
-      "        Fire       0.86      0.83      0.84        46\n",
-      "   Lightning       0.94      0.94      0.94       123\n",
-      "        Wind       0.96      0.91      0.93       107\n",
-      "        Hail       1.00      0.94      0.97        18\n",
-      "     Vehicle       0.92      0.93      0.93       227\n",
-      "     WaterNW       0.81      0.19      0.31        67\n",
-      "      WaterW       0.39      0.87      0.54        38\n",
-      "        Misc       0.70      0.66      0.68       103\n",
-      "\n",
-      "    accuracy                           0.85      1039\n",
-      "   macro avg       0.83      0.80      0.78      1039\n",
-      "weighted avg       0.87      0.85      0.84      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VR7Qe56REVHH",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "Then, we feed the claim descriptions of the entire test set,\n",
+        "presenting the classifier with the list of possible choices as the second argument.\n",
+        "\n",
+        "We use the test set directly, because zero shot classification requires no training!\n",
+        "\n",
+        "On an AWS EC2 p2.xlarge instance, the run time is about 5 minutes."
+      ]
     },
     {
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+      "outputs": [],
+      "source": [
+        "res = classifier(ds[\"test\"][\"Description\"], list(choices.keys()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "amIfcKCREVHH"
+      },
+      "source": [
+        "This returns a list of `dict` with the following keys:\n",
+        "* **sequence** (`str`) — The sequence for which this is the output.\n",
+        "* **labels** (`List[str]`) — The labels sorted by order of likelihood.\n",
+        "* **scores** (`List[float]`) — The probabilities for each of the labels.\n",
+        "\n",
+        "We store the predictions in a Pandas DataFrame and evaluate the performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "rrOMsvGHRPHR",
+        "outputId": "e58fc511-c36d-4b4b-b1f8-9a0487132b60"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Zero-shot-classification\n",
+            "accuracy score = 65.5%,  log loss = 1.043,  Brier loss = 0.463\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.93      0.44      0.59       310\n",
+            "        Fire       0.68      0.70      0.69        46\n",
+            "   Lightning       0.94      0.93      0.94       123\n",
+            "        Wind       1.00      0.84      0.91       107\n",
+            "        Hail       0.75      1.00      0.86        18\n",
+            "     Vehicle       0.89      0.69      0.77       227\n",
+            "     WaterNW       0.56      0.75      0.64        67\n",
+            "      WaterW       0.00      0.00      0.00        38\n",
+            "        Misc       0.25      0.83      0.38       103\n",
+            "\n",
+            "    accuracy                           0.66      1039\n",
+            "   macro avg       0.67      0.69      0.64      1039\n",
+            "weighted avg       0.79      0.66      0.68      1039\n",
+            "\n"
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+      ],
+      "source": [
+        "proba = np.zeros((df_valid.shape[0], len(labels)))\n",
+        "for i, sample in enumerate(res):\n",
+        "  for label, score in zip(sample[\"labels\"], sample[\"scores\"]):\n",
+        "    proba[i, choices[label]] += score\n",
+        "  proba[i, :] = proba[i, :] / np.sum(proba[i, :])\n",
+        "_ = evaluate_classifier(np.array(df_valid[\"labels\"]), None, proba, labels, \"Zero-shot-classification\", \"cm_peril_zero_a\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Biym_RIDEVHH",
+        "pycharm": {
+          "name": "#%% md\n"
+        }
+      },
+      "source": [
+        "On the test set, we achieve an accuracy of 65.5% (compared to 29.8% of the dummy classifier).\n",
+        "Apparently, the classifier struggles to correctly identify the `WaterW` cases based on the expression “Weather”.\n",
+        "Also, it seems that the expression “Misc” may not be the optimal choice, as it produces many false positives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "4EG2NJ_FEVHH",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "pred = [{\n",
+        "    **{\"pred\"+str(i): choices[item[\"labels\"][i]] for i in range(10)},\n",
+        "    **{\"score\"+str(i): item[\"scores\"][i] for i in range(10)}\n",
+        "} for item in res]\n",
+        "df_pred = pd.DataFrame(pred)\n",
+        "df_pred[[\"labels\", \"Description\"]] = df_valid[[\"labels\", \"Description\"]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3z9s2Z7zEVHH"
+      },
+      "source": [
+        "<a id='zero_shot_refinement'></a>\n",
+        "<a name='zero_shot_refinement'></a>\n",
+        "### 3.2  Refinement\n",
+        "\n",
+        "To improve the performance on \"Misc\", we introduce the following heuristic:\n",
+        "If the probability assigned to the expression “Misc” is highest\n",
+        "but with a margin of less than 50 percentage points to the second-most likely expression, we select the latter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "GbSjWVZtEVHH",
+        "outputId": "21346bc0-ba8c-43ff-8886-feeb542f59aa",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Zero-shot classification, refined\n",
+            "accuracy score = 69.7%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.77      0.62      0.69       310\n",
+            "        Fire       0.69      0.78      0.73        46\n",
+            "   Lightning       0.92      0.94      0.93       123\n",
+            "        Wind       0.91      0.85      0.88       107\n",
+            "        Hail       0.58      1.00      0.73        18\n",
+            "     Vehicle       0.62      0.77      0.69       227\n",
+            "     WaterNW       0.54      0.78      0.63        67\n",
+            "      WaterW       0.29      0.11      0.15        38\n",
+            "        Misc       0.45      0.40      0.42       103\n",
+            "\n",
+            "    accuracy                           0.70      1039\n",
+            "   macro avg       0.64      0.69      0.65      1039\n",
+            "weighted avg       0.70      0.70      0.69      1039\n",
+            "\n"
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+        "def select_misc(row, threshold):\n",
+        "    return row[\"pred1\"] if row[\"pred0\"] == 8 and row[\"score0\"] - row[\"score1\"] < threshold else row[\"pred0\"]\n",
+        "df_pred[\"pred*\"] = df_pred.apply(lambda x: select_misc(x, 0.5), axis=1)\n",
+        "_ = evaluate_classifier(np.array(df_pred[\"labels\"]), np.array(df_pred[\"pred*\"]), None, labels, \"Zero-shot classification, refined\", \"cm_peril_zero_b\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VeEvTc2yEVHI"
+      },
+      "source": [
+        "We export the output to Excel to analyze the prediction errors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "fVV5op92EVHI"
+      },
+      "outputs": [],
+      "source": [
+        "if not os.path.exists(\"./results\"):\n",
+        "  os.mkdir(\"./results\")\n",
+        "df_pred.to_excel(\"results/peril_pred_zero_shot.xlsx\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uucd4ButEVHI"
+      },
+      "source": [
+        "Looking at false predictions in the training set, we observe the following:\n",
+        "\n",
+        "* True label “Vandalism”, predicted label “Vehicle” or “Misc”: Quite many descriptions contain the word “glass”. For these claims, “Vandalism” appears to be a natural classification.\n",
+        "    \n",
+        "* True label “Vehicle”, predicted label “Vandalism”: This group contains many descriptions like “light pole damaged”, “fence damaged”. Apparently, the zero-shot classifier does not realize that for these items, damage caused by a vehicle is more likely than damage caused by vandalism.\n",
+        "* True label “WaterW”, predicted label “WaterNW”: Some of the descriptions like “frozen pipe caused water damage to indoor pool”, “gutter pulled from roof ice dam”, “Water damage and mold growth from storms” suggest that the candidate word “Weather” is not optimal to attract all weather-related water claims.\n",
+        "\n",
+        "Based on these and similar observations, one could refine the approach by adding more candidate expressions, e.g., adding “glass” to hazard type 0 (“Vandalism”), “light pole” and “fence” to hazard type 5 (“Vehicle”), “storm” and “ice” to hazard type 7 (“WaterW”), etc.\n",
+        "\n",
+        "However, the computational effort of zero-shot classification scales with the number of candidate expressions times number of samples, so that we don't want to supply too many candidate expressions.\n",
+        "Ideally, we would have an approach to extract candidate expressions from the data....\n",
+        "\n",
+        "We will look at such an approach in [Section 5](#topic_modeling).\n",
+        "Before going there, the next section offers an alternative approach with less computational effort than zero-shot classification."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "csG9Uh15maLE"
+      },
+      "source": [
+        "<a id='similarity'></a>\n",
+        "<a name='similarity'></a>\n",
+        "## 4.&nbsp;Unsupervised Classification Using Similarity\n",
+        "\n",
+        "This approach is similar to the previous one. It is also suitable in situations with no or only few labeled data.\n",
+        "\n",
+        "The model is presented with a text sequence and a list of expressions, and selects the expression which is most \"similar\" to the text sequence.\n",
+        "Here, we use cosine-similarity, which is defined as the dot product of two embedding vectors, each normalized to unit length."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vkqpIohSob7s"
+      },
+      "source": [
+        "<a id='similarity_demo'></a>\n",
+        "<a name='similarity_demo'></a>\n",
+        "###  4.1 Demonstration of the approach\n",
+        "\n",
+        "In this section you will learn how to perform unsupervised classification using similarity.\n",
+        "\n",
+        "Again, we will try to predict the peril type from the claim descriptions.\n",
+        "\n",
+        "First, we create a dictionary that maps certain verbal expression to peril types:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "A-MB8vWeo9Qa"
+      },
+      "outputs": [],
+      "source": [
+        "candidates = OrderedDict({\n",
+        "    \"Vandalism\": 0,\n",
+        "    \"Glass\": 0,\n",
+        "    \"Theft\": 0,\n",
+        "    \"Fire damage\": 1,\n",
+        "    \"Lightning damage\": 2,\n",
+        "    \"Wind damage\": 3,\n",
+        "    \"Hail damage\": 4,\n",
+        "    \"Damage caused by a vehicle\": 5,\n",
+        "    \"Water damage\": 6,\n",
+        "    \"Weather damage\": 7,\n",
+        "    \"Ice\": 7,\n",
+        "    \"Electricity\": 8,\n",
+        "    \"power surge\": 8})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5JhbMQjepls9"
+      },
+      "source": [
+        "As you can see, we have applied some of the lessons learned from the previous experiments with the zero-shot classifier. For instance, we have added \"Glass\" to the list of candidate expressions mapped to \"Vandalism\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Coa6gM93qNZv"
+      },
+      "source": [
+        "We use the model ``sentence-transformers/all-MiniLM-L12-v2``, which is a BERT model that produces a sequence of real-valued vectors of length 384. During its pre-training on sentence similarity tasks, mean pooling was applied to convert this sequence into a single vector."
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+      "source": [
+        "model_name = \"sentence-transformers/all-MiniLM-L12-v2\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "model = AutoModel.from_pretrained(model_name).to(device)"
+      ]
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+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
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+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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           },
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+              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
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           },
-          "title": {
-           "x": 0.05
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+      "source": [
+        "ds_sim = DatasetDict({\"train\": Dataset.from_pandas(df_train), \"test\": Dataset.from_pandas(df_valid)})\n",
+        "ds_sim = ds_sim.map(tokenize, batched=True)\n",
+        "ds_sim = ds_sim.map(extract_sequence_encoding, fn_kwargs={\"model\": model, \"normalize\": True}, batched=True, batch_size=16)\n",
+        "x = np.array(ds_sim[\"test\"][\"mean_hidden_state\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U1KiO06nViH4"
+      },
+      "source": [
+        "Now, the Numpy array `x` contains the sequence embeddings for each claim description.\n",
+        "\n",
+        "The same procedure is applied to the candidate expressions, which are stored in the array `y`."
+      ]
+    },
+    {
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/13 [00:00<?, ? examples/s]"
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+            "application/vnd.jupyter.widget-view+json": {
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           },
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-           "ticks": "",
-           "title": {
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/13 [00:00<?, ? examples/s]"
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+            "application/vnd.jupyter.widget-view+json": {
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-        "title": {
-         "text": "Transformer-based classifier"
-        },
-        "width": 600,
-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
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-          "text": "<b>predicted class</b>"
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-          "text": "<b>actual class</b>"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "ds_candidates = Dataset.from_dict({\"Description\": candidates.keys()})\n",
+        "ds_candidates = ds_candidates.map(tokenize, batched=True)\n",
+        "ds_candidates = ds_candidates.map(extract_sequence_encoding, fn_kwargs={\"model\": model, \"normalize\": True}, batched=True)\n",
+        "y = np.array(ds_candidates[\"mean_hidden_state\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1DLkEGHiVwsJ"
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+      "source": [
+        "Finally, we calculate the pairwise cosine similarity scores by the dot product of the two arrays, and greedily select the peril type with the highest score."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(ds[\"test\"])\n",
-    "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), labels,\"Transformer-based classifier\", \"cm_peril_transformer\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "N68LJA8yEVHG"
-   },
-   "source": [
-    "The performance is comparable to that of the logistic regression classifier, with an improved Brier loss and accuracy score.\n",
-    "It appears that the model struggles to tell `WaterNW` apart from `WaterW`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CoYTxK22EVHG"
-   },
-   "source": [
-    "<a id='zero_shot'></a>\n",
-    "<a name='zero_shot'></a>\n",
-    "## 3.&nbsp;Zero-shot Classification\n",
-    "\n",
-    "There are situations with no or only few labeled data.\n",
-    "\n",
-    "Zero-shot classification is an approach that is suited in this case.\n",
-    "Zero-shot classification is about classifying text sequences in an unsupervised way\n",
-    "(without having training data in advance and building a model).\n",
-    "\n",
-    "The model is presented with a text sequence and a list of expressions, and assigns a probability to each expression."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "VLx2AElsEVHG"
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-   "source": [
-    "<a id='zero_shot_demo'></a>\n",
-    "<a name='zero_shot_demo'></a>\n",
-    "###  3.1 Demonstration of the approach\n",
-    "\n",
-    "In this section you will learn how to apply zero-shot classification to perform the classification by peril type on\n",
-    "the claims data described above.\n",
-    "\n",
-    "First, we create a dictionary mapping certain verbal expression to peril types:"
-   ]
-  },
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-   },
-   "outputs": [],
-   "source": [
-    "choices = OrderedDict({\n",
-    "    \"Vandalism\": 0,\n",
-    "    \"Theft\": 0,\n",
-    "    \"Fire\": 1,\n",
-    "    \"Lightning\": 2,\n",
-    "    \"Wind\": 3,\n",
-    "    \"Hail\": 4,\n",
-    "    \"Vehicle\": 5,\n",
-    "    \"Water\": 6,\n",
-    "    \"Weather\": 7,\n",
-    "    \"Misc\": 8})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IpkNFlZ8EVHG",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We set up the zero-shot classifier using the `pipeline` abstraction.\n",
-    "By default, the `facebook/bart-large-mnli` model is used.\n",
-    "By specifying `device=0`, we use GPU support if available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0,
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-      "c315d91e15f34eeaa4ee955f88e86285"
-     ]
     },
-    "id": "kmmi1hs8EVHG",
-    "outputId": "21aa6b34-289c-4f42-e617-298c042a8391",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No model was supplied, defaulted to facebook/bart-large-mnli (https://huggingface.co/facebook/bart-large-mnli)\n",
-      "loading configuration file https://huggingface.co/facebook/bart-large-mnli/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/980f2be6bd282c5079e99199d7554cfd13000433ed0fdc527e7def799e5738fe.4fdc7ce6768977d347b32986aff152e26fcebbda34ef89ac9b114971d0342e09\n",
-      "Model config BartConfig {\n",
-      "  \"_name_or_path\": \"facebook/bart-large-mnli\",\n",
-      "  \"_num_labels\": 3,\n",
-      "  \"activation_dropout\": 0.0,\n",
-      "  \"activation_function\": \"gelu\",\n",
-      "  \"add_final_layer_norm\": false,\n",
-      "  \"architectures\": [\n",
-      "    \"BartForSequenceClassification\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.0,\n",
-      "  \"bos_token_id\": 0,\n",
-      "  \"classif_dropout\": 0.0,\n",
-      "  \"classifier_dropout\": 0.0,\n",
-      "  \"d_model\": 1024,\n",
-      "  \"decoder_attention_heads\": 16,\n",
-      "  \"decoder_ffn_dim\": 4096,\n",
-      "  \"decoder_layerdrop\": 0.0,\n",
-      "  \"decoder_layers\": 12,\n",
-      "  \"decoder_start_token_id\": 2,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"encoder_attention_heads\": 16,\n",
-      "  \"encoder_ffn_dim\": 4096,\n",
-      "  \"encoder_layerdrop\": 0.0,\n",
-      "  \"encoder_layers\": 12,\n",
-      "  \"eos_token_id\": 2,\n",
-      "  \"forced_eos_token_id\": 2,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"contradiction\",\n",
-      "    \"1\": \"neutral\",\n",
-      "    \"2\": \"entailment\"\n",
-      "  },\n",
-      "  \"init_std\": 0.02,\n",
-      "  \"is_encoder_decoder\": true,\n",
-      "  \"label2id\": {\n",
-      "    \"contradiction\": 0,\n",
-      "    \"entailment\": 2,\n",
-      "    \"neutral\": 1\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 1024,\n",
-      "  \"model_type\": \"bart\",\n",
-      "  \"normalize_before\": false,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"output_past\": false,\n",
-      "  \"pad_token_id\": 1,\n",
-      "  \"scale_embedding\": false,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 50265\n",
-      "}\n",
-      "\n",
-      "loading configuration file https://huggingface.co/facebook/bart-large-mnli/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/980f2be6bd282c5079e99199d7554cfd13000433ed0fdc527e7def799e5738fe.4fdc7ce6768977d347b32986aff152e26fcebbda34ef89ac9b114971d0342e09\n",
-      "Model config BartConfig {\n",
-      "  \"_name_or_path\": \"facebook/bart-large-mnli\",\n",
-      "  \"_num_labels\": 3,\n",
-      "  \"activation_dropout\": 0.0,\n",
-      "  \"activation_function\": \"gelu\",\n",
-      "  \"add_final_layer_norm\": false,\n",
-      "  \"architectures\": [\n",
-      "    \"BartForSequenceClassification\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.0,\n",
-      "  \"bos_token_id\": 0,\n",
-      "  \"classif_dropout\": 0.0,\n",
-      "  \"classifier_dropout\": 0.0,\n",
-      "  \"d_model\": 1024,\n",
-      "  \"decoder_attention_heads\": 16,\n",
-      "  \"decoder_ffn_dim\": 4096,\n",
-      "  \"decoder_layerdrop\": 0.0,\n",
-      "  \"decoder_layers\": 12,\n",
-      "  \"decoder_start_token_id\": 2,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"encoder_attention_heads\": 16,\n",
-      "  \"encoder_ffn_dim\": 4096,\n",
-      "  \"encoder_layerdrop\": 0.0,\n",
-      "  \"encoder_layers\": 12,\n",
-      "  \"eos_token_id\": 2,\n",
-      "  \"forced_eos_token_id\": 2,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"contradiction\",\n",
-      "    \"1\": \"neutral\",\n",
-      "    \"2\": \"entailment\"\n",
-      "  },\n",
-      "  \"init_std\": 0.02,\n",
-      "  \"is_encoder_decoder\": true,\n",
-      "  \"label2id\": {\n",
-      "    \"contradiction\": 0,\n",
-      "    \"entailment\": 2,\n",
-      "    \"neutral\": 1\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 1024,\n",
-      "  \"model_type\": \"bart\",\n",
-      "  \"normalize_before\": false,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"output_past\": false,\n",
-      "  \"pad_token_id\": 1,\n",
-      "  \"scale_embedding\": false,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 50265\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/facebook/bart-large-mnli/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/35014754ae1fcb956d44903df02e4f69d0917cab0901ace5ac7f4a4a998346fe.a30bb5d685bb3c6e9376ab4480f1b252d9796d438d1c84a9b2deb0275c5b2151\n",
-      "All model checkpoint weights were used when initializing BartForSequenceClassification.\n",
-      "\n",
-      "All the weights of BartForSequenceClassification were initialized from the model checkpoint at facebook/bart-large-mnli.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BartForSequenceClassification for predictions without further training.\n",
-      "loading configuration file https://huggingface.co/facebook/bart-large-mnli/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/980f2be6bd282c5079e99199d7554cfd13000433ed0fdc527e7def799e5738fe.4fdc7ce6768977d347b32986aff152e26fcebbda34ef89ac9b114971d0342e09\n",
-      "Model config BartConfig {\n",
-      "  \"_name_or_path\": \"facebook/bart-large-mnli\",\n",
-      "  \"_num_labels\": 3,\n",
-      "  \"activation_dropout\": 0.0,\n",
-      "  \"activation_function\": \"gelu\",\n",
-      "  \"add_final_layer_norm\": false,\n",
-      "  \"architectures\": [\n",
-      "    \"BartForSequenceClassification\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.0,\n",
-      "  \"bos_token_id\": 0,\n",
-      "  \"classif_dropout\": 0.0,\n",
-      "  \"classifier_dropout\": 0.0,\n",
-      "  \"d_model\": 1024,\n",
-      "  \"decoder_attention_heads\": 16,\n",
-      "  \"decoder_ffn_dim\": 4096,\n",
-      "  \"decoder_layerdrop\": 0.0,\n",
-      "  \"decoder_layers\": 12,\n",
-      "  \"decoder_start_token_id\": 2,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"encoder_attention_heads\": 16,\n",
-      "  \"encoder_ffn_dim\": 4096,\n",
-      "  \"encoder_layerdrop\": 0.0,\n",
-      "  \"encoder_layers\": 12,\n",
-      "  \"eos_token_id\": 2,\n",
-      "  \"forced_eos_token_id\": 2,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"contradiction\",\n",
-      "    \"1\": \"neutral\",\n",
-      "    \"2\": \"entailment\"\n",
-      "  },\n",
-      "  \"init_std\": 0.02,\n",
-      "  \"is_encoder_decoder\": true,\n",
-      "  \"label2id\": {\n",
-      "    \"contradiction\": 0,\n",
-      "    \"entailment\": 2,\n",
-      "    \"neutral\": 1\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 1024,\n",
-      "  \"model_type\": \"bart\",\n",
-      "  \"normalize_before\": false,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"output_past\": false,\n",
-      "  \"pad_token_id\": 1,\n",
-      "  \"scale_embedding\": false,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 50265\n",
-      "}\n",
-      "\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json from cache at /home/ubuntu/.cache/huggingface/transformers/b4f8395edd321fd7cd8a87bca767b1135680a41d8931516dd1a447294633b9db.647b4548b6d9ea817e82e7a9231a320231a1c9ea24053cc9e758f3fe68216f05\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt from cache at /home/ubuntu/.cache/huggingface/transformers/19c09c9654551e163f858f3c99c226a8d0026acc4935528df3b09179204efe4c.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json from cache at /home/ubuntu/.cache/huggingface/transformers/540455855ce0a3c13893c5d090d142de9481365bd32dc5457c957e5d13444d23.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/special_tokens_map.json from cache at None\n",
-      "loading file https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/569800088d6f014777e6d5d8cb61b2b8bb3d18a508a1d8af041aae6bbc6f3dfe.67d01b18f2079bd75eac0b2f2e7235768c7f26bd728e7a855a1c5acae01a91a8\n",
-      "loading configuration file https://huggingface.co/facebook/bart-large-mnli/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/980f2be6bd282c5079e99199d7554cfd13000433ed0fdc527e7def799e5738fe.4fdc7ce6768977d347b32986aff152e26fcebbda34ef89ac9b114971d0342e09\n",
-      "Model config BartConfig {\n",
-      "  \"_name_or_path\": \"facebook/bart-large-mnli\",\n",
-      "  \"_num_labels\": 3,\n",
-      "  \"activation_dropout\": 0.0,\n",
-      "  \"activation_function\": \"gelu\",\n",
-      "  \"add_final_layer_norm\": false,\n",
-      "  \"architectures\": [\n",
-      "    \"BartForSequenceClassification\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.0,\n",
-      "  \"bos_token_id\": 0,\n",
-      "  \"classif_dropout\": 0.0,\n",
-      "  \"classifier_dropout\": 0.0,\n",
-      "  \"d_model\": 1024,\n",
-      "  \"decoder_attention_heads\": 16,\n",
-      "  \"decoder_ffn_dim\": 4096,\n",
-      "  \"decoder_layerdrop\": 0.0,\n",
-      "  \"decoder_layers\": 12,\n",
-      "  \"decoder_start_token_id\": 2,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"encoder_attention_heads\": 16,\n",
-      "  \"encoder_ffn_dim\": 4096,\n",
-      "  \"encoder_layerdrop\": 0.0,\n",
-      "  \"encoder_layers\": 12,\n",
-      "  \"eos_token_id\": 2,\n",
-      "  \"forced_eos_token_id\": 2,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"contradiction\",\n",
-      "    \"1\": \"neutral\",\n",
-      "    \"2\": \"entailment\"\n",
-      "  },\n",
-      "  \"init_std\": 0.02,\n",
-      "  \"is_encoder_decoder\": true,\n",
-      "  \"label2id\": {\n",
-      "    \"contradiction\": 0,\n",
-      "    \"entailment\": 2,\n",
-      "    \"neutral\": 1\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 1024,\n",
-      "  \"model_type\": \"bart\",\n",
-      "  \"normalize_before\": false,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"output_past\": false,\n",
-      "  \"pad_token_id\": 1,\n",
-      "  \"scale_embedding\": false,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 50265\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "classifier = pipeline(\"zero-shot-classification\", device=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "VR7Qe56REVHH",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Then, we feed the claim descriptions of the entire test set,\n",
-    "presenting the classifier with the list of possible choices as the second argument.\n",
-    "\n",
-    "We use the test set directly, because zero shot classification requires no training!\n",
-    "\n",
-    "On an AWS EC2 p2.xlarge instance, the run time is about 5 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "lfYLThd3scHW"
+      },
+      "outputs": [],
+      "source": [
+        "scores = np.dot(x, y.T)\n",
+        "selected = np.argmax(scores, axis=1)\n",
+        "pred = np.array([list(candidates.values())[i] for i in selected])"
+      ]
     },
-    "id": "JLTEy0G8EVHH",
-    "outputId": "b237faa9-fa45-427c-a2fc-ba5bb249a878",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Disabling tokenizer parallelism, we're using DataLoader multithreading already\n"
-     ]
-    }
-   ],
-   "source": [
-    "res = classifier(ds[\"test\"][\"Description\"], list(choices.keys()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "amIfcKCREVHH"
-   },
-   "source": [
-    "This returns a list of `dict` with the following keys:\n",
-    "* **sequence** (`str`) — The sequence for which this is the output.\n",
-    "* **labels** (`List[str]`) — The labels sorted by order of likelihood.\n",
-    "* **scores** (`List[float]`) — The probabilities for each of the labels.\n",
-    "\n",
-    "We store the predictions in a Pandas DataFrame and evaluate the performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vpE6-FbaVvUT"
+      },
+      "source": [
+        "For inspection, we generate a Pandas DataFrame and export it to an Excel file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "N6vJhe_oyfxU"
+      },
+      "outputs": [],
+      "source": [
+        "df_scores = pd.DataFrame(columns=candidates.keys(), data=scores)\n",
+        "df_selected = pd.DataFrame(columns=[\"selected\"], data=selected)\n",
+        "df_pred = pd.DataFrame(columns=[\"pred\"], data=pred)\n",
+        "if not os.path.exists(\"./results\"):\n",
+        "  os.mkdir(\"./results\")\n",
+        "pd.concat([df_valid, df_scores, df_pred], axis=1).to_excel(\"./results/zero_similarity_test.xlsx\")"
+      ]
     },
-    "id": "rrOMsvGHRPHR",
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-      "Zero-shot-classification\n",
-      "accuracy score = 65.5%,  log loss = 1.043,  Brier loss = 0.463\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.93      0.44      0.59       310\n",
-      "        Fire       0.68      0.70      0.69        46\n",
-      "   Lightning       0.94      0.93      0.94       123\n",
-      "        Wind       1.00      0.84      0.91       107\n",
-      "        Hail       0.75      1.00      0.86        18\n",
-      "     Vehicle       0.89      0.69      0.77       227\n",
-      "     WaterNW       0.56      0.75      0.64        67\n",
-      "      WaterW       0.00      0.00      0.00        38\n",
-      "        Misc       0.25      0.83      0.38       103\n",
-      "\n",
-      "    accuracy                           0.66      1039\n",
-      "   macro avg       0.67      0.69      0.64      1039\n",
-      "weighted avg       0.79      0.66      0.68      1039\n",
-      "\n"
-     ]
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+        "The performance is as follows:"
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+            "Similarity\n",
+            "accuracy score = 74.5%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.90      0.80      0.85       310\n",
+            "        Fire       0.64      0.83      0.72        46\n",
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+            "      WaterW       0.12      0.26      0.17        38\n",
+            "        Misc       0.76      0.30      0.43       103\n",
+            "\n",
+            "    accuracy                           0.74      1039\n",
+            "   macro avg       0.68      0.73      0.68      1039\n",
+            "weighted avg       0.80      0.74      0.75      1039\n",
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+      ],
+      "source": [
+        "_ = evaluate_classifier(np.array(df_valid[\"labels\"]), pred, None, labels, \"Similarity\", \"cm_peril_sim_a\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
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+      "source": [
+        "This is not bad! We have already improved on the accuracy score obtained by the zero-shot classifier (using a different set of candidate expressions though).\n",
+        "\n",
+        "Let’s see how we can improve the results further."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "proba = np.zeros((df_valid.shape[0], len(labels)))\n",
-    "for i, sample in enumerate(res):\n",
-    "  for label, score in zip(sample[\"labels\"], sample[\"scores\"]):\n",
-    "    proba[i, choices[label]] += score\n",
-    "  proba[i, :] = proba[i, :] / np.sum(proba[i, :])\n",
-    "_ = evaluate_classifier(np.array(df_valid[\"labels\"]), None, proba, labels, \"Zero-shot-classification\", \"cm_peril_zero_a\")"
-   ]
-  },
-  {
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-   "source": [
-    "On the test set, we achieve an accuracy of 65.5% (compared to 29.8% of the dummy classifier).\n",
-    "Apparently, the classifier struggles to correctly identify the `WaterW` cases based on the expression “Weather”.\n",
-    "Also, it seems that the expression “Misc” may not be the optimal choice, as it produces many false positives."
-   ]
-  },
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-   "execution_count": 21,
-   "metadata": {
-    "id": "4EG2NJ_FEVHH",
-    "pycharm": {
-     "name": "#%%\n"
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-   "outputs": [],
-   "source": [
-    "pred = [{\n",
-    "    **{\"pred\"+str(i): choices[item[\"labels\"][i]] for i in range(10)},\n",
-    "    **{\"score\"+str(i): item[\"scores\"][i] for i in range(10)}\n",
-    "} for item in res]\n",
-    "df_pred = pd.DataFrame(pred)\n",
-    "df_pred[[\"labels\", \"Description\"]] = df_valid[[\"labels\", \"Description\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3z9s2Z7zEVHH"
-   },
-   "source": [
-    "<a id='zero_shot_refinement'></a>\n",
-    "<a name='zero_shot_refinement'></a>\n",
-    "### 3.2  Refinement\n",
-    "\n",
-    "To improve the performance on \"Misc\", we introduce the following heuristic:\n",
-    "If the probability assigned to the expression “Misc” is highest\n",
-    "but with a margin of less than 50 percentage points to the second-most likely expression, we select the latter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Zero-shot classification, refined\n",
-      "accuracy score = 69.7%,  log loss = nan,  Brier loss = nan\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.77      0.62      0.69       310\n",
-      "        Fire       0.69      0.78      0.73        46\n",
-      "   Lightning       0.92      0.94      0.93       123\n",
-      "        Wind       0.91      0.85      0.88       107\n",
-      "        Hail       0.58      1.00      0.73        18\n",
-      "     Vehicle       0.62      0.77      0.69       227\n",
-      "     WaterNW       0.54      0.78      0.63        67\n",
-      "      WaterW       0.29      0.11      0.15        38\n",
-      "        Misc       0.45      0.40      0.42       103\n",
-      "\n",
-      "    accuracy                           0.70      1039\n",
-      "   macro avg       0.64      0.69      0.65      1039\n",
-      "weighted avg       0.70      0.70      0.69      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CEEIA6IT3lZE"
+      },
+      "source": [
+        "<a id='similarity_refinement'></a>\n",
+        "<a name='similarity_refinement'></a>\n",
+        "### 4.2. Refinement\n",
+        "\n",
+        "A possible way to improve the performance is to train a classifier using the predicted labels of the previous section. Although this is a supervised learning step, we are not using the original labels, therefore the overall approach is still unsupervised."
+      ]
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+      "source": [
+        "First, we predict the labels on the training set, the same way as before, and store them in the DataFrame `df_train_copy`:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def select_misc(row, threshold):\n",
-    "    return row[\"pred1\"] if row[\"pred0\"] == 8 and row[\"score0\"] - row[\"score1\"] < threshold else row[\"pred0\"]\n",
-    "df_pred[\"pred*\"] = df_pred.apply(lambda x: select_misc(x, 0.5), axis=1)\n",
-    "_ = evaluate_classifier(np.array(df_pred[\"labels\"]), np.array(df_pred[\"pred*\"]), None, labels, \"Zero-shot classification, refined\", \"cm_peril_zero_b\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "VeEvTc2yEVHI"
-   },
-   "source": [
-    "We export the output to Excel to analyze the prediction errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "id": "fVV5op92EVHI"
-   },
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(\"./results\"):\n",
-    "  os.mkdir(\"./results\")\n",
-    "df_pred.to_excel(\"results/peril_pred_zero_shot.xlsx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uucd4ButEVHI"
-   },
-   "source": [
-    "Looking at false predictions in the training set, we observe the following:\n",
-    "\n",
-    "* True label “Vandalism”, predicted label “Vehicle” or “Misc”: Quite many descriptions contain the word “glass”. For these claims, “Vandalism” appears to be a natural classification.\n",
-    "    \n",
-    "* True label “Vehicle”, predicted label “Vandalism”: This group contains many descriptions like “light pole damaged”, “fence damaged”. Apparently, the zero-shot classifier does not realize that for these items, damage caused by a vehicle is more likely than damage caused by vandalism.\n",
-    "* True label “WaterW”, predicted label “WaterNW”: Some of the descriptions like “frozen pipe caused water damage to indoor pool”, “gutter pulled from roof ice dam”, “Water damage and mold growth from storms” suggest that the candidate word “Weather” is not optimal to attract all weather-related water claims.\n",
-    "\n",
-    "Based on these and similar observations, one could refine the approach by adding more candidate expressions, e.g., adding “glass” to hazard type 0 (“Vandalism”), “light pole” and “fence” to hazard type 5 (“Vehicle”), “storm” and “ice” to hazard type 7 (“WaterW”), etc.\n",
-    "\n",
-    "However, the computational effort of zero-shot classification scales with the number of candidate expressions times number of samples, so that we don't want to supply too many candidate expressions.\n",
-    "Ideally, we would have an approach to extract candidate expressions from the data.... \n",
-    "\n",
-    "We will look at such an approach in [Section 5](#topic_modeling).\n",
-    "Before going there, the next section offers an alternative approach with less computational effort than zero-shot classification."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "csG9Uh15maLE"
-   },
-   "source": [
-    "<a id='similarity'></a>\n",
-    "<a name='similarity'></a>\n",
-    "## 4.&nbsp;Unsupervised Classification Using Similarity\n",
-    "\n",
-    "This approach is similar to the previous one. It is also suitable in situations with no or only few labeled data.\n",
-    "\n",
-    "The model is presented with a text sequence and a list of expressions, and selects the expression which is most \"similar\" to the text sequence. \n",
-    "Here, we use cosine-similarity, which is defined as the dot product of two embedding vectors, each normalized to unit length."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vkqpIohSob7s"
-   },
-   "source": [
-    "<a id='similarity_demo'></a>\n",
-    "<a name='similarity_demo'></a>\n",
-    "###  4.1 Demonstration of the approach\n",
-    "\n",
-    "In this section you will learn how to perform unsupervised classification using similarity.\n",
-    "\n",
-    "Again, we will try to predict the peril type from the claim descriptions.\n",
-    "\n",
-    "First, we create a dictionary that maps certain verbal expression to peril types:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "id": "A-MB8vWeo9Qa"
-   },
-   "outputs": [],
-   "source": [
-    "candidates = OrderedDict({\n",
-    "    \"Vandalism\": 0,\n",
-    "    \"Glass\": 0,\n",
-    "    \"Theft\": 0,\n",
-    "    \"Fire damage\": 1,\n",
-    "    \"Lightning damage\": 2,\n",
-    "    \"Wind damage\": 3,\n",
-    "    \"Hail damage\": 4,\n",
-    "    \"Damage caused by a vehicle\": 5,\n",
-    "    \"Water damage\": 6,\n",
-    "    \"Weather damage\": 7,\n",
-    "    \"Ice\": 7,\n",
-    "    \"Electricity\": 8,\n",
-    "    \"power surge\": 8})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5JhbMQjepls9"
-   },
-   "source": [
-    "As you can see, we have applied some of the lessons learned from the previous experiments with the zero-shot classifier. For instance, we have added \"Glass\" to the list of candidate expressions mapped to \"Vandalism\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Coa6gM93qNZv"
-   },
-   "source": [
-    "We use the model ``sentence-transformers/all-MiniLM-L12-v2``, which is a BERT model that produces a sequence of real-valued vectors of length 384. During its pre-training on sentence similarity tasks, mean pooling was applied to convert this sequence into a single vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "65gL10u6q0_F",
-    "outputId": "39e9484a-666e-467e-cf92-43d3456ab522"
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/vocab.txt from cache at /home/ubuntu/.cache/huggingface/transformers/8453639ef9bce7fd3334f2ffcc63f19ffa3ff3b8b0aec5ee8741d0d59ab5eb31.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
-      "loading file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/tokenizer.json from cache at /home/ubuntu/.cache/huggingface/transformers/c0d6cf2687a28e7fdc2d77fc4b172632b15debbafe3f381b3d800782b3bb28fc.b1d3b19ab013240a348484166f1a44749cad6f108156f07e3784cbf6c2a27772\n",
-      "loading file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/transformers/759b5b75ca69b02d11c5dc31d4e00cbcb38ad91929ab3ca2f0c6bf6dc9459b1e.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
-      "loading file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/3a7676bf5f7cb02094e1801ae25a698f8a4a6866ba2460a42461b62dac7b1334.5b23e166abf4f8000a0354959c4020a8884f59070a6d651fea41ed5c12d74910\n",
-      "loading configuration file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/a46191c34f15e96c8728222b3df77a916eb326096e81d5197b2ce8b95ab23a2a.58abd84a9ec36b7a88d68f5a4f944253c7454868833676e579e2b47196f2d068\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 384,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 1536,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 12,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/d44255b116c329ad3895f8436d9a62d72aac4ddff7017b906d59a3f00e9e27c5.89e6bd2185b4194bc0170670472dca8e5818c9c1e9f6cea8b8650d3fa2f1262b\n",
-      "All model checkpoint weights were used when initializing BertModel.\n",
-      "\n",
-      "All the weights of BertModel were initialized from the model checkpoint at sentence-transformers/all-MiniLM-L12-v2.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertModel for predictions without further training.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name = \"sentence-transformers/all-MiniLM-L12-v2\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = AutoModel.from_pretrained(model_name).to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "p7ojKresUJIY"
-   },
-   "source": [
-    "Next, we generate the sentence embeddings of the claim descriptions. To this end, we tokenize them and then pass them into the helper function `extract_sequence_encoding`, which applies the transformer encoder and extracts the last hidden state. Mean pooling is applied, in line with the way the model was pre-trained. The value `True` is passed with key word argument `normalize` to enforce normalization of the output vector to unit length in the Euclidean norm. "
-   ]
-  },
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-   "cell_type": "code",
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-     ]
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "NjT3zaHM3sNv"
+      },
+      "outputs": [],
+      "source": [
+        "x_train = np.array(ds_sim[\"train\"][\"mean_hidden_state\"])\n",
+        "scores = np.dot(x_train, y.T)\n",
+        "selected = np.argmax(scores, axis=1)\n",
+        "pred = np.array([list(candidates.values())[i] for i in selected])\n",
+        "\n",
+        "df_train_copy  = df_train.copy()\n",
+        "df_train_copy[\"labels\"] = pred"
+      ]
     },
-    "id": "EV0qy1wNrHuJ",
-    "outputId": "d10b40ff-8731-4b71-c4f4-10145e593c37"
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd1193f498234674bc75278377c704ec",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uNcUCbopiSMA"
       },
-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?ba/s]"
+      "source": [
+        "From here, we follow the approach of [Section 2](#supervised). Again, we use ` distilbert-base-uncased`."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4e3dc1dc38e94e0fa634eca7b48502c8",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "AKStE1hD8s8g"
       },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      "outputs": [],
+      "source": [
+        "model_name = \"distilbert-base-uncased\"\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6aad6fffcf0452b9158eeaf8230ecb1",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yzX4H1CrijUf"
       },
-      "text/plain": [
-       "  0%|          | 0/312 [00:00<?, ?ba/s]"
+      "source": [
+        "First, we tokenize the claim descriptions:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3280f7e4775141f0812bd93352d60350",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "referenced_widgets": [
+            "a5031e1dc6974abda39169786b5c199d",
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+            "d1b13e4ae377404e84b5d4128fa5fa08",
+            "044c8ea072134111a3634a0f9815125e"
+          ],
+          "height": 49
+        },
+        "id": "xkzmKbyF8Q5J",
+        "outputId": "c0f5193e-4490-4bad-e49f-95e13da4f8b0"
       },
-      "text/plain": [
-       "  0%|          | 0/65 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/4991 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a5031e1dc6974abda39169786b5c199d"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ds_train_copy = Dataset.from_pandas(df_train_copy)\n",
+        "ds_train_copy = ds_train_copy.map(tokenize, batched=True)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_sim = DatasetDict({\"train\": Dataset.from_pandas(df_train), \"test\": Dataset.from_pandas(df_valid)})\n",
-    "ds_sim = ds_sim.map(tokenize, batched=True)\n",
-    "ds_sim = ds_sim.map(extract_sequence_encoding, fn_kwargs={\"model\": model, \"normalize\": True}, batched=True, batch_size=16)\n",
-    "x = np.array(ds_sim[\"test\"][\"mean_hidden_state\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "U1KiO06nViH4"
-   },
-   "source": [
-    "Now, the Numpy array `x` contains the sequence embeddings for each claim description.\n",
-    "\n",
-    "The same procedure is applied to the candidate expressions, which are stored in the array `y`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0,
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     },
-    "id": "iltD0TstsArh",
-    "outputId": "c5534f63-8a54-44f8-d219-0a97461574aa"
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52793cafa40c48b1b4b2c8096111b3ad",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p61QHwmpioyo"
       },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      "source": [
+        "Then, we perform one epoch of training…"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2a0cdcc290f43ef8ff713dc11f1230d",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 141
+        },
+        "id": "sInKXHcx4yy2",
+        "outputId": "2f865d15-03d3-45eb-e564-13ddebb05901"
       },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='624' max='624' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [624/624 00:30, Epoch 1/1]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>623</td>\n",
+              "      <td>0.437100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
+        "\n",
+        "# train the model\n",
+        "batch_size = 8\n",
+        "logging_steps = len(ds_train_copy) // batch_size\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=model_name+\"_peril_s_epochs\",\n",
+        "    num_train_epochs=1,\n",
+        "    per_device_train_batch_size=batch_size,\n",
+        "    per_device_eval_batch_size=batch_size,\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    logging_steps=logging_steps,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "trainer = Trainer(model=model, args=training_args,\n",
+        "                  compute_metrics=compute_metrics, train_dataset=ds_train_copy,\n",
+        "                  eval_dataset=ds_train_copy)\n",
+        "trainer.train();\n",
+        "trainer.save_model(model_name + \"_peril_s\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_candidates = Dataset.from_dict({\"Description\": candidates.keys()})\n",
-    "ds_candidates = ds_candidates.map(tokenize, batched=True)\n",
-    "ds_candidates = ds_candidates.map(extract_sequence_encoding, fn_kwargs={\"model\": model, \"normalize\": True}, batched=True)\n",
-    "y = np.array(ds_candidates[\"mean_hidden_state\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1DLkEGHiVwsJ"
-   },
-   "source": [
-    "Finally, we calculate the pairwise cosine similarity scores by the dot product of the two arrays, and greedily select the peril type with the highest score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "id": "lfYLThd3scHW"
-   },
-   "outputs": [],
-   "source": [
-    "scores = np.dot(x, y.T)\n",
-    "selected = np.argmax(scores, axis=1)\n",
-    "pred = np.array([list(candidates.values())[i] for i in selected])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vpE6-FbaVvUT"
-   },
-   "source": [
-    "For inspection, we generate a Pandas DataFrame and export it to an Excel file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "id": "N6vJhe_oyfxU"
-   },
-   "outputs": [],
-   "source": [
-    "df_scores = pd.DataFrame(columns=candidates.keys(), data=scores)\n",
-    "df_selected = pd.DataFrame(columns=[\"selected\"], data=selected)\n",
-    "df_pred = pd.DataFrame(columns=[\"pred\"], data=pred)\n",
-    "if not os.path.exists(\"./results\"):\n",
-    "  os.mkdir(\"./results\")\n",
-    "pd.concat([df_valid, df_scores, df_pred], axis=1).to_excel(\"./results/zero_similarity_test.xlsx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZKfBreSXhx98"
-   },
-   "source": [
-    "The performance is as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
     },
-    "id": "lEOYl-skt6by",
-    "outputId": "e80f76a5-837d-4b3d-c87a-9ef56477b93d"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Similarity\n",
-      "accuracy score = 74.5%,  log loss = nan,  Brier loss = nan\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.90      0.80      0.85       310\n",
-      "        Fire       0.64      0.83      0.72        46\n",
-      "   Lightning       0.80      0.95      0.87       123\n",
-      "        Wind       0.91      0.84      0.87       107\n",
-      "        Hail       0.67      1.00      0.80        18\n",
-      "     Vehicle       0.88      0.71      0.79       227\n",
-      "     WaterNW       0.48      0.88      0.62        67\n",
-      "      WaterW       0.12      0.26      0.17        38\n",
-      "        Misc       0.76      0.30      0.43       103\n",
-      "\n",
-      "    accuracy                           0.74      1039\n",
-      "   macro avg       0.68      0.73      0.68      1039\n",
-      "weighted avg       0.80      0.74      0.75      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zb40jPS-iulN"
+      },
+      "source": [
+        "… and evaluate the performance on the test set:"
+      ]
     },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "cm_peril_sim_a",
-         "format": "svg"
-        }
-       },
-       "data": [
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "cU3f6uVc9gjU",
+        "outputId": "85fe8509-4a30-482d-9323-1b240b6f37ee"
+      },
+      "outputs": [
         {
-         "coloraxis": "coloraxis",
-         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>",
-         "name": "0",
-         "texttemplate": "%{z}",
-         "type": "heatmap",
-         "x": [
-          " <b>Vandalism</b> ",
-          " <b>Fire</b> ",
-          " <b>Lightning</b> ",
-          " <b>Wind</b> ",
-          " <b>Hail</b> ",
-          " <b>Vehicle</b> ",
-          " <b>WaterNW</b> ",
-          " <b>WaterW</b> ",
-          " <b>Misc</b> "
-         ],
-         "xaxis": "x",
-         "y": [
-          " <b>Vandalism</b> ",
-          " <b>Fire</b> ",
-          " <b>Lightning</b> ",
-          " <b>Wind</b> ",
-          " <b>Hail</b> ",
-          " <b>Vehicle</b> ",
-          " <b>WaterNW</b> ",
-          " <b>WaterW</b> ",
-          " <b>Misc</b> "
-         ],
-         "yaxis": "y",
-         "z": [
-          [
-           249,
-           8,
-           4,
-           3,
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+            "Simularity, refined\n",
+            "accuracy score = 75.9%,  log loss = 1.156,  Brier loss = 0.410\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
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+            "   Vandalism       0.90      0.86      0.88       310\n",
+            "        Fire       0.56      0.76      0.64        46\n",
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+            "      WaterW       0.11      0.18      0.14        38\n",
+            "        Misc       0.87      0.32      0.47       103\n",
+            "\n",
+            "    accuracy                           0.76      1039\n",
+            "   macro avg       0.71      0.72      0.69      1039\n",
+            "weighted avg       0.82      0.76      0.77      1039\n",
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+            ]
+          },
+          "metadata": {}
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+      ],
+      "source": [
+        "predictions = trainer.predict(ds[\"test\"])\n",
+        "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), labels, \"Simularity, refined\", \"cm_peril_sim_b\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
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+      "source": [
+        "The accuracy score has improved by about 2 percentage points.\n",
+        "\n",
+        "Compared to the results obtained by zero-shot classification, we observe that the confusion between “Vandalism” and “Vehicle” has strongly reduced. This might be at least partially due to the fact that we have used different candidate expressions.\n",
+        "\n",
+        "For a fair comparison, you might want to go back and re-run the zero-shot classification using the new candidate expressions. However, you will have noticed that the sentence similarity approach is much faster to execute. The computational effort for both approaches is dominated by running the respective transformer model. For the zero-shot classification, the model is run behind the scenes for each combination of sample and candidate expression, so that the effort scales with the number of samples times the number of candidate expressions. In contrast, as we have seen above, the similarity approach runs the transformer model once for each input sample and once for each candidate expression, so that the effort scales with the number of samples plus the number of candidate expressions. This allows experimenting with different candidate expressions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QO_xTyOKEVHI"
+      },
+      "source": [
+        "<a id='topic_modeling'></a>\n",
+        "<a name='topic_modeling'></a>\n",
+        "## 5.&nbsp;Unsupervised Topic Modeling by Clustering of Document Embeddings\n",
+        "\n",
+        "In the previous section we have seen the strength of zero-shot classification:\n",
+        "No prior training of the language model is required to produce a classification of reasonable quality.\n",
+        "However, it may be difficult to provide suitable candidate expressions.\n",
+        "\n",
+        "In this section, we present an alternative approach.\n",
+        "\n",
+        "The idea is to encode all text samples, to create clusters of \"similar\" documents and to extract meaningful\n",
+        "verbal representations of the clusters.\n",
+        "\n",
+        "Several packages are available to perform this task, e.g.,\n",
+        "[BERTopic](https://maartengr.github.io/BERTopic/index.html),\n",
+        "[Top2Vec](https://github.com/ddangelov/Top2Vec) and\n",
+        "[chat-intents](https://github.com/dborrelli/chat-intents).\n",
+        "These packages use similar concepts but provide different APIs, hyper-parameters, diagnostics tools, etc.\n",
+        "\n",
+        "Here, we use BERTopic.\n",
+        "\n",
+        "The algorithm consists of the following steps:\n",
+        "\n",
+        "1. **Embed documents:**\n",
+        "    * Encode each text sample (document) into a vector - the embedding.\n",
+        "    This can be based on a BERT model or any other document embedding technique.\n",
+        "    By default, BERTopic uses `all-MiniLM-L6-v2`, which is trained in English.\n",
+        "    In the multi-lingual case it uses `paraphrase-multilingual-MiniLM-L12-v2`.<br><br>\n",
+        "\n",
+        "2.  **Cluster documents:**\n",
+        "    * Reduce the dimensionality of the embeddings.\n",
+        "    This is required because the documents embeddings are high-dimensional,\n",
+        "    and typically, clustering algorithms have difficulty clustering data in high dimensional space.\n",
+        "    By default, BERTopic uses\n",
+        "    [UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction)](https://umap-learn.readthedocs.io/en/latest/)\n",
+        "    as it preserves both the local and global structure of embeddings quite well.<br>\n",
+        "\n",
+        "    * Create clusters of semantically similar documents.\n",
+        "    By default, BERTopic uses\n",
+        "    [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/)\n",
+        "    as it allows to identify outliers.<br><br>\n",
+        "\n",
+        "3. **Create topic representation:**\n",
+        "    * Extract and reduce topics with c-TF-IDF.\n",
+        "    This is a modification of TF-IDF, which applies TD-IDF to the concatenation of all documents within each document cluster,\n",
+        "    to obtain importance scores for the words within the cluster.\n",
+        "    \n",
+        "    * Improve coherence and diversity of words with Maximal Marginal Relevance, to find the most coherent words without having too much overlap between the words themselves. This results in the removal of words that do not contribute to a topic.\n",
+        "    \n",
+        "Let's apply the algorithm to our dataset and examine the results."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wds9A1oIEVHI"
+      },
+      "source": [
+        "<a id='topic_modeling_basic'></a>\n",
+        "<a name='topic_modeling_basic'></a>\n",
+        "### 5.1. Basic topic modeling\n",
+        "\n",
+        "Normally, BERTopic instantiates UMAP and HDBSCAN automatically.\n",
+        "Here, we instantiate them manually and pass them to BERTopic, for the following reasons:\n",
+        "\n",
+        "* For UMAP, we specify `random_state=42`, to improve reproducibility across runs. Please note that reproducibility across platforms is not guaranteed.\n",
+        "\n",
+        "* For HDBSCAN, we specify `min_cluster_size=30` and `min_samples=1` in order to control the number of clusters and the percentage of samples classified as outliers.\n",
+        "\n",
+        "Otherwise, we use the default parameters used by BERTopic."
+      ]
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+              "0      -1    662          -1_lightning_water_damage_wwtp   \n",
+              "1       0    700            0_vandalism_park_dmg_shelter   \n",
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+              "11     10    103           10_broken_door_glass_breakage   \n",
+              "12     11    101                   11_fence_plow_gate_by   \n",
+              "13     12     93              12_roof_wind_shingles_blew   \n",
+              "14     13     83             13_hydrant_fire_hit_damaged   \n",
+              "15     14     81            14_wind_storage_damage_tower   \n",
+              "16     15     81               15_llm_glass_mendota_hawk   \n",
+              "17     16     78            16_building_truck_vehicle_by   \n",
+              "18     17     76            17_water_damage_goodman_pool   \n",
+              "19     18     75                18_garage_door_truck_hwy   \n",
+              "20     19     74         19_window_windows_broken_thrown   \n",
+              "21     20     72         20_hail_buildings_roof_multiple   \n",
+              "22     21     67         21_light_pole_damaged_lightpole   \n",
+              "23     22     63              22_pole_vehicle_hit_struck   \n",
+              "24     23     58                 23_laptop_theft_of_from   \n",
+              "25     24     54             24_hwy_lightning_st_highway   \n",
+              "26     25     52     25_laptop_bradford_computer_damaged   \n",
+              "27     26     51      26_airport_lightning_lights_runway   \n",
+              "28     27     48  27_equipment_playground_instrument_gps   \n",
+              "29     28     45         28_overhead_door_damaged_loader   \n",
+              "30     29     45                29_wind_trees_fence_park   \n",
+              "31     30     45        30_street_light_damaged_accident   \n",
+              "32     31     44                  31_gym_floor_leak_roof   \n",
+              "33     32     44          32_sign_vehicle_signal_traffic   \n",
+              "34     33     43                 33_hs_lightning_hhs_lec   \n",
+              "35     34     41         34_school_elementary_water_high   \n",
+              "36     35     39       35_storm_bldgs_locations_multiple   \n",
+              "37     36     37             36_hs_water_tremper_reuther   \n",
+              "38     37     37       37_radio_antenna_lightning_radios   \n",
+              "39     38     36     38_water_carpet_equipment_computers   \n",
+              "40     39     35  39_toki_courthouse_ms_damagecourthouse   \n",
+              "41     40     34               40_center_water_bldg_main   \n",
+              "42     41     34               41_ms_es_lightning_damage   \n",
+              "43     42     34        42_street_pole_light_streetlight   \n",
+              "44     43     33              43_well_10_lightning_wells   \n",
+              "45     44     31           44_hydrant_vehicle_struck_hit   \n",
+              "46     45     31      45_plant_reservoir_tower_lightning   \n",
+              "47     46     31               46_falk_follette_la_glass   \n",
+              "48     47     30            47_radio_dropped_radios_lost   \n",
+              "49     48     30      48_lift_station_elevator_lightning   \n",
+              "50     49     30    49_buildings_building_water_basement   \n",
+              "\n",
+              "                                       Representation  \\\n",
+              "0   [lightning, water, damage, wwtp, ms, siren, at...   \n",
+              "1   [vandalism, park, dmg, shelter, at, pavilion, ...   \n",
+              "2   [glass, vandalism, west, es, ms, at, lincoln, ...   \n",
+              "3   [fire, smoke, damage, equipment, station, park...   \n",
+              "4   [phone, lightning, system, to, compressor, com...   \n",
+              "5   [power, surge, generator, spoilage, food, outa...   \n",
+              "6   [froze, pipe, sewer, pipes, library, ice, up, ...   \n",
+              "7   [theft, of, stolen, break, from, in, wire, cam...   \n",
+              "8   [graffiti, on, kennedy, hoyt, llm, wall, doors...   \n",
+              "9   [lightning, damage, scale, dpw, museum, nasonv...   \n",
+              "10  [signal, traffic, damaged, box, paradise, knoc...   \n",
+              "11  [broken, door, glass, breakage, entrance, brok...   \n",
+              "12  [fence, plow, gate, by, damaged, vehicle, snow...   \n",
+              "13  [roof, wind, shingles, blew, collapsed, off, w...   \n",
+              "14  [hydrant, fire, hit, damaged, run, plow, vehic...   \n",
+              "15  [wind, storage, damage, tower, antenna, to, te...   \n",
+              "16  [llm, glass, mendota, hawk, black, whitehorse,...   \n",
+              "17  [building, truck, vehicle, by, damaged, bldg, ...   \n",
+              "18  [water, damage, goodman, pool, at, field, toki...   \n",
+              "19  [garage, door, truck, hwy, damaged, shop, over...   \n",
+              "20  [window, windows, broken, thrown, screens, scr...   \n",
+              "21  [hail, buildings, roof, multiple, to, windhail...   \n",
+              "22  [light, pole, damaged, lightpole, rawson, pole...   \n",
+              "23  [pole, vehicle, hit, struck, light, utility, a...   \n",
+              "24  [laptop, theft, of, from, stolen, ita, compute...   \n",
+              "25  [hwy, lightning, st, highway, dept, main, shop...   \n",
+              "26  [laptop, bradford, computer, damaged, mckinley...   \n",
+              "27  [airport, lightning, lights, runway, damage, b...   \n",
+              "28  [equipment, playground, instrument, gps, gear,...   \n",
+              "29  [overhead, door, damaged, loader, hangar, fram...   \n",
+              "30  [wind, trees, fence, park, fencing, shed, stor...   \n",
+              "31  [street, light, damaged, accident, vehicle, kn...   \n",
+              "32  [gym, floor, leak, roof, k9, injured, training...   \n",
+              "33  [sign, vehicle, signal, traffic, struck, hit, ...   \n",
+              "34  [hs, lightning, hhs, lec, damage, at, lighning...   \n",
+              "35  [school, elementary, water, high, damage, elem...   \n",
+              "36  [storm, bldgs, locations, multiple, damage, fa...   \n",
+              "37  [hs, water, tremper, reuther, hcc, mats, damag...   \n",
+              "38  [radio, antenna, lightning, radios, to, freque...   \n",
+              "39  [water, carpet, equipment, computers, to, copi...   \n",
+              "40  [toki, courthouse, ms, damagecourthouse, glass...   \n",
+              "41  [center, water, bldg, main, sinkhole, health, ...   \n",
+              "42  [ms, es, lightning, damage, micrologix, mms, w...   \n",
+              "43  [street, pole, light, streetlight, damaged, du...   \n",
+              "44  [well, 10, lightning, wells, house, monitoring...   \n",
+              "45  [hydrant, vehicle, struck, hit, over, by, car,...   \n",
+              "46  [plant, reservoir, tower, lightning, wastewate...   \n",
+              "47  [falk, follette, la, glass, es, vandalism, sta...   \n",
+              "48  [radio, dropped, radios, lost, portable, when,...   \n",
+              "49  [lift, station, elevator, lightning, stations,...   \n",
+              "50  [buildings, building, water, basement, to, abo...   \n",
+              "\n",
+              "                                  Representative_Docs  \n",
+              "0   [theft of laptop Washington MS                ...  \n",
+              "1   [a/c vandalism                                ...  \n",
+              "2   [glass vandalism at HS                        ...  \n",
+              "3   [fire & smoke damage at Fire Station #7       ...  \n",
+              "4   [lightning damaged phone system               ...  \n",
+              "5   [power surge damage                           ...  \n",
+              "6   [pipe froze and water damage                  ...  \n",
+              "7   [theft of equipment from vehicle              ...  \n",
+              "8   [graffiti at West                             ...  \n",
+              "9   [lightning damage                             ...  \n",
+              "10  [traffic signal damaged                       ...  \n",
+              "11  [broken door/glass at LaFollette HS           ...  \n",
+              "12  [fence gate damaged by vehicle                ...  \n",
+              "13  [wind damage to roof                          ...  \n",
+              "14  [fire hydrant damaged                         ...  \n",
+              "15  [wind damage                                  ...  \n",
+              "16  [LaFollette - llm-12118 - glass door          ...  \n",
+              "17  [HS building damaged by truck                 ...  \n",
+              "18  [water damage                                 ...  \n",
+              "19  [garage door damaged                          ...  \n",
+              "20  [broken window                                ...  \n",
+              "21  [hail damage                                  ...  \n",
+              "22  [light pole damaged                           ...  \n",
+              "23  [vehicle damaged light pole                   ...  \n",
+              "24  [theft of laptop                              ...  \n",
+              "25  [lightning damage at hwy dept.                ...  \n",
+              "26  [laptop #6 damaged                            ...  \n",
+              "27  [lightning damage at airport                  ...  \n",
+              "28  [playground equipment damaged                 ...  \n",
+              "29  [overhead door damaged                        ...  \n",
+              "30  [wind damage at O'Donnell Park                ...  \n",
+              "31  [street light damaged                         ...  \n",
+              "32  [water damage to gym floor                    ...  \n",
+              "33  [vehicle struck and damaged traffic signal    ...  \n",
+              "34  [lightning damage at HS                       ...  \n",
+              "35  [water damage at school                       ...  \n",
+              "36  [storm damage                                 ...  \n",
+              "37  [water damage at HS                           ...  \n",
+              "38  [lightning damage to radio                    ...  \n",
+              "39  [water damage to equipment                    ...  \n",
+              "40  [glass vandalism at Toki MS                   ...  \n",
+              "41  [water damage at Comm Center                  ...  \n",
+              "42  [lightning damage at MS                       ...  \n",
+              "43  [street light pole damaged                    ...  \n",
+              "44  [lightning damage to well 5                   ...  \n",
+              "45  [hydrant damaged                              ...  \n",
+              "46  [lightning damage to sewer plant              ...  \n",
+              "47  [glass vandalism at Falk ES                   ...  \n",
+              "48  [radio damaged                                ...  \n",
+              "49  [lightning damage at lift station             ...  \n",
+              "50  [water damage to building                     ...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-27892c96-6c5e-4699-9495-c6d311ce8aa6\" class=\"colab-df-container\">\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Topic</th>\n",
+              "      <th>Count</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Representation</th>\n",
+              "      <th>Representative_Docs</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-1</td>\n",
+              "      <td>662</td>\n",
+              "      <td>-1_lightning_water_damage_wwtp</td>\n",
+              "      <td>[lightning, water, damage, wwtp, ms, siren, at...</td>\n",
+              "      <td>[theft of laptop Washington MS                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>700</td>\n",
+              "      <td>0_vandalism_park_dmg_shelter</td>\n",
+              "      <td>[vandalism, park, dmg, shelter, at, pavilion, ...</td>\n",
+              "      <td>[a/c vandalism                                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>237</td>\n",
+              "      <td>1_glass_vandalism_west_es</td>\n",
+              "      <td>[glass, vandalism, west, es, ms, at, lincoln, ...</td>\n",
+              "      <td>[glass vandalism at HS                        ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2</td>\n",
+              "      <td>203</td>\n",
+              "      <td>2_fire_smoke_damage_equipment</td>\n",
+              "      <td>[fire, smoke, damage, equipment, station, park...</td>\n",
+              "      <td>[fire &amp; smoke damage at Fire Station #7       ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>3</td>\n",
+              "      <td>180</td>\n",
+              "      <td>3_phone_lightning_system_to</td>\n",
+              "      <td>[phone, lightning, system, to, compressor, com...</td>\n",
+              "      <td>[lightning damaged phone system               ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4</td>\n",
+              "      <td>178</td>\n",
+              "      <td>4_power_surge_generator_spoilage</td>\n",
+              "      <td>[power, surge, generator, spoilage, food, outa...</td>\n",
+              "      <td>[power surge damage                           ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>5</td>\n",
+              "      <td>177</td>\n",
+              "      <td>5_froze_pipe_sewer_pipes</td>\n",
+              "      <td>[froze, pipe, sewer, pipes, library, ice, up, ...</td>\n",
+              "      <td>[pipe froze and water damage                  ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>6</td>\n",
+              "      <td>164</td>\n",
+              "      <td>6_theft_of_stolen_break</td>\n",
+              "      <td>[theft, of, stolen, break, from, in, wire, cam...</td>\n",
+              "      <td>[theft of equipment from vehicle              ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>7</td>\n",
+              "      <td>146</td>\n",
+              "      <td>7_graffiti_on_kennedy_hoyt</td>\n",
+              "      <td>[graffiti, on, kennedy, hoyt, llm, wall, doors...</td>\n",
+              "      <td>[graffiti at West                             ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>8</td>\n",
+              "      <td>121</td>\n",
+              "      <td>8_lightning_damage_scale_dpw</td>\n",
+              "      <td>[lightning, damage, scale, dpw, museum, nasonv...</td>\n",
+              "      <td>[lightning damage                             ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>9</td>\n",
+              "      <td>104</td>\n",
+              "      <td>9_signal_traffic_damaged_box</td>\n",
+              "      <td>[signal, traffic, damaged, box, paradise, knoc...</td>\n",
+              "      <td>[traffic signal damaged                       ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>10</td>\n",
+              "      <td>103</td>\n",
+              "      <td>10_broken_door_glass_breakage</td>\n",
+              "      <td>[broken, door, glass, breakage, entrance, brok...</td>\n",
+              "      <td>[broken door/glass at LaFollette HS           ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>11</td>\n",
+              "      <td>101</td>\n",
+              "      <td>11_fence_plow_gate_by</td>\n",
+              "      <td>[fence, plow, gate, by, damaged, vehicle, snow...</td>\n",
+              "      <td>[fence gate damaged by vehicle                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>12_roof_wind_shingles_blew</td>\n",
+              "      <td>[roof, wind, shingles, blew, collapsed, off, w...</td>\n",
+              "      <td>[wind damage to roof                          ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>13</td>\n",
+              "      <td>83</td>\n",
+              "      <td>13_hydrant_fire_hit_damaged</td>\n",
+              "      <td>[hydrant, fire, hit, damaged, run, plow, vehic...</td>\n",
+              "      <td>[fire hydrant damaged                         ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>14</td>\n",
+              "      <td>81</td>\n",
+              "      <td>14_wind_storage_damage_tower</td>\n",
+              "      <td>[wind, storage, damage, tower, antenna, to, te...</td>\n",
+              "      <td>[wind damage                                  ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>15</td>\n",
+              "      <td>81</td>\n",
+              "      <td>15_llm_glass_mendota_hawk</td>\n",
+              "      <td>[llm, glass, mendota, hawk, black, whitehorse,...</td>\n",
+              "      <td>[LaFollette - llm-12118 - glass door          ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>16</td>\n",
+              "      <td>78</td>\n",
+              "      <td>16_building_truck_vehicle_by</td>\n",
+              "      <td>[building, truck, vehicle, by, damaged, bldg, ...</td>\n",
+              "      <td>[HS building damaged by truck                 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>17</td>\n",
+              "      <td>76</td>\n",
+              "      <td>17_water_damage_goodman_pool</td>\n",
+              "      <td>[water, damage, goodman, pool, at, field, toki...</td>\n",
+              "      <td>[water damage                                 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>18</td>\n",
+              "      <td>75</td>\n",
+              "      <td>18_garage_door_truck_hwy</td>\n",
+              "      <td>[garage, door, truck, hwy, damaged, shop, over...</td>\n",
+              "      <td>[garage door damaged                          ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>19</td>\n",
+              "      <td>74</td>\n",
+              "      <td>19_window_windows_broken_thrown</td>\n",
+              "      <td>[window, windows, broken, thrown, screens, scr...</td>\n",
+              "      <td>[broken window                                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>20</td>\n",
+              "      <td>72</td>\n",
+              "      <td>20_hail_buildings_roof_multiple</td>\n",
+              "      <td>[hail, buildings, roof, multiple, to, windhail...</td>\n",
+              "      <td>[hail damage                                  ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>21</td>\n",
+              "      <td>67</td>\n",
+              "      <td>21_light_pole_damaged_lightpole</td>\n",
+              "      <td>[light, pole, damaged, lightpole, rawson, pole...</td>\n",
+              "      <td>[light pole damaged                           ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>22</td>\n",
+              "      <td>63</td>\n",
+              "      <td>22_pole_vehicle_hit_struck</td>\n",
+              "      <td>[pole, vehicle, hit, struck, light, utility, a...</td>\n",
+              "      <td>[vehicle damaged light pole                   ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>23</td>\n",
+              "      <td>58</td>\n",
+              "      <td>23_laptop_theft_of_from</td>\n",
+              "      <td>[laptop, theft, of, from, stolen, ita, compute...</td>\n",
+              "      <td>[theft of laptop                              ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>24</td>\n",
+              "      <td>54</td>\n",
+              "      <td>24_hwy_lightning_st_highway</td>\n",
+              "      <td>[hwy, lightning, st, highway, dept, main, shop...</td>\n",
+              "      <td>[lightning damage at hwy dept.                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>25</td>\n",
+              "      <td>52</td>\n",
+              "      <td>25_laptop_bradford_computer_damaged</td>\n",
+              "      <td>[laptop, bradford, computer, damaged, mckinley...</td>\n",
+              "      <td>[laptop #6 damaged                            ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>26</td>\n",
+              "      <td>51</td>\n",
+              "      <td>26_airport_lightning_lights_runway</td>\n",
+              "      <td>[airport, lightning, lights, runway, damage, b...</td>\n",
+              "      <td>[lightning damage at airport                  ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>27</td>\n",
+              "      <td>48</td>\n",
+              "      <td>27_equipment_playground_instrument_gps</td>\n",
+              "      <td>[equipment, playground, instrument, gps, gear,...</td>\n",
+              "      <td>[playground equipment damaged                 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>28</td>\n",
+              "      <td>45</td>\n",
+              "      <td>28_overhead_door_damaged_loader</td>\n",
+              "      <td>[overhead, door, damaged, loader, hangar, fram...</td>\n",
+              "      <td>[overhead door damaged                        ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>29</td>\n",
+              "      <td>45</td>\n",
+              "      <td>29_wind_trees_fence_park</td>\n",
+              "      <td>[wind, trees, fence, park, fencing, shed, stor...</td>\n",
+              "      <td>[wind damage at O'Donnell Park                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>30</td>\n",
+              "      <td>45</td>\n",
+              "      <td>30_street_light_damaged_accident</td>\n",
+              "      <td>[street, light, damaged, accident, vehicle, kn...</td>\n",
+              "      <td>[street light damaged                         ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>31</td>\n",
+              "      <td>44</td>\n",
+              "      <td>31_gym_floor_leak_roof</td>\n",
+              "      <td>[gym, floor, leak, roof, k9, injured, training...</td>\n",
+              "      <td>[water damage to gym floor                    ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>32</td>\n",
+              "      <td>44</td>\n",
+              "      <td>32_sign_vehicle_signal_traffic</td>\n",
+              "      <td>[sign, vehicle, signal, traffic, struck, hit, ...</td>\n",
+              "      <td>[vehicle struck and damaged traffic signal    ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>33</td>\n",
+              "      <td>43</td>\n",
+              "      <td>33_hs_lightning_hhs_lec</td>\n",
+              "      <td>[hs, lightning, hhs, lec, damage, at, lighning...</td>\n",
+              "      <td>[lightning damage at HS                       ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>34</td>\n",
+              "      <td>41</td>\n",
+              "      <td>34_school_elementary_water_high</td>\n",
+              "      <td>[school, elementary, water, high, damage, elem...</td>\n",
+              "      <td>[water damage at school                       ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>35</td>\n",
+              "      <td>39</td>\n",
+              "      <td>35_storm_bldgs_locations_multiple</td>\n",
+              "      <td>[storm, bldgs, locations, multiple, damage, fa...</td>\n",
+              "      <td>[storm damage                                 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>36</td>\n",
+              "      <td>37</td>\n",
+              "      <td>36_hs_water_tremper_reuther</td>\n",
+              "      <td>[hs, water, tremper, reuther, hcc, mats, damag...</td>\n",
+              "      <td>[water damage at HS                           ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>37</td>\n",
+              "      <td>37</td>\n",
+              "      <td>37_radio_antenna_lightning_radios</td>\n",
+              "      <td>[radio, antenna, lightning, radios, to, freque...</td>\n",
+              "      <td>[lightning damage to radio                    ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>38</td>\n",
+              "      <td>36</td>\n",
+              "      <td>38_water_carpet_equipment_computers</td>\n",
+              "      <td>[water, carpet, equipment, computers, to, copi...</td>\n",
+              "      <td>[water damage to equipment                    ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>39</td>\n",
+              "      <td>35</td>\n",
+              "      <td>39_toki_courthouse_ms_damagecourthouse</td>\n",
+              "      <td>[toki, courthouse, ms, damagecourthouse, glass...</td>\n",
+              "      <td>[glass vandalism at Toki MS                   ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>40</td>\n",
+              "      <td>34</td>\n",
+              "      <td>40_center_water_bldg_main</td>\n",
+              "      <td>[center, water, bldg, main, sinkhole, health, ...</td>\n",
+              "      <td>[water damage at Comm Center                  ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>41</td>\n",
+              "      <td>34</td>\n",
+              "      <td>41_ms_es_lightning_damage</td>\n",
+              "      <td>[ms, es, lightning, damage, micrologix, mms, w...</td>\n",
+              "      <td>[lightning damage at MS                       ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>42</td>\n",
+              "      <td>34</td>\n",
+              "      <td>42_street_pole_light_streetlight</td>\n",
+              "      <td>[street, pole, light, streetlight, damaged, du...</td>\n",
+              "      <td>[street light pole damaged                    ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>43</td>\n",
+              "      <td>33</td>\n",
+              "      <td>43_well_10_lightning_wells</td>\n",
+              "      <td>[well, 10, lightning, wells, house, monitoring...</td>\n",
+              "      <td>[lightning damage to well 5                   ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>44</td>\n",
+              "      <td>31</td>\n",
+              "      <td>44_hydrant_vehicle_struck_hit</td>\n",
+              "      <td>[hydrant, vehicle, struck, hit, over, by, car,...</td>\n",
+              "      <td>[hydrant damaged                              ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>45</td>\n",
+              "      <td>31</td>\n",
+              "      <td>45_plant_reservoir_tower_lightning</td>\n",
+              "      <td>[plant, reservoir, tower, lightning, wastewate...</td>\n",
+              "      <td>[lightning damage to sewer plant              ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>46</td>\n",
+              "      <td>31</td>\n",
+              "      <td>46_falk_follette_la_glass</td>\n",
+              "      <td>[falk, follette, la, glass, es, vandalism, sta...</td>\n",
+              "      <td>[glass vandalism at Falk ES                   ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>47</td>\n",
+              "      <td>30</td>\n",
+              "      <td>47_radio_dropped_radios_lost</td>\n",
+              "      <td>[radio, dropped, radios, lost, portable, when,...</td>\n",
+              "      <td>[radio damaged                                ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>48</td>\n",
+              "      <td>30</td>\n",
+              "      <td>48_lift_station_elevator_lightning</td>\n",
+              "      <td>[lift, station, elevator, lightning, stations,...</td>\n",
+              "      <td>[lightning damage at lift station             ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>49</td>\n",
+              "      <td>30</td>\n",
+              "      <td>49_buildings_building_water_basement</td>\n",
+              "      <td>[buildings, building, water, basement, to, abo...</td>\n",
+              "      <td>[water damage to building                     ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+            ]
           },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
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-           "zerolinewidth": 2
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "np.random.seed(42)\n",
+        "import random\n",
+        "random.seed(42)\n",
+        "umap_model = UMAP(n_neighbors=15, n_components=10, metric='cosine', low_memory=False, random_state=42)\n",
+        "hdbscan_model = HDBSCAN(min_cluster_size=30, metric='euclidean', prediction_data=True, min_samples=1)\n",
+        "topic_model = BERTopic(umap_model=umap_model, hdbscan_model=hdbscan_model)\n",
+        "topics, probs = topic_model.fit_transform(df_train[\"Description\"])\n",
+        "topic_model.get_topic_info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LcmIftTnEVHJ"
+      },
+      "source": [
+        "The first output of `fit_transform` holds the topic ID for each sample. The second output is the probability of the sample belonging to that topic.\n",
+        "\n",
+        "In our case, we have obtained ca. 50 clusters. due to randomness of UMAP, the results may differ between runs. Unfortunately, we have not found a way to fix this.\n",
+        "\n",
+        "The cluster with ID `-1` contains all samples which are considered \"noise\" because they were not attributed to any cluster.\n",
+        "\n",
+        "The function `get_topic_info` returns the topic ID, the sample count, and a concatenation of the words representing the cluster.\n",
+        "\n",
+        "To get a visual impression of the clusters, BERTopic provides the function `visualize_topics` which embeds the c-TF-IDF representation of the topics in 2D using UMAP and then visualizes the two dimensions using plotly in an interactive view."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 667
+        },
+        "id": "q5WOiwszEVHJ",
+        "outputId": "6f005b1b-5531-4fab-c886-a41662004f95"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "\n",
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+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+              "    x.observe(notebookContainer, {childList: true});\n",
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+      "source": [
+        "topic_model.visualize_topics()"
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+      "source": [
+        "We can visualize the selected terms for a few topics by creating bar charts out of the c-TF-IDF scores for each topic representation.\n",
+        "Insights can be gained from the relative c-TF-IDF scores between and within topics. Moreover, you can easily compare topic representations to each other. To visualize this hierarchy, simply call  the function `visualize_barchart`:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = evaluate_classifier(np.array(df_valid[\"labels\"]), pred, None, labels, \"Similarity\", \"cm_peril_sim_a\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rBb2NUiVh71w"
-   },
-   "source": [
-    "This is not bad! We have already improved on the accuracy score obtained by the zero-shot classifier (using a different set of candidate expressions though).\n",
-    "\n",
-    "Let’s see how we can improve the results further."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CEEIA6IT3lZE"
-   },
-   "source": [
-    "<a id='similarity_refinement'></a>\n",
-    "<a name='similarity_refinement'></a>\n",
-    "### 4.2. Refinement\n",
-    "\n",
-    "A possible way to improve the performance is to train a classifier using the predicted labels of the previous section. Although this is a supervised learning step, we are not using the original labels, therefore the overall approach is still unsupervised."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "M4rgq9pLiJku"
-   },
-   "source": [
-    "First, we predict the labels on the training set, the same way as before, and store them in the DataFrame `df_train_copy`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "id": "NjT3zaHM3sNv"
-   },
-   "outputs": [],
-   "source": [
-    "x_train = np.array(ds_sim[\"train\"][\"mean_hidden_state\"])\n",
-    "scores = np.dot(x_train, y.T)\n",
-    "selected = np.argmax(scores, axis=1)\n",
-    "pred = np.array([list(candidates.values())[i] for i in selected])\n",
-    "\n",
-    "df_train_copy  = df_train.copy()\n",
-    "df_train_copy[\"labels\"] = pred"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uNcUCbopiSMA"
-   },
-   "source": [
-    "From here, we follow the approach of [Section 2](#supervised). Again, we use ` distilbert-base-uncased`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "AKStE1hD8s8g",
-    "outputId": "0a3d9d81-74ff-4e3a-c6ab-f8cdeacd32a0"
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n",
-      "loading file https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt from cache at /home/ubuntu/.cache/huggingface/transformers/0e1bbfda7f63a99bb52e3915dcf10c3c92122b827d92eb2d34ce94ee79ba486c.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
-      "loading file https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json from cache at /home/ubuntu/.cache/huggingface/transformers/75abb59d7a06f4f640158a9bfcde005264e59e8d566781ab1415b139d2e4c603.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
-      "loading file https://huggingface.co/distilbert-base-uncased/resolve/main/added_tokens.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-uncased/resolve/main/special_tokens_map.json from cache at None\n",
-      "loading file https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/transformers/8c8624b8ac8aa99c60c912161f8332de003484428c47906d7ff7eb7f73eecdbb.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79\n",
-      "loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name = \"distilbert-base-uncased\"\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yzX4H1CrijUf"
-   },
-   "source": [
-    "First, we tokenize the claim descriptions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
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-     "base_uri": "https://localhost:8080/",
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+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 342
+        },
+        "id": "Gd0yT5hMEVHJ",
+        "outputId": "583785e4-2541-42a3-800e-0bd3b1920930"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
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+        "topic_model.visualize_barchart(top_n_topics=4)"
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-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?ba/s]"
+      "source": [
+        "BERTopics creates topics in a hierarchical structure. The function `visualize_hierarchy` displays the hierarchy. This information is useful to reduce the number of topics, either by specifying a value for the parameter `nr_topics` upon instantiation of BERTopic, or after the training by calling the function `reduce_topics`."
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+      "source": [
+        "topic_model.visualize_hierarchy()"
       ]
-     },
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-   "source": [
-    "ds_train_copy = Dataset.from_pandas(df_train_copy)\n",
-    "ds_train_copy = ds_train_copy.map(tokenize, batched=True)"
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-   "cell_type": "markdown",
-   "metadata": {
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-   "source": [
-    "Then, we perform one epoch of training…"
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-   "metadata": {
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-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"LABEL_0\",\n",
-      "    \"1\": \"LABEL_1\",\n",
-      "    \"2\": \"LABEL_2\",\n",
-      "    \"3\": \"LABEL_3\",\n",
-      "    \"4\": \"LABEL_4\",\n",
-      "    \"5\": \"LABEL_5\",\n",
-      "    \"6\": \"LABEL_6\",\n",
-      "    \"7\": \"LABEL_7\",\n",
-      "    \"8\": \"LABEL_8\"\n",
-      "  },\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"label2id\": {\n",
-      "    \"LABEL_0\": 0,\n",
-      "    \"LABEL_1\": 1,\n",
-      "    \"LABEL_2\": 2,\n",
-      "    \"LABEL_3\": 3,\n",
-      "    \"LABEL_4\": 4,\n",
-      "    \"LABEL_5\": 5,\n",
-      "    \"LABEL_6\": 6,\n",
-      "    \"LABEL_7\": 7,\n",
-      "    \"LABEL_8\": 8\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, WaterNW, Vandalism, words per description, Fire, Hail, Vehicle, Misc, Description, Loss. If Lightning, WaterW, Wind, WaterNW, Vandalism, words per description, Fire, Hail, Vehicle, Misc, Description, Loss are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running training *****\n",
-      "  Num examples = 4991\n",
-      "  Num Epochs = 1\n",
-      "  Instantaneous batch size per device = 8\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 624\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QR332SW6EVHK"
+      },
+      "source": [
+        "Next, we want to assign labels to each cluster.\n",
+        "Compared to manually labeling thousands of samples, this task is much less burdensome!\n",
+        "\n",
+        "This is usually a manual task. Assignment of labels is guided by the topic information, the topic word scores and the hierarchical clustering.\n",
+        "\n",
+        "In our case, the actual labels are available, so that we can use this information to perform the labeling.\n",
+        "\n",
+        "Let's inspect how well the clusters matches the labels. The graph below shows one column per topic.\n",
+        "The shading indicates the distribution of labels within a given topic.\n",
+        "The presence of a single dark patch in a column indicates that almost all of the samples of the topic are associated with a single label."
+      ]
     },
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-       "      \n",
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-       "      [624/624 00:27, Epoch 1/1]\n",
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-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
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-       "    <tr>\n",
-       "      <td>623</td>\n",
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-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "df_train[\"Topic\"] = topics\n",
+        "tb = pd.pivot_table(df_train, index=[\"Topic\"], columns=[\"labels\"], aggfunc='count', fill_value=0)[\"Description\"]\n",
+        "fig = px.imshow(tb.divide(tb.sum(axis=1), axis=0).T, zmin=-0.05)\n",
+        "fig.update_layout(xaxis={\"dtick\": 1}, yaxis={\"dtick\": 1, \"range\":[0,8]}, coloraxis={\"colorscale\": \"Greys\"})\n",
+        "fig.show()"
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-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to distilbert-base-uncased_peril_s\n",
-      "Configuration saved in distilbert-base-uncased_peril_s/config.json\n",
-      "Model weights saved in distilbert-base-uncased_peril_s/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
-    "\n",
-    "# train the model\n",
-    "batch_size = 8\n",
-    "logging_steps = len(ds_train_copy) // batch_size\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=model_name+\"_peril_s_epochs\",\n",
-    "    num_train_epochs=1,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    metric_for_best_model=\"f1\",\n",
-    "    logging_steps=logging_steps,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "trainer = Trainer(model=model, args=training_args,\n",
-    "                  compute_metrics=compute_metrics, train_dataset=ds_train_copy,\n",
-    "                  eval_dataset=ds_train_copy)\n",
-    "trainer.train();\n",
-    "trainer.save_model(model_name + \"_peril_s\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Zb40jPS-iulN"
-   },
-   "source": [
-    "… and evaluate the performance on the test set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cLoeG-9tEVHK"
+      },
+      "source": [
+        "Obviously, the topic `-1`, which represents the outliers, has a finite frequency for many classes.\n",
+        "Further, the classes 6 (`WaterNW`) and 7 (`WaterW`) seem to be difficult to tell apart from the clusters; this affects some of the topics.\n",
+        "For most other topics, the clustering aligns quite well with the labels.\n",
+        "\n",
+        "Overall, it appears reasonable to map each topic to the label with the highest frequency. Apart from the exceptions mentioned above, this aligns with a mapping that a human would define manually, in absence of the actual labels.\n",
+        "\n",
+        "Therefore, let's define the mapping from topics to labels by picking the label with the highest frequency. The table below shows the topic info, enriched with the label counts and the mapping."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "NZ6vPSS-EVHK",
+        "outputId": "3fbba007-530f-4089-f28a-c5d83f373586"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Topic  Count                                    Name  \\\n",
+              "0      -1    662          -1_lightning_water_damage_wwtp   \n",
+              "1       0    700            0_vandalism_park_dmg_shelter   \n",
+              "2       1    237               1_glass_vandalism_west_es   \n",
+              "3       2    203           2_fire_smoke_damage_equipment   \n",
+              "4       3    180             3_phone_lightning_system_to   \n",
+              "5       4    178        4_power_surge_generator_spoilage   \n",
+              "6       5    177                5_froze_pipe_sewer_pipes   \n",
+              "7       6    164                 6_theft_of_stolen_break   \n",
+              "8       7    146              7_graffiti_on_kennedy_hoyt   \n",
+              "9       8    121            8_lightning_damage_scale_dpw   \n",
+              "10      9    104            9_signal_traffic_damaged_box   \n",
+              "11     10    103           10_broken_door_glass_breakage   \n",
+              "12     11    101                   11_fence_plow_gate_by   \n",
+              "13     12     93              12_roof_wind_shingles_blew   \n",
+              "14     13     83             13_hydrant_fire_hit_damaged   \n",
+              "15     14     81            14_wind_storage_damage_tower   \n",
+              "16     15     81               15_llm_glass_mendota_hawk   \n",
+              "17     16     78            16_building_truck_vehicle_by   \n",
+              "18     17     76            17_water_damage_goodman_pool   \n",
+              "19     18     75                18_garage_door_truck_hwy   \n",
+              "20     19     74         19_window_windows_broken_thrown   \n",
+              "21     20     72         20_hail_buildings_roof_multiple   \n",
+              "22     21     67         21_light_pole_damaged_lightpole   \n",
+              "23     22     63              22_pole_vehicle_hit_struck   \n",
+              "24     23     58                 23_laptop_theft_of_from   \n",
+              "25     24     54             24_hwy_lightning_st_highway   \n",
+              "26     25     52     25_laptop_bradford_computer_damaged   \n",
+              "27     26     51      26_airport_lightning_lights_runway   \n",
+              "28     27     48  27_equipment_playground_instrument_gps   \n",
+              "29     28     45         28_overhead_door_damaged_loader   \n",
+              "30     29     45                29_wind_trees_fence_park   \n",
+              "31     30     45        30_street_light_damaged_accident   \n",
+              "32     31     44                  31_gym_floor_leak_roof   \n",
+              "33     32     44          32_sign_vehicle_signal_traffic   \n",
+              "34     33     43                 33_hs_lightning_hhs_lec   \n",
+              "35     34     41         34_school_elementary_water_high   \n",
+              "36     35     39       35_storm_bldgs_locations_multiple   \n",
+              "37     36     37             36_hs_water_tremper_reuther   \n",
+              "38     37     37       37_radio_antenna_lightning_radios   \n",
+              "39     38     36     38_water_carpet_equipment_computers   \n",
+              "40     39     35  39_toki_courthouse_ms_damagecourthouse   \n",
+              "41     40     34               40_center_water_bldg_main   \n",
+              "42     41     34               41_ms_es_lightning_damage   \n",
+              "43     42     34        42_street_pole_light_streetlight   \n",
+              "44     43     33              43_well_10_lightning_wells   \n",
+              "45     44     31           44_hydrant_vehicle_struck_hit   \n",
+              "46     45     31      45_plant_reservoir_tower_lightning   \n",
+              "47     46     31               46_falk_follette_la_glass   \n",
+              "48     47     30            47_radio_dropped_radios_lost   \n",
+              "49     48     30      48_lift_station_elevator_lightning   \n",
+              "50     49     30    49_buildings_building_water_basement   \n",
+              "\n",
+              "                                       Representation  \\\n",
+              "0   [lightning, water, damage, wwtp, ms, siren, at...   \n",
+              "1   [vandalism, park, dmg, shelter, at, pavilion, ...   \n",
+              "2   [glass, vandalism, west, es, ms, at, lincoln, ...   \n",
+              "3   [fire, smoke, damage, equipment, station, park...   \n",
+              "4   [phone, lightning, system, to, compressor, com...   \n",
+              "5   [power, surge, generator, spoilage, food, outa...   \n",
+              "6   [froze, pipe, sewer, pipes, library, ice, up, ...   \n",
+              "7   [theft, of, stolen, break, from, in, wire, cam...   \n",
+              "8   [graffiti, on, kennedy, hoyt, llm, wall, doors...   \n",
+              "9   [lightning, damage, scale, dpw, museum, nasonv...   \n",
+              "10  [signal, traffic, damaged, box, paradise, knoc...   \n",
+              "11  [broken, door, glass, breakage, entrance, brok...   \n",
+              "12  [fence, plow, gate, by, damaged, vehicle, snow...   \n",
+              "13  [roof, wind, shingles, blew, collapsed, off, w...   \n",
+              "14  [hydrant, fire, hit, damaged, run, plow, vehic...   \n",
+              "15  [wind, storage, damage, tower, antenna, to, te...   \n",
+              "16  [llm, glass, mendota, hawk, black, whitehorse,...   \n",
+              "17  [building, truck, vehicle, by, damaged, bldg, ...   \n",
+              "18  [water, damage, goodman, pool, at, field, toki...   \n",
+              "19  [garage, door, truck, hwy, damaged, shop, over...   \n",
+              "20  [window, windows, broken, thrown, screens, scr...   \n",
+              "21  [hail, buildings, roof, multiple, to, windhail...   \n",
+              "22  [light, pole, damaged, lightpole, rawson, pole...   \n",
+              "23  [pole, vehicle, hit, struck, light, utility, a...   \n",
+              "24  [laptop, theft, of, from, stolen, ita, compute...   \n",
+              "25  [hwy, lightning, st, highway, dept, main, shop...   \n",
+              "26  [laptop, bradford, computer, damaged, mckinley...   \n",
+              "27  [airport, lightning, lights, runway, damage, b...   \n",
+              "28  [equipment, playground, instrument, gps, gear,...   \n",
+              "29  [overhead, door, damaged, loader, hangar, fram...   \n",
+              "30  [wind, trees, fence, park, fencing, shed, stor...   \n",
+              "31  [street, light, damaged, accident, vehicle, kn...   \n",
+              "32  [gym, floor, leak, roof, k9, injured, training...   \n",
+              "33  [sign, vehicle, signal, traffic, struck, hit, ...   \n",
+              "34  [hs, lightning, hhs, lec, damage, at, lighning...   \n",
+              "35  [school, elementary, water, high, damage, elem...   \n",
+              "36  [storm, bldgs, locations, multiple, damage, fa...   \n",
+              "37  [hs, water, tremper, reuther, hcc, mats, damag...   \n",
+              "38  [radio, antenna, lightning, radios, to, freque...   \n",
+              "39  [water, carpet, equipment, computers, to, copi...   \n",
+              "40  [toki, courthouse, ms, damagecourthouse, glass...   \n",
+              "41  [center, water, bldg, main, sinkhole, health, ...   \n",
+              "42  [ms, es, lightning, damage, micrologix, mms, w...   \n",
+              "43  [street, pole, light, streetlight, damaged, du...   \n",
+              "44  [well, 10, lightning, wells, house, monitoring...   \n",
+              "45  [hydrant, vehicle, struck, hit, over, by, car,...   \n",
+              "46  [plant, reservoir, tower, lightning, wastewate...   \n",
+              "47  [falk, follette, la, glass, es, vandalism, sta...   \n",
+              "48  [radio, dropped, radios, lost, portable, when,...   \n",
+              "49  [lift, station, elevator, lightning, stations,...   \n",
+              "50  [buildings, building, water, basement, to, abo...   \n",
+              "\n",
+              "                                  Representative_Docs    0    1    2   3   4  \\\n",
+              "0   [theft of laptop Washington MS                ...  117    5  225  59   1   \n",
+              "1   [a/c vandalism                                ...  694    1    0   4   0   \n",
+              "2   [glass vandalism at HS                        ...  237    0    0   0   0   \n",
+              "3   [fire & smoke damage at Fire Station #7       ...   23  147    4   0   0   \n",
+              "4   [lightning damaged phone system               ...    4    4  150   1   0   \n",
+              "5   [power surge damage                           ...    0    4   23   3   0   \n",
+              "6   [pipe froze and water damage                  ...    3    0    1   3   0   \n",
+              "7   [theft of equipment from vehicle              ...  130    2    1   0   0   \n",
+              "8   [graffiti at West                             ...  143    0    0   0   0   \n",
+              "9   [lightning damage                             ...    0    0  120   0   1   \n",
+              "10  [traffic signal damaged                       ...    0    0    0   0   0   \n",
+              "11  [broken door/glass at LaFollette HS           ...   92    0    0   1   0   \n",
+              "12  [fence gate damaged by vehicle                ...    6    0    0   7   0   \n",
+              "13  [wind damage to roof                          ...    2    1    0  64   1   \n",
+              "14  [fire hydrant damaged                         ...    0    0    0   0   0   \n",
+              "15  [wind damage                                  ...    0    0    0  78   0   \n",
+              "16  [LaFollette - llm-12118 - glass door          ...   73    0    2   0   0   \n",
+              "17  [HS building damaged by truck                 ...    3    0    0   6   0   \n",
+              "18  [water damage                                 ...    0    0    0   0   0   \n",
+              "19  [garage door damaged                          ...    1    0    0   0   0   \n",
+              "20  [broken window                                ...   64    1    0   1   1   \n",
+              "21  [hail damage                                  ...    0    0    0   4  68   \n",
+              "22  [light pole damaged                           ...    5    1    1   0   1   \n",
+              "23  [vehicle damaged light pole                   ...    0    0    0   0   1   \n",
+              "24  [theft of laptop                              ...   51    0    0   0   0   \n",
+              "25  [lightning damage at hwy dept.                ...    0    0   53   0   0   \n",
+              "26  [laptop #6 damaged                            ...   38    0    0   0   0   \n",
+              "27  [lightning damage at airport                  ...    0    1   46   1   0   \n",
+              "28  [playground equipment damaged                 ...   14    0    0   1   0   \n",
+              "29  [overhead door damaged                        ...    3    0    0   1   0   \n",
+              "30  [wind damage at O'Donnell Park                ...    2    0    0  41   0   \n",
+              "31  [street light damaged                         ...    4    0    0   2   0   \n",
+              "32  [water damage to gym floor                    ...    4    0    0   0   0   \n",
+              "33  [vehicle struck and damaged traffic signal    ...    4    0    0   0   0   \n",
+              "34  [lightning damage at HS                       ...    0    0   43   0   0   \n",
+              "35  [water damage at school                       ...    1    0    0   0   0   \n",
+              "36  [storm damage                                 ...    0    0    6  17   2   \n",
+              "37  [water damage at HS                           ...    0    0    0   0   0   \n",
+              "38  [lightning damage to radio                    ...    0    1   36   0   0   \n",
+              "39  [water damage to equipment                    ...    0    0    1   0   0   \n",
+              "40  [glass vandalism at Toki MS                   ...   19    0    0   0   0   \n",
+              "41  [water damage at Comm Center                  ...    0    0    0   0   0   \n",
+              "42  [lightning damage at MS                       ...    0    1   33   0   0   \n",
+              "43  [street light pole damaged                    ...    0    0    0   0   0   \n",
+              "44  [lightning damage to well 5                   ...    0    0   31   0   0   \n",
+              "45  [hydrant damaged                              ...    2    0    0   0   0   \n",
+              "46  [lightning damage to sewer plant              ...    0    0   31   0   0   \n",
+              "47  [glass vandalism at Falk ES                   ...   30    0    0   0   0   \n",
+              "48  [radio damaged                                ...    3    2    0   1   0   \n",
+              "49  [lightning damage at lift station             ...    1    0   25   0   0   \n",
+              "50  [water damage to building                     ...    1    0    0   1   0   \n",
+              "\n",
+              "      5   6   7    8  mapping      label  \n",
+              "0    83  59  86   27        2  Lightning  \n",
+              "1     1   0   0    0        0  Vandalism  \n",
+              "2     0   0   0    0        0  Vandalism  \n",
+              "3    10   5   5    9        1       Fire  \n",
+              "4     2   1   5   13        2  Lightning  \n",
+              "5     1   1   2  144        8       Misc  \n",
+              "6    10  32  98   30        7     WaterW  \n",
+              "7     6   1   0   24        0  Vandalism  \n",
+              "8     2   0   1    0        0  Vandalism  \n",
+              "9     0   0   0    0        2  Lightning  \n",
+              "10  101   0   0    3        5    Vehicle  \n",
+              "11    4   0   0    6        0  Vandalism  \n",
+              "12   85   0   0    3        5    Vehicle  \n",
+              "13    0   1  10   14        3       Wind  \n",
+              "14   81   1   0    1        5    Vehicle  \n",
+              "15    0   0   1    2        3       Wind  \n",
+              "16    2   0   1    3        0  Vandalism  \n",
+              "17   60   0   2    7        5    Vehicle  \n",
+              "18    0  25  50    1        7     WaterW  \n",
+              "19   71   0   0    3        5    Vehicle  \n",
+              "20    2   0   2    3        0  Vandalism  \n",
+              "21    0   0   0    0        4       Hail  \n",
+              "22   57   0   0    2        5    Vehicle  \n",
+              "23   60   0   0    2        5    Vehicle  \n",
+              "24    1   0   0    6        0  Vandalism  \n",
+              "25    0   0   0    1        2  Lightning  \n",
+              "26    5   0   0    9        0  Vandalism  \n",
+              "27    2   0   1    0        2  Lightning  \n",
+              "28   13   0   0   20        8       Misc  \n",
+              "29   39   0   0    2        5    Vehicle  \n",
+              "30    1   0   1    0        3       Wind  \n",
+              "31   39   0   0    0        5    Vehicle  \n",
+              "32    0   9  23    8        7     WaterW  \n",
+              "33   40   0   0    0        5    Vehicle  \n",
+              "34    0   0   0    0        2  Lightning  \n",
+              "35    0   9  30    1        7     WaterW  \n",
+              "36    0   0  13    1        3       Wind  \n",
+              "37    0  11  25    1        7     WaterW  \n",
+              "38    0   0   0    0        2  Lightning  \n",
+              "39    0  10  24    1        7     WaterW  \n",
+              "40    0   6   9    1        0  Vandalism  \n",
+              "41    1  13  19    1        7     WaterW  \n",
+              "42    0   0   0    0        2  Lightning  \n",
+              "43   33   0   0    1        5    Vehicle  \n",
+              "44    2   0   0    0        2  Lightning  \n",
+              "45   28   1   0    0        5    Vehicle  \n",
+              "46    0   0   0    0        2  Lightning  \n",
+              "47    0   0   0    1        0  Vandalism  \n",
+              "48    8   5   1   10        8       Misc  \n",
+              "49    1   1   1    1        2  Lightning  \n",
+              "50    1  11  16    0        7     WaterW  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-784a6c50-513a-4fa9-8114-7b5694a51c82\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Topic</th>\n",
+              "      <th>Count</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Representation</th>\n",
+              "      <th>Representative_Docs</th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>mapping</th>\n",
+              "      <th>label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-1</td>\n",
+              "      <td>662</td>\n",
+              "      <td>-1_lightning_water_damage_wwtp</td>\n",
+              "      <td>[lightning, water, damage, wwtp, ms, siren, at...</td>\n",
+              "      <td>[theft of laptop Washington MS                ...</td>\n",
+              "      <td>117</td>\n",
+              "      <td>5</td>\n",
+              "      <td>225</td>\n",
+              "      <td>59</td>\n",
+              "      <td>1</td>\n",
+              "      <td>83</td>\n",
+              "      <td>59</td>\n",
+              "      <td>86</td>\n",
+              "      <td>27</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>700</td>\n",
+              "      <td>0_vandalism_park_dmg_shelter</td>\n",
+              "      <td>[vandalism, park, dmg, shelter, at, pavilion, ...</td>\n",
+              "      <td>[a/c vandalism                                ...</td>\n",
+              "      <td>694</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>237</td>\n",
+              "      <td>1_glass_vandalism_west_es</td>\n",
+              "      <td>[glass, vandalism, west, es, ms, at, lincoln, ...</td>\n",
+              "      <td>[glass vandalism at HS                        ...</td>\n",
+              "      <td>237</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2</td>\n",
+              "      <td>203</td>\n",
+              "      <td>2_fire_smoke_damage_equipment</td>\n",
+              "      <td>[fire, smoke, damage, equipment, station, park...</td>\n",
+              "      <td>[fire &amp; smoke damage at Fire Station #7       ...</td>\n",
+              "      <td>23</td>\n",
+              "      <td>147</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Fire</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>3</td>\n",
+              "      <td>180</td>\n",
+              "      <td>3_phone_lightning_system_to</td>\n",
+              "      <td>[phone, lightning, system, to, compressor, com...</td>\n",
+              "      <td>[lightning damaged phone system               ...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>150</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>13</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4</td>\n",
+              "      <td>178</td>\n",
+              "      <td>4_power_surge_generator_spoilage</td>\n",
+              "      <td>[power, surge, generator, spoilage, food, outa...</td>\n",
+              "      <td>[power surge damage                           ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>23</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>144</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Misc</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>5</td>\n",
+              "      <td>177</td>\n",
+              "      <td>5_froze_pipe_sewer_pipes</td>\n",
+              "      <td>[froze, pipe, sewer, pipes, library, ice, up, ...</td>\n",
+              "      <td>[pipe froze and water damage                  ...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>32</td>\n",
+              "      <td>98</td>\n",
+              "      <td>30</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>6</td>\n",
+              "      <td>164</td>\n",
+              "      <td>6_theft_of_stolen_break</td>\n",
+              "      <td>[theft, of, stolen, break, from, in, wire, cam...</td>\n",
+              "      <td>[theft of equipment from vehicle              ...</td>\n",
+              "      <td>130</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>7</td>\n",
+              "      <td>146</td>\n",
+              "      <td>7_graffiti_on_kennedy_hoyt</td>\n",
+              "      <td>[graffiti, on, kennedy, hoyt, llm, wall, doors...</td>\n",
+              "      <td>[graffiti at West                             ...</td>\n",
+              "      <td>143</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>8</td>\n",
+              "      <td>121</td>\n",
+              "      <td>8_lightning_damage_scale_dpw</td>\n",
+              "      <td>[lightning, damage, scale, dpw, museum, nasonv...</td>\n",
+              "      <td>[lightning damage                             ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>9</td>\n",
+              "      <td>104</td>\n",
+              "      <td>9_signal_traffic_damaged_box</td>\n",
+              "      <td>[signal, traffic, damaged, box, paradise, knoc...</td>\n",
+              "      <td>[traffic signal damaged                       ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>101</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>10</td>\n",
+              "      <td>103</td>\n",
+              "      <td>10_broken_door_glass_breakage</td>\n",
+              "      <td>[broken, door, glass, breakage, entrance, brok...</td>\n",
+              "      <td>[broken door/glass at LaFollette HS           ...</td>\n",
+              "      <td>92</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>11</td>\n",
+              "      <td>101</td>\n",
+              "      <td>11_fence_plow_gate_by</td>\n",
+              "      <td>[fence, plow, gate, by, damaged, vehicle, snow...</td>\n",
+              "      <td>[fence gate damaged by vehicle                ...</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>85</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>12_roof_wind_shingles_blew</td>\n",
+              "      <td>[roof, wind, shingles, blew, collapsed, off, w...</td>\n",
+              "      <td>[wind damage to roof                          ...</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>64</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>14</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wind</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>13</td>\n",
+              "      <td>83</td>\n",
+              "      <td>13_hydrant_fire_hit_damaged</td>\n",
+              "      <td>[hydrant, fire, hit, damaged, run, plow, vehic...</td>\n",
+              "      <td>[fire hydrant damaged                         ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>81</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>14</td>\n",
+              "      <td>81</td>\n",
+              "      <td>14_wind_storage_damage_tower</td>\n",
+              "      <td>[wind, storage, damage, tower, antenna, to, te...</td>\n",
+              "      <td>[wind damage                                  ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>78</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wind</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>15</td>\n",
+              "      <td>81</td>\n",
+              "      <td>15_llm_glass_mendota_hawk</td>\n",
+              "      <td>[llm, glass, mendota, hawk, black, whitehorse,...</td>\n",
+              "      <td>[LaFollette - llm-12118 - glass door          ...</td>\n",
+              "      <td>73</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>16</td>\n",
+              "      <td>78</td>\n",
+              "      <td>16_building_truck_vehicle_by</td>\n",
+              "      <td>[building, truck, vehicle, by, damaged, bldg, ...</td>\n",
+              "      <td>[HS building damaged by truck                 ...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>60</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>17</td>\n",
+              "      <td>76</td>\n",
+              "      <td>17_water_damage_goodman_pool</td>\n",
+              "      <td>[water, damage, goodman, pool, at, field, toki...</td>\n",
+              "      <td>[water damage                                 ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>18</td>\n",
+              "      <td>75</td>\n",
+              "      <td>18_garage_door_truck_hwy</td>\n",
+              "      <td>[garage, door, truck, hwy, damaged, shop, over...</td>\n",
+              "      <td>[garage door damaged                          ...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>71</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>19</td>\n",
+              "      <td>74</td>\n",
+              "      <td>19_window_windows_broken_thrown</td>\n",
+              "      <td>[window, windows, broken, thrown, screens, scr...</td>\n",
+              "      <td>[broken window                                ...</td>\n",
+              "      <td>64</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>20</td>\n",
+              "      <td>72</td>\n",
+              "      <td>20_hail_buildings_roof_multiple</td>\n",
+              "      <td>[hail, buildings, roof, multiple, to, windhail...</td>\n",
+              "      <td>[hail damage                                  ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>68</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Hail</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>21</td>\n",
+              "      <td>67</td>\n",
+              "      <td>21_light_pole_damaged_lightpole</td>\n",
+              "      <td>[light, pole, damaged, lightpole, rawson, pole...</td>\n",
+              "      <td>[light pole damaged                           ...</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>22</td>\n",
+              "      <td>63</td>\n",
+              "      <td>22_pole_vehicle_hit_struck</td>\n",
+              "      <td>[pole, vehicle, hit, struck, light, utility, a...</td>\n",
+              "      <td>[vehicle damaged light pole                   ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>60</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>23</td>\n",
+              "      <td>58</td>\n",
+              "      <td>23_laptop_theft_of_from</td>\n",
+              "      <td>[laptop, theft, of, from, stolen, ita, compute...</td>\n",
+              "      <td>[theft of laptop                              ...</td>\n",
+              "      <td>51</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>24</td>\n",
+              "      <td>54</td>\n",
+              "      <td>24_hwy_lightning_st_highway</td>\n",
+              "      <td>[hwy, lightning, st, highway, dept, main, shop...</td>\n",
+              "      <td>[lightning damage at hwy dept.                ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>53</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>25</td>\n",
+              "      <td>52</td>\n",
+              "      <td>25_laptop_bradford_computer_damaged</td>\n",
+              "      <td>[laptop, bradford, computer, damaged, mckinley...</td>\n",
+              "      <td>[laptop #6 damaged                            ...</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>26</td>\n",
+              "      <td>51</td>\n",
+              "      <td>26_airport_lightning_lights_runway</td>\n",
+              "      <td>[airport, lightning, lights, runway, damage, b...</td>\n",
+              "      <td>[lightning damage at airport                  ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>46</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>27</td>\n",
+              "      <td>48</td>\n",
+              "      <td>27_equipment_playground_instrument_gps</td>\n",
+              "      <td>[equipment, playground, instrument, gps, gear,...</td>\n",
+              "      <td>[playground equipment damaged                 ...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Misc</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>28</td>\n",
+              "      <td>45</td>\n",
+              "      <td>28_overhead_door_damaged_loader</td>\n",
+              "      <td>[overhead, door, damaged, loader, hangar, fram...</td>\n",
+              "      <td>[overhead door damaged                        ...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>39</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>29</td>\n",
+              "      <td>45</td>\n",
+              "      <td>29_wind_trees_fence_park</td>\n",
+              "      <td>[wind, trees, fence, park, fencing, shed, stor...</td>\n",
+              "      <td>[wind damage at O'Donnell Park                ...</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wind</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>30</td>\n",
+              "      <td>45</td>\n",
+              "      <td>30_street_light_damaged_accident</td>\n",
+              "      <td>[street, light, damaged, accident, vehicle, kn...</td>\n",
+              "      <td>[street light damaged                         ...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>39</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>31</td>\n",
+              "      <td>44</td>\n",
+              "      <td>31_gym_floor_leak_roof</td>\n",
+              "      <td>[gym, floor, leak, roof, k9, injured, training...</td>\n",
+              "      <td>[water damage to gym floor                    ...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>23</td>\n",
+              "      <td>8</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>32</td>\n",
+              "      <td>44</td>\n",
+              "      <td>32_sign_vehicle_signal_traffic</td>\n",
+              "      <td>[sign, vehicle, signal, traffic, struck, hit, ...</td>\n",
+              "      <td>[vehicle struck and damaged traffic signal    ...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>33</td>\n",
+              "      <td>43</td>\n",
+              "      <td>33_hs_lightning_hhs_lec</td>\n",
+              "      <td>[hs, lightning, hhs, lec, damage, at, lighning...</td>\n",
+              "      <td>[lightning damage at HS                       ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>43</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>34</td>\n",
+              "      <td>41</td>\n",
+              "      <td>34_school_elementary_water_high</td>\n",
+              "      <td>[school, elementary, water, high, damage, elem...</td>\n",
+              "      <td>[water damage at school                       ...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>30</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>35</td>\n",
+              "      <td>39</td>\n",
+              "      <td>35_storm_bldgs_locations_multiple</td>\n",
+              "      <td>[storm, bldgs, locations, multiple, damage, fa...</td>\n",
+              "      <td>[storm damage                                 ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>17</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wind</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>36</td>\n",
+              "      <td>37</td>\n",
+              "      <td>36_hs_water_tremper_reuther</td>\n",
+              "      <td>[hs, water, tremper, reuther, hcc, mats, damag...</td>\n",
+              "      <td>[water damage at HS                           ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>25</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>37</td>\n",
+              "      <td>37</td>\n",
+              "      <td>37_radio_antenna_lightning_radios</td>\n",
+              "      <td>[radio, antenna, lightning, radios, to, freque...</td>\n",
+              "      <td>[lightning damage to radio                    ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>36</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>38</td>\n",
+              "      <td>36</td>\n",
+              "      <td>38_water_carpet_equipment_computers</td>\n",
+              "      <td>[water, carpet, equipment, computers, to, copi...</td>\n",
+              "      <td>[water damage to equipment                    ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>24</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>39</td>\n",
+              "      <td>35</td>\n",
+              "      <td>39_toki_courthouse_ms_damagecourthouse</td>\n",
+              "      <td>[toki, courthouse, ms, damagecourthouse, glass...</td>\n",
+              "      <td>[glass vandalism at Toki MS                   ...</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>40</td>\n",
+              "      <td>34</td>\n",
+              "      <td>40_center_water_bldg_main</td>\n",
+              "      <td>[center, water, bldg, main, sinkhole, health, ...</td>\n",
+              "      <td>[water damage at Comm Center                  ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>13</td>\n",
+              "      <td>19</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>41</td>\n",
+              "      <td>34</td>\n",
+              "      <td>41_ms_es_lightning_damage</td>\n",
+              "      <td>[ms, es, lightning, damage, micrologix, mms, w...</td>\n",
+              "      <td>[lightning damage at MS                       ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>33</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>42</td>\n",
+              "      <td>34</td>\n",
+              "      <td>42_street_pole_light_streetlight</td>\n",
+              "      <td>[street, pole, light, streetlight, damaged, du...</td>\n",
+              "      <td>[street light pole damaged                    ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>33</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>43</td>\n",
+              "      <td>33</td>\n",
+              "      <td>43_well_10_lightning_wells</td>\n",
+              "      <td>[well, 10, lightning, wells, house, monitoring...</td>\n",
+              "      <td>[lightning damage to well 5                   ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>44</td>\n",
+              "      <td>31</td>\n",
+              "      <td>44_hydrant_vehicle_struck_hit</td>\n",
+              "      <td>[hydrant, vehicle, struck, hit, over, by, car,...</td>\n",
+              "      <td>[hydrant damaged                              ...</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>28</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Vehicle</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>45</td>\n",
+              "      <td>31</td>\n",
+              "      <td>45_plant_reservoir_tower_lightning</td>\n",
+              "      <td>[plant, reservoir, tower, lightning, wastewate...</td>\n",
+              "      <td>[lightning damage to sewer plant              ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>46</td>\n",
+              "      <td>31</td>\n",
+              "      <td>46_falk_follette_la_glass</td>\n",
+              "      <td>[falk, follette, la, glass, es, vandalism, sta...</td>\n",
+              "      <td>[glass vandalism at Falk ES                   ...</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Vandalism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>47</td>\n",
+              "      <td>30</td>\n",
+              "      <td>47_radio_dropped_radios_lost</td>\n",
+              "      <td>[radio, dropped, radios, lost, portable, when,...</td>\n",
+              "      <td>[radio damaged                                ...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Misc</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>48</td>\n",
+              "      <td>30</td>\n",
+              "      <td>48_lift_station_elevator_lightning</td>\n",
+              "      <td>[lift, station, elevator, lightning, stations,...</td>\n",
+              "      <td>[lightning damage at lift station             ...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Lightning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>49</td>\n",
+              "      <td>30</td>\n",
+              "      <td>49_buildings_building_water_basement</td>\n",
+              "      <td>[buildings, building, water, basement, to, abo...</td>\n",
+              "      <td>[water damage to building                     ...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>16</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>WaterW</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-784a6c50-513a-4fa9-8114-7b5694a51c82')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-784a6c50-513a-4fa9-8114-7b5694a51c82 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-784a6c50-513a-4fa9-8114-7b5694a51c82');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-db0aa293-878e-409a-aaa3-c2eec1daf413\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db0aa293-878e-409a-aaa3-c2eec1daf413')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-db0aa293-878e-409a-aaa3-c2eec1daf413 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ],
+      "source": [
+        "tb[\"mapping\"] = tb.values.argmax(axis=1)\n",
+        "tb[\"label\"] = [labels[i] for i in tb[\"mapping\"]]\n",
+        "mapping = {i: tb.loc[i, \"mapping\"] for i in tb.index}\n",
+        "topic_model.get_topic_info().merge(tb, on=\"Topic\")"
+      ]
     },
-    "id": "cU3f6uVc9gjU",
-    "outputId": "be8e6700-743e-460d-b01c-7ab8ecb929b8"
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss. If Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1039\n",
-      "  Batch size = 8\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YkF2EY-hEVHK"
+      },
+      "source": [
+        "Now, let's apply this model to the validation set. First, we assign each sample to a cluster, based on the clustering model."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
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-       "      [130/130 00:01]\n",
-       "    </div>\n",
-       "    "
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xeE03QWFEVHK",
+        "outputId": "a86f0d7f-c056-4b44-f37b-80d2828200f1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/scipy/sparse/_index.py:146: SparseEfficiencyWarning:\n",
+            "\n",
+            "Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
+            "\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "topics_test, probs_test = topic_model.transform(df_valid[\"Description\"])"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Simularity, refined\n",
-      "accuracy score = 76.6%,  log loss = 1.172,  Brier loss = 0.403\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.90      0.88      0.89       310\n",
-      "        Fire       0.62      0.74      0.67        46\n",
-      "   Lightning       0.80      0.94      0.87       123\n",
-      "        Wind       0.97      0.82      0.89       107\n",
-      "        Hail       0.90      1.00      0.95        18\n",
-      "     Vehicle       0.89      0.75      0.82       227\n",
-      "     WaterNW       0.46      0.93      0.61        67\n",
-      "      WaterW       0.11      0.18      0.14        38\n",
-      "        Misc       0.80      0.27      0.41       103\n",
-      "\n",
-      "    accuracy                           0.77      1039\n",
-      "   macro avg       0.72      0.72      0.69      1039\n",
-      "weighted avg       0.81      0.77      0.77      1039\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gKz-IvZEEVHL"
+      },
+      "source": [
+        "Then, we apply the mapping from topics to labels, which we have defined above based on the training set. The table below shows for each topic the frequency by label, and the mapping."
+      ]
     },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly",
-        "toImageButtonOptions": {
-         "filename": "cm_peril_sim_b",
-         "format": "svg"
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "WRLhMAZhEVHL",
+        "outputId": "9c2430ae-c65a-48b5-bf67-cf3d409754e7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "labels    0   1   2   3   4   5   6  7   8  mapping\n",
+              "Topic                                              \n",
+              "-1       21   6  28  28   0  23  10  8  17        2\n",
+              " 0       49   0   0   0   0   0   0  0   0        0\n",
+              " 1        1   0   0   0   0   0   0  0   0        0\n",
+              " 2        5  37   0   0   0   3   3  1   2        1\n",
+              " 3        0   1  42   1   0   1   0  0   3        2\n",
+              " 4        0   0   5   0   0   1   0  0  30        8\n",
+              " 5        3   1   1   0   0   6  21  9  10        7\n",
+              " 6       18   0   1   0   0   1   0  0   8        0\n",
+              " 7       59   0   0   0   0   1   0  0   0        0\n",
+              " 8        0   0  11   0   0   0   0  0   0        2\n",
+              " 9        1   0   0   3   0   9   0  0   1        5\n",
+              " 10      19   0   0   0   0   0   0  0   1        0\n",
+              " 11       1   0   0   0   0  13   0  0   0        5\n",
+              " 12       0   0   0  27   0   0   0  0   3        3\n",
+              " 13       0   0   0   0   0  13   0  0   0        5\n",
+              " 14       0   0   0  29   0   0   0  1   0        3\n",
+              " 15     106   0   0   1   0   4   2  0   1        0\n",
+              " 16       1   0   0   0   0  17   0  0   2        5\n",
+              " 17       0   0   0   0   0   1   7  6   0        7\n",
+              " 18       0   0   0   0   0  13   0  0   0        5\n",
+              " 19      16   0   0   0   0   2   0  0   4        0\n",
+              " 20       0   0   0   2  18   0   0  0   0        4\n",
+              " 21       0   0   0   1   0   5   0  0   1        5\n",
+              " 22       0   0   0   1   0  46   0  0   1        5\n",
+              " 23       4   0   0   0   0   0   0  0   2        0\n",
+              " 24       0   0   3   1   0   0   0  0   0        2\n",
+              " 25       0   0   0   0   0   0   0  0   5        0\n",
+              " 26       0   0   8   1   0   0   0  0   0        2\n",
+              " 27       1   0   0   0   0   2   0  0   1        8\n",
+              " 28       3   0   1   2   0  15   0  0   1        5\n",
+              " 29       0   0   0   6   0   0   0  0   0        3\n",
+              " 30       1   0   0   0   0  19   0  0   0        5\n",
+              " 31       0   0   0   0   0   1   7  4   2        7\n",
+              " 32       0   0   0   0   0  21   0  0   2        5\n",
+              " 33       0   0   1   0   0   0   0  0   0        2\n",
+              " 34       0   1   0   0   0   0   3  1   0        7\n",
+              " 35       0   0   1   4   0   0   0  2   0        3\n",
+              " 36       0   0   0   0   0   0   1  1   0        7\n",
+              " 37       0   0  12   0   0   0   0  0   0        2\n",
+              " 38       0   0   0   0   0   0   5  0   0        7\n",
+              " 39       1   0   0   0   0   0   1  0   0        0\n",
+              " 40       0   0   0   0   0   0   2  1   0        7\n",
+              " 42       0   0   0   0   0   2   0  0   1        5\n",
+              " 44       0   0   0   0   0   7   0  0   0        5\n",
+              " 45       0   0   4   0   0   0   1  0   1        2\n",
+              " 47       0   0   0   0   0   1   0  0   2        8\n",
+              " 48       0   0   5   0   0   0   0  0   0        2\n",
+              " 49       0   0   0   0   0   0   4  4   2        7"
+            ],
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+              "<style scoped>\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>labels</th>\n",
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+              "      <td>42</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30</td>\n",
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+              "    <tr>\n",
+              "      <th>6</th>\n",
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+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>59</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>16</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>18</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>15</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c8ba86b-5f3e-446a-8561-661c9c3989e3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7c8ba86b-5f3e-446a-8561-661c9c3989e3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7c8ba86b-5f3e-446a-8561-661c9c3989e3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-19c20369-843e-41ab-992b-57fa4a8e34ce\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19c20369-843e-41ab-992b-57fa4a8e34ce')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-19c20369-843e-41ab-992b-57fa4a8e34ce button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
         }
-       },
-       "data": [
+      ],
+      "source": [
+        "df_valid[\"Topic\"] = topics_test\n",
+        "df_valid[\"prob\"] = probs_test\n",
+        "df_valid[\"pred\"] = [mapping[t] for t in topics_test]\n",
+        "df_valid.to_excel(\"results/peril_topics.xlsx\")\n",
+        "tb_valid = pd.pivot_table(df_valid, index=[\"Topic\"], columns=[\"labels\"], aggfunc='count', fill_value=0)[\"Description\"]\n",
+        "tb_valid[\"mapping\"] = [mapping[t] for t in tb_valid.index]\n",
+        "tb_valid"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m7epfnhsEVHL"
+      },
+      "source": [
+        "This classifier achieves an accuracy score of ca. 70%, compared to 30% obtained with the dummy classifier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "ZF9x0l1xEVHL",
+        "outputId": "71a91dab-9ae3-4db3-df72-c441dc63007d"
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+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Topic modeling by clustering\n",
+            "accuracy score = 71.9%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.89      0.88      0.88       310\n",
+            "        Fire       0.73      0.80      0.76        46\n",
+            "   Lightning       0.48      0.93      0.63       123\n",
+            "        Wind       0.90      0.62      0.73       107\n",
+            "        Hail       0.90      1.00      0.95        18\n",
+            "     Vehicle       0.88      0.79      0.84       227\n",
+            "     WaterNW       0.00      0.00      0.00        67\n",
+            "      WaterW       0.25      0.68      0.37        38\n",
+            "        Misc       0.77      0.32      0.45       103\n",
+            "\n",
+            "    accuracy                           0.72      1039\n",
+            "   macro avg       0.64      0.67      0.62      1039\n",
+            "weighted avg       0.74      0.72      0.71      1039\n",
+            "\n"
           ]
-         ]
+        },
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+      "source": [
+        "_ = evaluate_classifier(df_valid[\"labels\"], df_valid[\"pred\"], None, labels, \"Topic modeling by clustering\", \"cm_peril_topic_a\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
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+      "source": [
+        "BERTopic provides the function `find_topics` which returns a list of IDs and similarity scores of topics that best match a given search term.\n",
+        "\n",
+        "This is useful to validate the mapping. Let's use the search term \"Fire\" and retrieve the three most similar topics.\n",
+        "For each of these topics, we print the similarity score and the label it was mapped to. We also show the word scores for each topic."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "colab": {
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+        "id": "8lXgN3eBEVHL",
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+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "topic  2: similarity score 63.5%, mapped to peril 1 (Fire)\n",
+            "topic 13: similarity score 39.1%, mapped to peril 5 (Vehicle)\n",
+            "topic  8: similarity score 35.9%, mapped to peril 2 (Lightning)\n"
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+              "\n",
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+              "    x.observe(outputEl, {childList: true});\n",
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+              "\n",
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+              "</body>\n",
+              "</html>"
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-         "text": "Simularity, refined"
-        },
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-        "xaxis": {
-         "anchor": "y",
-         "constrain": "domain",
-         "domain": [
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-         "title": {
-          "text": "<b>predicted class</b>"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "autorange": "reversed",
-         "constrain": "domain",
-         "domain": [
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-         "title": {
-          "text": "<b>actual class</b>"
-         }
+          "metadata": {}
         }
-       }
+      ],
+      "source": [
+        "similar_topics, similarity = topic_model.find_topics(\"Fire\", top_n=3)\n",
+        "for t, s in zip(similar_topics, similarity):\n",
+        "    print(f\"topic {t:2d}: similarity score {s:.1%}, mapped to peril {mapping[t]:d} ({labels[mapping[t]]})\")\n",
+        "topic_model.visualize_barchart(similar_topics)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zP9N-QnoEVHM"
       },
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+      "source": [
+        "As expected, the topics which have been mapped to \"Fire\" appear first in the list, with similarity scores of more than 80%.\n",
+        "\n",
+        "The first topic that was not mapped to \"Fire\" has a similarity score of less than 70%. It was mapped to the label \"Vehicle\".\n",
+        "Indeed: Although the word \"Fire\" ranks second in the word score, this is in combination with hydrant. This is about vehicles hitting fire hydrants."
       ]
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-    "predictions = trainer.predict(ds[\"test\"])\n",
-    "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), labels, \"Simularity, refined\", \"cm_peril_sim_b\")"
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-    "The accuracy score has improved by about 2 percentage points.\n",
-    "\n",
-    "Compared to the results obtained by zero-shot classification, we observe that the confusion between “Vandalism” and “Vehicle” has strongly reduced. This might be at least partially due to the fact that we have used different candidate expressions. \n",
-    "\n",
-    "For a fair comparison, you might want to go back and re-run the zero-shot classification using the new candidate expressions. However, you will have noticed that the sentence similarity approach is much faster to execute. The computational effort for both approaches is dominated by running the respective transformer model. For the zero-shot classification, the model is run behind the scenes for each combination of sample and candidate expression, so that the effort scales with the number of samples times the number of candidate expressions. In contrast, as we have seen above, the similarity approach runs the transformer model once for each input sample and once for each candidate expression, so that the effort scales with the number of samples plus the number of candidate expressions. This allows experimenting with different candidate expressions."
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-    "<a id='topic_modeling'></a>\n",
-    "<a name='topic_modeling'></a>\n",
-    "## 5.&nbsp;Unsupervised Topic Modeling by Clustering of Document Embeddings\n",
-    "\n",
-    "In the previous section we have seen the strength of zero-shot classification:\n",
-    "No prior training of the language model is required to produce a classification of reasonable quality.\n",
-    "However, it may be difficult to provide suitable candidate expressions.\n",
-    "\n",
-    "In this section, we present an alternative approach.\n",
-    "\n",
-    "The idea is to encode all text samples, to create clusters of \"similar\" documents and to extract meaningful\n",
-    "verbal representations of the clusters.\n",
-    "\n",
-    "Several packages are available to perform this task, e.g.,\n",
-    "[BERTopic](https://maartengr.github.io/BERTopic/index.html),\n",
-    "[Top2Vec](https://github.com/ddangelov/Top2Vec) and\n",
-    "[chat-intents](https://github.com/dborrelli/chat-intents).\n",
-    "These packages use similar concepts but provide different APIs, hyper-parameters, diagnostics tools, etc.\n",
-    "\n",
-    "Here, we use BERTopic.\n",
-    "\n",
-    "The algorithm consists of the following steps:\n",
-    "\n",
-    "1. **Embed documents:**\n",
-    "    * Encode each text sample (document) into a vector - the embedding.\n",
-    "    This can be based on a BERT model or any other document embedding technique.\n",
-    "    By default, BERTopic uses `all-MiniLM-L6-v2`, which is trained in English.\n",
-    "    In the multi-lingual case it uses `paraphrase-multilingual-MiniLM-L12-v2`.<br><br>\n",
-    "\n",
-    "2.  **Cluster documents:**\n",
-    "    * Reduce the dimensionality of the embeddings.\n",
-    "    This is required because the documents embeddings are high-dimensional,\n",
-    "    and typically, clustering algorithms have difficulty clustering data in high dimensional space.\n",
-    "    By default, BERTopic uses\n",
-    "    [UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction)](https://umap-learn.readthedocs.io/en/latest/)\n",
-    "    as it preserves both the local and global structure of embeddings quite well.<br>\n",
-    "\n",
-    "    * Create clusters of semantically similar documents. \n",
-    "    By default, BERTopic uses\n",
-    "    [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/)\n",
-    "    as it allows to identify outliers.<br><br>\n",
-    "\n",
-    "3. **Create topic representation:**\n",
-    "    * Extract and reduce topics with c-TF-IDF.\n",
-    "    This is a modification of TF-IDF, which applies TD-IDF to the concatenation of all documents within each document cluster,\n",
-    "    to obtain importance scores for the words within the cluster.\n",
-    "    \n",
-    "    * Improve coherence and diversity of words with Maximal Marginal Relevance, to find the most coherent words without having too much overlap between the words themselves. This results in the removal of words that do not contribute to a topic.\n",
-    "    \n",
-    "Let's apply the algorithm to our dataset and examine the results."
-   ]
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-    "<a id='topic_modeling_basic'></a>\n",
-    "<a name='topic_modeling_basic'></a>\n",
-    "### 5.1. Basic topic modeling\n",
-    "\n",
-    "Normally, BERTopic instantiates UMAP and HDBSCAN automatically.\n",
-    "Here, we instantiate them manually and pass them to BERTopic, for the following reasons:\n",
-    "\n",
-    "* For UMAP, we specify `random_state=42`, to improve reproducibility across runs. Please note that reproducibility across platforms is not guaranteed.\n",
-    "\n",
-    "* For HDBSCAN, we specify `min_cluster_size=30` and `min_samples=1` in order to control the number of clusters and the percentage of samples classified as outliers.\n",
-    "\n",
-    "Otherwise, we use the default parameters used by BERTopic. "
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     },
-    "id": "ILamlptxEVHI",
-    "outputId": "30f4a11e-ae78-42c8-bbb2-93c2b0f9988e"
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/config.json\n",
-      "Model config BertConfig {\n",
-      "  \"_name_or_path\": \"/home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/\",\n",
-      "  \"architectures\": [\n",
-      "    \"BertModel\"\n",
-      "  ],\n",
-      "  \"attention_probs_dropout_prob\": 0.1,\n",
-      "  \"classifier_dropout\": null,\n",
-      "  \"gradient_checkpointing\": false,\n",
-      "  \"hidden_act\": \"gelu\",\n",
-      "  \"hidden_dropout_prob\": 0.1,\n",
-      "  \"hidden_size\": 384,\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"intermediate_size\": 1536,\n",
-      "  \"layer_norm_eps\": 1e-12,\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"bert\",\n",
-      "  \"num_attention_heads\": 12,\n",
-      "  \"num_hidden_layers\": 6,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"position_embedding_type\": \"absolute\",\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"type_vocab_size\": 2,\n",
-      "  \"use_cache\": true,\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n",
-      "loading weights file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/pytorch_model.bin\n",
-      "All model checkpoint weights were used when initializing BertModel.\n",
-      "\n",
-      "All the weights of BertModel were initialized from the model checkpoint at /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertModel for predictions without further training.\n",
-      "Didn't find file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/added_tokens.json. We won't load it.\n",
-      "loading file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/vocab.txt\n",
-      "loading file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/tokenizer.json\n",
-      "loading file None\n",
-      "loading file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/special_tokens_map.json\n",
-      "loading file /home/ubuntu/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/tokenizer_config.json\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "67ZBWOL-EVHM"
+      },
+      "source": [
+        "<a id='topic_modelling_refinement'></a>\n",
+        "<a name='topic_modelling_refinement'></a>\n",
+        "### 5.2. Refinement\n",
+        "\n",
+        "Above, a relatively large number of samples was classified as outlier. All outliers were mapped to a single class, but this mapping is questionable, because we have seen that outlier samples belong to different classes.\n",
+        "\n",
+        "To mitigate this issue, we could label the outlier samples manually. However, this is quite tedious.\n",
+        "\n",
+        "Alternatively, we can train a classifier to the labels obtained from the unsupervised approach. To avoid label noise, we suppress outliers.\n",
+        "\n",
+        "First, we create the training dataset. We replace the true labels by the labels obtained from the clustering approach."
+      ]
     },
     {
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-       "      <th>13</th>\n",
-       "      <td>12</td>\n",
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-       "    </tr>\n",
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-       "      <th>14</th>\n",
-       "      <td>13</td>\n",
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-       "      <th>16</th>\n",
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-       "      <td>28_vandalism_damage_odonnell_bandshell</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
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-       "      <td>29_street_light_run_damaged</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>30</td>\n",
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-       "      <td>30_airport_lightning_lights_runway</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>31</td>\n",
-       "      <td>50</td>\n",
-       "      <td>31_center_water_main_dept</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>32</td>\n",
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-       "      <td>32_phone_system_phones_telephone</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
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-       "    </tr>\n",
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-       "      <th>35</th>\n",
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-       "      <th>36</th>\n",
-       "      <td>35</td>\n",
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-       "      <th>37</th>\n",
-       "      <td>36</td>\n",
-       "      <td>45</td>\n",
-       "      <td>36_sign_vehicle_signal_traffic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>37</td>\n",
-       "      <td>44</td>\n",
-       "      <td>37_overhead_door_damaged_loader</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>38</td>\n",
-       "      <td>44</td>\n",
-       "      <td>38_wind_fence_park_trees</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>39</td>\n",
-       "      <td>44</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>40</td>\n",
-       "      <td>42</td>\n",
-       "      <td>40_storm_multiple_sites_locations</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>41</td>\n",
-       "      <td>42</td>\n",
-       "      <td>41_hs_lightning_hhs_damage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>42</td>\n",
-       "      <td>41</td>\n",
-       "      <td>42_park_washington_vandalism_jacobus</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>43</td>\n",
-       "      <td>39</td>\n",
-       "      <td>43_hs_water_tremper_pw</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>44</td>\n",
-       "      <td>38</td>\n",
-       "      <td>44_laptop_mckinley_damaged_computer</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>45</td>\n",
-       "      <td>38</td>\n",
-       "      <td>45_water_equipment_carpet_to</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>46</td>\n",
-       "      <td>38</td>\n",
-       "      <td>46_radio_antenna_lightning_radios</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>47</td>\n",
-       "      <td>33</td>\n",
-       "      <td>47_street_pole_light_streetlight</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>48</td>\n",
-       "      <td>33</td>\n",
-       "      <td>48_gym_floor_injured_k9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>49</td>\n",
-       "      <td>32</td>\n",
-       "      <td>49_ms_es_lightning_damage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>50</td>\n",
-       "      <td>32</td>\n",
-       "      <td>50_hydrant_vehicle_struck_hit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>51</td>\n",
-       "      <td>31</td>\n",
-       "      <td>51_radio_lost_dropped_portable</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>52</td>\n",
-       "      <td>30</td>\n",
-       "      <td>52_roof_collapsed_collapse_gutter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>53</td>\n",
-       "      <td>30</td>\n",
-       "      <td>53_buildings_building_water_basement</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>54</td>\n",
-       "      <td>30</td>\n",
-       "      <td>54_tower_lightning_north_internet</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "4ef1df4f11a6406c8ffe11d3be4af259",
+            "6454374431404c1ea46899c35c05b063",
+            "73e34a464d6142d4bee73412fbc91e93",
+            "c7eed26541f14cb8a92a29b99b569d34",
+            "9d4bac79b75f4d1b8d7965eb5d5c5bc3",
+            "41b17d092d6c41f7a4d37d4b454c1825",
+            "51acf591d4f2434ba35a4665247c815f",
+            "70adad47fbe848ebb9497f683127adf7",
+            "a46e3ffffed1435087c74d615ceebe9d",
+            "db7ee224b00b4d2eb787d24561e83ce4",
+            "4d51eb40e0a1457e8411536dfe0ddb0a"
+          ]
+        },
+        "id": "rM4tmo_WEVHM",
+        "outputId": "7b89b04b-7792-4a5a-ff89-b9bd142c85af"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/4329 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ef1df4f11a6406c8ffe11d3be4af259"
+            }
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "    Topic  Count                                    Name\n",
-       "0      -1   1077            -1_vandalism_at_lightning_es\n",
-       "1       0    208               0_glass_vandalism_west_es\n",
-       "2       1    201           1_fire_smoke_damage_equipment\n",
-       "3       2    182                2_froze_pipe_sewer_pipes\n",
-       "4       3    176        3_power_surge_generator_spoilage\n",
-       "5       4    161                 4_theft_of_stolen_break\n",
-       "6       5    141              5_graffiti_on_kennedy_hoyt\n",
-       "7       6    128            6_lightning_damage_scale_dpw\n",
-       "8       7    112           7_park_vandalism_pavilion_dmg\n",
-       "9       8    109            8_broken_door_glass_breakage\n",
-       "10      9    109               9_lightning_dept_hall_hwy\n",
-       "11     10    104      10_signal_traffic_damaged_paradise\n",
-       "12     11     84              11_wind_damage_course_golf\n",
-       "13     12     83             12_hydrant_fire_hit_damaged\n",
-       "14     13     79               13_llm_glass_mendota_hawk\n",
-       "15     14     77                 14_garage_door_hwy_shop\n",
-       "16     15     76      15_computer_lightning_to_equipment\n",
-       "17     16     73           16_fence_gate_vehicle_damaged\n",
-       "18     17     73            17_building_truck_vehicle_by\n",
-       "19     18     72         18_hail_buildings_roof_multiple\n",
-       "20     19     68            19_water_damage_goodman_pool\n",
-       "21     20     68       20_shelter_eastman_farlin_seymour\n",
-       "22     21     67               21_pole_vehicle_hit_light\n",
-       "23     22     64         22_pole_light_damaged_lightpole\n",
-       "24     23     62        23_window_broken_windows_screens\n",
-       "25     24     61                 24_laptop_theft_from_of\n",
-       "26     25     57              25_roof_wind_shingles_blew\n",
-       "27     26     56                   26_water_es_ms_damage\n",
-       "29     27     55    27_dmg_humboldt_lafollette_vandalism\n",
-       "28     28     55  28_vandalism_damage_odonnell_bandshell\n",
-       "30     29     54             29_street_light_run_damaged\n",
-       "31     30     51      30_airport_lightning_lights_runway\n",
-       "32     31     50               31_center_water_main_dept\n",
-       "33     32     49        32_phone_system_phones_telephone\n",
-       "34     33     49      33_vandalism_lemonweir_lock_gazebo\n",
-       "35     34     47            34_well_meter_flow_lightning\n",
-       "36     35     47         35_school_water_elementary_high\n",
-       "37     36     45          36_sign_vehicle_signal_traffic\n",
-       "38     37     44         37_overhead_door_damaged_loader\n",
-       "39     38     44                38_wind_fence_park_trees\n",
-       "40     39     44       39_equipment_playground_slide_gps\n",
-       "41     40     42       40_storm_multiple_sites_locations\n",
-       "42     41     42              41_hs_lightning_hhs_damage\n",
-       "43     42     41    42_park_washington_vandalism_jacobus\n",
-       "44     43     39                  43_hs_water_tremper_pw\n",
-       "45     44     38     44_laptop_mckinley_damaged_computer\n",
-       "46     45     38            45_water_equipment_carpet_to\n",
-       "47     46     38       46_radio_antenna_lightning_radios\n",
-       "48     47     33        47_street_pole_light_streetlight\n",
-       "49     48     33                 48_gym_floor_injured_k9\n",
-       "50     49     32               49_ms_es_lightning_damage\n",
-       "51     50     32           50_hydrant_vehicle_struck_hit\n",
-       "52     51     31          51_radio_lost_dropped_portable\n",
-       "53     52     30       52_roof_collapsed_collapse_gutter\n",
-       "54     53     30    53_buildings_building_water_basement\n",
-       "55     54     30       54_tower_lightning_north_internet"
+      "source": [
+        "df_train_unsupervised = df_train[df_train[\"Topic\"]>=0].copy()\n",
+        "df_train_unsupervised[\"labels\"] = [mapping[t] for t in df_train_unsupervised[\"Topic\"]]\n",
+        "ds_train_unsupervised = Dataset.from_pandas(df_train_unsupervised)\n",
+        "ds_train_unsupervised = ds_train_unsupervised.map(tokenize, batched=True)"
       ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "import random\n",
-    "random.seed(42)\n",
-    "umap_model = UMAP(n_neighbors=15, n_components=10, metric='cosine', low_memory=False, random_state=42)\n",
-    "hdbscan_model = HDBSCAN(min_cluster_size=30, metric='euclidean', prediction_data=True, min_samples=1)\n",
-    "topic_model = BERTopic(umap_model=umap_model, hdbscan_model=hdbscan_model)\n",
-    "topics, probs = topic_model.fit_transform(df_train[\"Description\"])\n",
-    "topic_model.get_topic_info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LcmIftTnEVHJ"
-   },
-   "source": [
-    "The first output of `fit_transform` holds the topic ID for each sample. The second output is the probability of the sample belonging to that topic.\n",
-    "\n",
-    "In our case, we have obtained ca. 50 clusters. due to randomness of UMAP, the results may differ between runs. Unfortunately, we have not found a way to fix this.\n",
-    "\n",
-    "The cluster with ID `-1` contains all samples which are considered \"noise\" because they were not attributed to any cluster.\n",
-    "\n",
-    "The function `get_topic_info` returns the topic ID, the sample count, and a concatenation of the words representing the cluster.\n",
-    "\n",
-    "To get a visual impression of the clusters, BERTopic provides the function `visualize_topics` which embeds the c-TF-IDF representation of the topics in 2D using UMAP and then visualizes the two dimensions using plotly in an interactive view."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 667
     },
-    "id": "q5WOiwszEVHJ",
-    "outputId": "81c6a4e0-317c-4abf-fd2c-daf91952a04e"
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 172
+        },
+        "id": "4Mdi7Y6aEVHN",
+        "outputId": "abfd0c29-2306-4c80-99b7-ded7518d9774"
+      },
+      "outputs": [
         {
-         "customdata": [
-          [
-           1,
-           "fire | smoke | damage | equipment | station",
-           201
-          ],
-          [
-           2,
-           "froze | pipe | sewer | pipes | library",
-           182
-          ],
-          [
-           3,
-           "power | surge | generator | spoilage | food",
-           176
-          ],
-          [
-           4,
-           "theft | of | stolen | break | from",
-           161
-          ],
-          [
-           5,
-           "graffiti | on | kennedy | hoyt | llm",
-           141
-          ],
-          [
-           6,
-           "lightning | damage | scale | dpw | museum",
-           128
-          ],
-          [
-           7,
-           "park | vandalism | pavilion | dmg | madison",
-           112
-          ],
-          [
-           8,
-           "broken | door | glass | breakage | entrance",
-           109
-          ],
-          [
-           9,
-           "lightning | dept | hall | hwy | st",
-           109
-          ],
-          [
-           10,
-           "signal | traffic | damaged | paradise | box",
-           104
-          ],
-          [
-           11,
-           "wind | damage | course | golf | tower",
-           84
-          ],
-          [
-           12,
-           "hydrant | fire | hit | damaged | run",
-           83
-          ],
-          [
-           13,
-           "llm | glass | mendota | hawk | black",
-           79
-          ],
-          [
-           14,
-           "garage | door | hwy | shop | truck",
-           77
-          ],
-          [
-           15,
-           "computer | lightning | to | equipment | system",
-           76
-          ],
-          [
-           16,
-           "fence | gate | vehicle | damaged | by",
-           73
-          ],
-          [
-           17,
-           "building | truck | vehicle | by | wall",
-           73
-          ],
-          [
-           18,
-           "hail | buildings | roof | multiple | windhail",
-           72
-          ],
-          [
-           19,
-           "water | damage | goodman | pool | toki",
-           68
-          ],
-          [
-           20,
-           "shelter | eastman | farlin | seymour | vandalism",
-           68
-          ],
-          [
-           21,
-           "pole | vehicle | hit | light | struck",
-           67
-          ],
-          [
-           22,
-           "pole | light | damaged | lightpole | rawson",
-           64
-          ],
-          [
-           23,
-           "window | broken | windows | screens | screen",
-           62
-          ],
-          [
-           24,
-           "laptop | theft | from | of | stolen",
-           61
-          ],
-          [
-           25,
-           "roof | wind | shingles | blew | off",
-           57
-          ],
-          [
-           26,
-           "water | es | ms | damage | west",
-           56
-          ],
-          [
-           27,
-           "dmg | humboldt | lafollette | vandalism | park",
-           55
-          ],
-          [
-           28,
-           "vandalism | damage | odonnell | bandshell | clubhouse",
-           55
-          ],
-          [
-           29,
-           "street | light | run | damaged | hit",
-           54
-          ],
-          [
-           30,
-           "airport | lightning | lights | runway | gates",
-           51
-          ],
-          [
-           31,
-           "center | water | main | dept | resch",
-           50
-          ],
-          [
-           32,
-           "phone | system | phones | telephone | lightning",
-           49
-          ],
-          [
-           33,
-           "vandalism | lemonweir | lock | gazebo | window",
-           49
-          ],
-          [
-           34,
-           "well | meter | flow | lightning | 10",
-           47
-          ],
-          [
-           35,
-           "school | water | elementary | high | lincoln",
-           47
-          ],
-          [
-           36,
-           "sign | vehicle | signal | traffic | struck",
-           45
-          ],
-          [
-           37,
-           "overhead | door | damaged | loader | hangar",
-           44
-          ],
-          [
-           38,
-           "wind | fence | park | trees | fencing",
-           44
-          ],
-          [
-           39,
-           "equipment | playground | slide | gps | instrument",
-           44
-          ],
-          [
-           40,
-           "storm | multiple | sites | locations | bldgs",
-           42
-          ],
-          [
-           41,
-           "hs | lightning | hhs | damage | at",
-           42
-          ],
-          [
-           42,
-           "park | washington | vandalism | jacobus | wilson",
-           41
-          ],
-          [
-           43,
-           "hs | water | tremper | pw | reuther",
-           39
-          ],
-          [
-           44,
-           "laptop | mckinley | damaged | computer | wic",
-           38
-          ],
-          [
-           45,
-           "water | equipment | carpet | to | computers",
-           38
-          ],
-          [
-           46,
-           "radio | antenna | lightning | radios | to",
-           38
-          ],
-          [
-           47,
-           "street | pole | light | streetlight | damaged",
-           33
-          ],
-          [
-           48,
-           "gym | floor | injured | k9 | training",
-           33
-          ],
-          [
-           49,
-           "ms | es | lightning | damage | karcher",
-           32
-          ],
-          [
-           50,
-           "hydrant | vehicle | struck | hit | over",
-           32
-          ],
-          [
-           51,
-           "radio | lost | dropped | portable | radios",
-           31
-          ],
-          [
-           52,
-           "roof | collapsed | collapse | gutter | snow",
-           30
-          ],
-          [
-           53,
-           "buildings | building | water | basement | to",
-           30
-          ],
-          [
-           54,
-           "tower | lightning | north | internet | barnes",
-           30
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
           ]
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-         "hovertemplate": "<b>Topic %{customdata[0]}</b><br>Words: %{customdata[1]}<br>Size: %{customdata[2]}",
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+              "      \n",
+              "      <progress value='1084' max='1084' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1084/1084 00:55, Epoch 2/2]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>541</td>\n",
+              "      <td>0.377000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>1082</td>\n",
+              "      <td>0.090200</td>\n",
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+      "source": [
+        "model_name = \"distilbert-base-uncased\"\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
+        "\n",
+        "# train the model\n",
+        "batch_size = 8\n",
+        "logging_steps = len(ds_train_unsupervised) // batch_size\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=model_name+\"_peril_u_epochs\",\n",
+        "    num_train_epochs=2,\n",
+        "    per_device_train_batch_size=batch_size,\n",
+        "    per_device_eval_batch_size=batch_size,\n",
+        "    metric_for_best_model=\"f1\",\n",
+        "    logging_steps=logging_steps,\n",
+        "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
+        ")\n",
+        "trainer = Trainer(model=model, args=training_args,\n",
+        "                  compute_metrics=compute_metrics, train_dataset=ds_train_unsupervised,\n",
+        "                  eval_dataset=ds[\"test\"])\n",
+        "trainer.train();\n",
+        "trainer.save_model(model_name + \"_peril_u\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Rs65tNt_EVHN"
+      },
+      "source": [
+        "Then, we evaluate the classifier on the test set, by comparing the predicted to the true labels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "id": "HldJUXwtEVHN",
+        "outputId": "10ee308d-7c4a-4edd-9315-18c9c2dc4cda"
+      },
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-    "Insights can be gained from the relative c-TF-IDF scores between and within topics. Moreover, you can easily compare topic representations to each other. To visualize this hierarchy, simply call  the function `visualize_barchart`:"
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+            "Topic modeling by clustering, refined\n",
+            "accuracy score = 79.8%,  log loss = 1.514,  Brier loss = 0.383\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "   Vandalism       0.84      0.95      0.89       310\n",
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+            "     WaterNW       0.00      0.00      0.00        67\n",
+            "      WaterW       0.28      0.89      0.43        38\n",
+            "        Misc       0.76      0.38      0.51       103\n",
+            "\n",
+            "    accuracy                           0.80      1039\n",
+            "   macro avg       0.70      0.75      0.70      1039\n",
+            "weighted avg       0.78      0.80      0.78      1039\n",
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-        "title": {
-         "font": {
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-         "text": "<b>Topic Word Scores",
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+          "metadata": {}
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+      ],
+      "source": [
+        "predictions = trainer.predict(ds[\"test\"])\n",
+        "_ = evaluate_classifier(predictions.label_ids, None, softmax(predictions.predictions, axis=1), labels, \"Topic modeling by clustering, refined\", \"cm_peril_topic_b\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
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+        "<a id='conclusion'></a>\n",
+        "<a name='conclusion'></a>\n",
+        "## 6.&nbsp;Conclusions\n",
+        "\n",
+        "Congratulations!\n",
+        "\n",
+        "In this Part II of the tutorial, you have first applied the techniques you have learned in Part I to a dataset with shorter texts.\n",
+        "\n",
+        "Then you have learned how to use zero shot classification in a situation with no labels. The beauty of this approach is that it requires no training and produces a reasonable classification by a list of user-defined expressions.\n",
+        "\n",
+        "You have also seen that unsupervised classification can be achieved by similarity scoring between the input sequence and a list of user-defined expressions.\n",
+        "\n",
+        "Going one step further, you have seen an approach that creates clusters of similar documents and represents each cluster by typical words. This can be used as a starting point to create meaningful labels.\n",
+        "\n",
+        "If you have enjoyed this tutorial, feel free to apply any of the approaches - or improved versions, of course - to your own text data, to enrich your structured features available for supervised learning tasks."
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-    "Next, we want to assign labels to each cluster.\n",
-    "Compared to manually labeling thousands of samples, this task is much less burdensome!\n",
-    "\n",
-    "This is usually a manual task. Assignment of labels is guided by the topic information, the topic word scores and the hierarchical clustering.\n",
-    "\n",
-    "In our case, the actual labels are available, so that we can use this information to perform the labeling.\n",
-    "\n",
-    "Let's inspect how well the clusters matches the labels. The graph below shows one column per topic.\n",
-    "The shading indicates the distribution of labels within a given topic.\n",
-    "The presence of a single dark patch in a column indicates that almost all of the samples of the topic are associated with a single label."
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-       "            observer.disconnect();\n",
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-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train[\"Topic\"] = topics\n",
-    "tb = pd.pivot_table(df_train, index=[\"Topic\"], columns=[\"labels\"], aggfunc='count', fill_value=0)[\"Description\"]\n",
-    "fig = px.imshow(tb.divide(tb.sum(axis=1), axis=0).T, zmin=-0.05)\n",
-    "fig.update_layout(xaxis={\"dtick\": 1}, yaxis={\"dtick\": 1, \"range\":[0,8]}, coloraxis={\"colorscale\": \"Greys\"})\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cLoeG-9tEVHK"
-   },
-   "source": [
-    "Obviously, the topic `-1`, which represents the outliers, has a finite frequency for many classes.\n",
-    "Further, the classes 6 (`WaterNW`) and 7 (`WaterW`) seem to be difficult to tell apart from the clusters; this affects some of the topics.\n",
-    "For most other topics, the clustering aligns quite well with the labels.\n",
-    "\n",
-    "Overall, it appears reasonable to map each topic to the label with the highest frequency. Apart from the exceptions mentioned above, this aligns with a mapping that a human would define manually, in absence of the actual labels.\n",
-    "\n",
-    "Therefore, let's define the mapping from topics to labels by picking the label with the highest frequency. The table below shows the topic info, enriched with the label counts and the mapping. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "NZ6vPSS-EVHK",
-    "outputId": "9ba06643-0e5a-40ac-d096-7365def09ae8"
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Topic</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Name</th>\n",
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-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
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-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-1</td>\n",
-       "      <td>1077</td>\n",
-       "      <td>-1_vandalism_at_lightning_es</td>\n",
-       "      <td>534</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>36</td>\n",
-       "      <td>75</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
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-       "      <th>1</th>\n",
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-       "      <td>208</td>\n",
-       "      <td>0_glass_vandalism_west_es</td>\n",
-       "      <td>208</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>201</td>\n",
-       "      <td>1_fire_smoke_damage_equipment</td>\n",
-       "      <td>20</td>\n",
-       "      <td>148</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Fire</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>32</td>\n",
-       "      <td>96</td>\n",
-       "      <td>32</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>176</td>\n",
-       "      <td>3_power_surge_generator_spoilage</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>21</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>145</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Misc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4</td>\n",
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-       "      <td>4_theft_of_stolen_break</td>\n",
-       "      <td>128</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
-       "      <td>141</td>\n",
-       "      <td>5_graffiti_on_kennedy_hoyt</td>\n",
-       "      <td>140</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
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-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>6</td>\n",
-       "      <td>128</td>\n",
-       "      <td>6_lightning_damage_scale_dpw</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>127</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
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-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>7</td>\n",
-       "      <td>112</td>\n",
-       "      <td>7_park_vandalism_pavilion_dmg</td>\n",
-       "      <td>111</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
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-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>8</td>\n",
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-       "      <td>8_broken_door_glass_breakage</td>\n",
-       "      <td>98</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>9</td>\n",
-       "      <td>109</td>\n",
-       "      <td>9_lightning_dept_hall_hwy</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>108</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10</td>\n",
-       "      <td>104</td>\n",
-       "      <td>10_signal_traffic_damaged_paradise</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>101</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>11</td>\n",
-       "      <td>84</td>\n",
-       "      <td>11_wind_damage_course_golf</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>83</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Wind</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>12</td>\n",
-       "      <td>83</td>\n",
-       "      <td>12_hydrant_fire_hit_damaged</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>13</td>\n",
-       "      <td>79</td>\n",
-       "      <td>13_llm_glass_mendota_hawk</td>\n",
-       "      <td>71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>14</td>\n",
-       "      <td>77</td>\n",
-       "      <td>14_garage_door_hwy_shop</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>15</td>\n",
-       "      <td>76</td>\n",
-       "      <td>15_computer_lightning_to_equipment</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>71</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>16</td>\n",
-       "      <td>73</td>\n",
-       "      <td>16_fence_gate_vehicle_damaged</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>17</td>\n",
-       "      <td>73</td>\n",
-       "      <td>17_building_truck_vehicle_by</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>18</td>\n",
-       "      <td>72</td>\n",
-       "      <td>18_hail_buildings_roof_multiple</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Hail</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>19</td>\n",
-       "      <td>68</td>\n",
-       "      <td>19_water_damage_goodman_pool</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>23</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>20</td>\n",
-       "      <td>68</td>\n",
-       "      <td>20_shelter_eastman_farlin_seymour</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>21</td>\n",
-       "      <td>67</td>\n",
-       "      <td>21_pole_vehicle_hit_light</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>64</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>22</td>\n",
-       "      <td>64</td>\n",
-       "      <td>22_pole_light_damaged_lightpole</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>23</td>\n",
-       "      <td>62</td>\n",
-       "      <td>23_window_broken_windows_screens</td>\n",
-       "      <td>55</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>24</td>\n",
-       "      <td>61</td>\n",
-       "      <td>24_laptop_theft_from_of</td>\n",
-       "      <td>53</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>25</td>\n",
-       "      <td>57</td>\n",
-       "      <td>25_roof_wind_shingles_blew</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Wind</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>26</td>\n",
-       "      <td>56</td>\n",
-       "      <td>26_water_es_ms_damage</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>27</td>\n",
-       "      <td>55</td>\n",
-       "      <td>27_dmg_humboldt_lafollette_vandalism</td>\n",
-       "      <td>55</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>28</td>\n",
-       "      <td>55</td>\n",
-       "      <td>28_vandalism_damage_odonnell_bandshell</td>\n",
-       "      <td>54</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>29</td>\n",
-       "      <td>54</td>\n",
-       "      <td>29_street_light_run_damaged</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>45</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>30</td>\n",
-       "      <td>51</td>\n",
-       "      <td>30_airport_lightning_lights_runway</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>47</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>31</td>\n",
-       "      <td>50</td>\n",
-       "      <td>31_center_water_main_dept</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>18</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>32</td>\n",
-       "      <td>49</td>\n",
-       "      <td>32_phone_system_phones_telephone</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>33</td>\n",
-       "      <td>49</td>\n",
-       "      <td>33_vandalism_lemonweir_lock_gazebo</td>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>34</td>\n",
-       "      <td>47</td>\n",
-       "      <td>34_well_meter_flow_lightning</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>35</td>\n",
-       "      <td>47</td>\n",
-       "      <td>35_school_water_elementary_high</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>32</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>36</td>\n",
-       "      <td>45</td>\n",
-       "      <td>36_sign_vehicle_signal_traffic</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>37</td>\n",
-       "      <td>44</td>\n",
-       "      <td>37_overhead_door_damaged_loader</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>39</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>38</td>\n",
-       "      <td>44</td>\n",
-       "      <td>38_wind_fence_park_trees</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>42</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Wind</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>39</td>\n",
-       "      <td>44</td>\n",
-       "      <td>39_equipment_playground_slide_gps</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Misc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>40</td>\n",
-       "      <td>42</td>\n",
-       "      <td>40_storm_multiple_sites_locations</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>41</td>\n",
-       "      <td>42</td>\n",
-       "      <td>41_hs_lightning_hhs_damage</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>42</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>42</td>\n",
-       "      <td>41</td>\n",
-       "      <td>42_park_washington_vandalism_jacobus</td>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>43</td>\n",
-       "      <td>39</td>\n",
-       "      <td>43_hs_water_tremper_pw</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>44</td>\n",
-       "      <td>38</td>\n",
-       "      <td>44_laptop_mckinley_damaged_computer</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Vandalism</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>45</td>\n",
-       "      <td>38</td>\n",
-       "      <td>45_water_equipment_carpet_to</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>23</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>46</td>\n",
-       "      <td>38</td>\n",
-       "      <td>46_radio_antenna_lightning_radios</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>37</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>47</td>\n",
-       "      <td>33</td>\n",
-       "      <td>47_street_pole_light_streetlight</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>48</td>\n",
-       "      <td>33</td>\n",
-       "      <td>48_gym_floor_injured_k9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>14</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>49</td>\n",
-       "      <td>32</td>\n",
-       "      <td>49_ms_es_lightning_damage</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>50</td>\n",
-       "      <td>32</td>\n",
-       "      <td>50_hydrant_vehicle_struck_hit</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vehicle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>51</td>\n",
-       "      <td>31</td>\n",
-       "      <td>51_radio_lost_dropped_portable</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Misc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>52</td>\n",
-       "      <td>30</td>\n",
-       "      <td>52_roof_collapsed_collapse_gutter</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>13</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Misc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>53</td>\n",
-       "      <td>30</td>\n",
-       "      <td>53_buildings_building_water_basement</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>WaterW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>54</td>\n",
-       "      <td>30</td>\n",
-       "      <td>54_tower_lightning_north_internet</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Lightning</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Topic  Count                                    Name    0    1    2   3  \\\n",
-       "0      -1   1077            -1_vandalism_at_lightning_es  534    7  217  66   \n",
-       "1       0    208               0_glass_vandalism_west_es  208    0    0   0   \n",
-       "2       1    201           1_fire_smoke_damage_equipment   20  148    4   0   \n",
-       "3       2    182                2_froze_pipe_sewer_pipes    8    0    1   2   \n",
-       "4       3    176        3_power_surge_generator_spoilage    0    4   21   3   \n",
-       "5       4    161                 4_theft_of_stolen_break  128    1    0   0   \n",
-       "6       5    141              5_graffiti_on_kennedy_hoyt  140    0    0   0   \n",
-       "7       6    128            6_lightning_damage_scale_dpw    0    0  127   0   \n",
-       "8       7    112           7_park_vandalism_pavilion_dmg  111    0    0   1   \n",
-       "9       8    109            8_broken_door_glass_breakage   98    0    0   1   \n",
-       "10      9    109               9_lightning_dept_hall_hwy    0    0  108   0   \n",
-       "11     10    104      10_signal_traffic_damaged_paradise    0    0    1   1   \n",
-       "12     11     84              11_wind_damage_course_golf    0    0    0  83   \n",
-       "13     12     83             12_hydrant_fire_hit_damaged    0    0    0   0   \n",
-       "14     13     79               13_llm_glass_mendota_hawk   71    0    2   0   \n",
-       "15     14     77                 14_garage_door_hwy_shop    1    0    0   1   \n",
-       "16     15     76      15_computer_lightning_to_equipment    1    2   71   1   \n",
-       "17     16     73           16_fence_gate_vehicle_damaged    6    0    0   6   \n",
-       "18     17     73            17_building_truck_vehicle_by    2    0    0   4   \n",
-       "19     18     72         18_hail_buildings_roof_multiple    0    0    0   4   \n",
-       "20     19     68            19_water_damage_goodman_pool    0    0    0   0   \n",
-       "21     20     68       20_shelter_eastman_farlin_seymour   68    0    0   0   \n",
-       "22     21     67               21_pole_vehicle_hit_light    0    0    0   0   \n",
-       "23     22     64         22_pole_light_damaged_lightpole    4    0    0   0   \n",
-       "24     23     62        23_window_broken_windows_screens   55    1    0   1   \n",
-       "25     24     61                 24_laptop_theft_from_of   53    0    0   0   \n",
-       "26     25     57              25_roof_wind_shingles_blew    1    0    0  56   \n",
-       "27     26     56                   26_water_es_ms_damage    2    0    0   0   \n",
-       "28     27     55    27_dmg_humboldt_lafollette_vandalism   55    0    0   0   \n",
-       "29     28     55  28_vandalism_damage_odonnell_bandshell   54    0    0   0   \n",
-       "30     29     54             29_street_light_run_damaged    5    0    0   2   \n",
-       "31     30     51      30_airport_lightning_lights_runway    0    1   47   1   \n",
-       "32     31     50               31_center_water_main_dept    0    0    0   0   \n",
-       "33     32     49        32_phone_system_phones_telephone    3    0   37   0   \n",
-       "34     33     49      33_vandalism_lemonweir_lock_gazebo   48    1    0   0   \n",
-       "35     34     47            34_well_meter_flow_lightning    0    1   40   0   \n",
-       "36     35     47         35_school_water_elementary_high    1    0    1   0   \n",
-       "37     36     45          36_sign_vehicle_signal_traffic    4    0    0   0   \n",
-       "38     37     44         37_overhead_door_damaged_loader    2    0    0   1   \n",
-       "39     38     44                38_wind_fence_park_trees    0    0    0  42   \n",
-       "40     39     44       39_equipment_playground_slide_gps   12    0    0   1   \n",
-       "41     40     42       40_storm_multiple_sites_locations    0    0   14  14   \n",
-       "42     41     42              41_hs_lightning_hhs_damage    0    0   42   0   \n",
-       "43     42     41    42_park_washington_vandalism_jacobus   41    0    0   0   \n",
-       "44     43     39                  43_hs_water_tremper_pw    0    1    0   0   \n",
-       "45     44     38     44_laptop_mckinley_damaged_computer   26    0    0   0   \n",
-       "46     45     38            45_water_equipment_carpet_to    0    0    1   0   \n",
-       "47     46     38       46_radio_antenna_lightning_radios    0    1   37   0   \n",
-       "48     47     33        47_street_pole_light_streetlight    0    0    0   0   \n",
-       "49     48     33                 48_gym_floor_injured_k9    4    0    0   0   \n",
-       "50     49     32               49_ms_es_lightning_damage    0    1   31   0   \n",
-       "51     50     32           50_hydrant_vehicle_struck_hit    2    0    0   0   \n",
-       "52     51     31          51_radio_lost_dropped_portable    4    1    0   1   \n",
-       "53     52     30       52_roof_collapsed_collapse_gutter    1    1    0   3   \n",
-       "54     53     30    53_buildings_building_water_basement    1    0    0   1   \n",
-       "55     54     30       54_tower_lightning_north_internet    0    0   30   0   \n",
-       "\n",
-       "     4    5   6   7    8  mapping      label  \n",
-       "0    1  101  36  75   40        0  Vandalism  \n",
-       "1    0    0   0   0    0        0  Vandalism  \n",
-       "2    0   10   5   5    9        1       Fire  \n",
-       "3    0   11  32  96   32        7     WaterW  \n",
-       "4    0    1   0   2  145        8       Misc  \n",
-       "5    0    6   1   0   25        0  Vandalism  \n",
-       "6    0    0   0   1    0        0  Vandalism  \n",
-       "7    1    0   0   0    0        2  Lightning  \n",
-       "8    0    0   0   0    0        0  Vandalism  \n",
-       "9    0    3   0   0    7        0  Vandalism  \n",
-       "10   0    1   0   0    0        2  Lightning  \n",
-       "11   0  101   0   0    1        5    Vehicle  \n",
-       "12   0    0   0   0    1        3       Wind  \n",
-       "13   0   81   1   0    1        5    Vehicle  \n",
-       "14   0    2   0   1    3        0  Vandalism  \n",
-       "15   0   72   0   0    3        5    Vehicle  \n",
-       "16   0    0   0   0    1        2  Lightning  \n",
-       "17   0   60   0   0    1        5    Vehicle  \n",
-       "18   0   59   0   2    6        5    Vehicle  \n",
-       "19  68    0   0   0    0        4       Hail  \n",
-       "20   0    0  23  44    1        7     WaterW  \n",
-       "21   0    0   0   0    0        0  Vandalism  \n",
-       "22   1   64   0   0    2        5    Vehicle  \n",
-       "23   1   57   0   0    2        5    Vehicle  \n",
-       "24   0    1   0   2    2        0  Vandalism  \n",
-       "25   0    1   0   0    7        0  Vandalism  \n",
-       "26   0    0   0   0    0        3       Wind  \n",
-       "27   0    0  24  30    0        7     WaterW  \n",
-       "28   0    0   0   0    0        0  Vandalism  \n",
-       "29   0    1   0   0    0        0  Vandalism  \n",
-       "30   1   45   0   1    0        5    Vehicle  \n",
-       "31   0    2   0   0    0        2  Lightning  \n",
-       "32   0    2  18  29    1        7     WaterW  \n",
-       "33   0    0   1   3    5        2  Lightning  \n",
-       "34   0    0   0   0    0        0  Vandalism  \n",
-       "35   0    4   0   0    2        2  Lightning  \n",
-       "36   0    0  12  32    1        7     WaterW  \n",
-       "37   0   41   0   0    0        5    Vehicle  \n",
-       "38   0   39   0   0    2        5    Vehicle  \n",
-       "39   0    1   0   1    0        3       Wind  \n",
-       "40   0   12   0   0   19        8       Misc  \n",
-       "41   2    0   0  12    0        2  Lightning  \n",
-       "42   0    0   0   0    0        2  Lightning  \n",
-       "43   0    0   0   0    0        0  Vandalism  \n",
-       "44   0    0  11  26    1        7     WaterW  \n",
-       "45   0    5   0   0    7        0  Vandalism  \n",
-       "46   0    0  13  23    1        7     WaterW  \n",
-       "47   0    0   0   0    0        2  Lightning  \n",
-       "48   0   32   0   0    1        5    Vehicle  \n",
-       "49   0    0   7  14    8        7     WaterW  \n",
-       "50   0    0   0   0    0        2  Lightning  \n",
-       "51   0   28   1   0    1        5    Vehicle  \n",
-       "52   0    8   5   1   11        8       Misc  \n",
-       "53   1    0   1  10   13        8       Misc  \n",
-       "54   0    1  11  16    0        7     WaterW  \n",
-       "55   0    0   0   0    0        2  Lightning  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tb[\"mapping\"] = tb.values.argmax(axis=1)\n",
-    "tb[\"label\"] = [labels[i] for i in tb[\"mapping\"]]\n",
-    "mapping = {i: tb.loc[i, \"mapping\"] for i in tb.index}\n",
-    "topic_model.get_topic_info().merge(tb, on=\"Topic\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YkF2EY-hEVHK"
-   },
-   "source": [
-    "Now, let's apply this model to the validation set. First, we assign each sample to a cluster, based on the clustering model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "xeE03QWFEVHK",
-    "outputId": "b6a5ff69-a3a6-40be-f9b2-fb9fb38cfba5"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/scipy/sparse/_index.py:125: SparseEfficiencyWarning:\n",
-      "\n",
-      "Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "topics_test, probs_test = topic_model.transform(df_valid[\"Description\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gKz-IvZEEVHL"
-   },
-   "source": [
-    "Then, we apply the mapping from topics to labels, which we have defined above based on the training set. The table below shows for each topic the frequency by label, and the mapping."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "WRLhMAZhEVHL",
-    "outputId": "d396bc87-da4e-491e-be9d-7104692a7a9c"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>labels</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>mapping</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Topic</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>-1</th>\n",
-       "      <td>57</td>\n",
-       "      <td>3</td>\n",
-       "      <td>36</td>\n",
-       "      <td>28</td>\n",
-       "      <td>0</td>\n",
-       "      <td>28</td>\n",
-       "      <td>13</td>\n",
-       "      <td>8</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>35</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>21</td>\n",
-       "      <td>11</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>59</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>34</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>104</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
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-       "    <tr>\n",
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-       "  </tbody>\n",
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-      "text/plain": [
-       "labels    0   1   2   3   4   5   6   7   8  mapping\n",
-       "Topic                                               \n",
-       "-1       57   3  36  28   0  28  13   8  18        0\n",
-       " 1        5  35   0   1   0   1   2   1   2        1\n",
-       " 2        4   1   1   0   0   6  21  11   9        7\n",
-       " 3        0   0   5   0   0   0   0   0  29        8\n",
-       " 4       17   0   1   0   0   1   0   0   9        0\n",
-       " 5       59   0   0   0   0   3   0   0   1        0\n",
-       " 6        0   0  11   0   0   0   0   0   0        2\n",
-       " 7        9   0   0   0   0   0   0   0   0        0\n",
-       " 8       18   0   0   0   0   0   0   0   1        0\n",
-       " 9        0   0   5   1   0   0   0   0   0        2\n",
-       " 10       1   0   0   2   0  10   0   0   1        5\n",
-       " 11       0   0   0  34   0   0   0   1   1        3\n",
-       " 12       0   0   0   0   0  13   0   0   0        5\n",
-       " 13     104   1   0   1   0   5   1   1   1        0\n",
-       " 14       0   0   0   0   0  13   0   0   0        5\n",
-       " 15       0   1  26   0   0   0   0   0   2        2\n",
-       " 16       1   0   0   0   0   9   0   0   1        5\n",
-       " 17       1   0   0   0   0  15   0   0   3        5\n",
-       " 18       0   0   0   2  18   0   0   0   0        4\n",
-       " 19       0   0   0   0   0   0   5   3   0        7\n",
-       " 20       3   0   0   0   0   0   0   0   0        0\n",
-       " 21       0   0   0   1   0  47   0   0   0        5\n",
-       " 22       0   0   1   1   0   6   0   0   1        5\n",
-       " 23      15   0   0   0   0   0   0   0   3        0\n",
-       " 24       5   0   0   0   0   0   0   0   5        0\n",
-       " 25       0   0   0  22   0   0   0   0   1        3\n",
-       " 26       0   0   0   0   0   0   2   0   0        7\n",
-       " 28       1   0   0   0   0   0   0   0   0        0\n",
-       " 29       1   0   0   0   0  19   0   0   0        5\n",
-       " 30       0   0  11   1   0   0   0   0   0        2\n",
-       " 31       0   0   0   0   0   0   4   2   0        7\n",
-       " 32       0   0   4   0   0   0   0   0   0        2\n",
-       " 33       5   0   0   0   0   0   0   0   0        0\n",
-       " 34       0   1   3   0   0   0   0   0   0        2\n",
-       " 35       0   2   0   0   0   0   3   1   0        7\n",
-       " 36       0   0   0   0   0  22   0   0   2        5\n",
-       " 37       3   0   1   2   0  15   0   0   1        5\n",
-       " 38       0   0   0   7   0   1   0   0   0        3\n",
-       " 39       1   0   0   0   0   2   0   0   1        8\n",
-       " 40       0   0   3   3   0   0   0   3   0        2\n",
-       " 43       0   0   0   0   0   0   2   0   0        7\n",
-       " 44       0   0   0   0   0   0   0   0   2        0\n",
-       " 45       0   0   0   0   0   0   4   0   0        7\n",
-       " 46       0   0  11   0   0   0   0   0   0        2\n",
-       " 47       0   0   0   0   0   3   0   0   1        5\n",
-       " 48       0   0   0   0   0   0   6   3   2        7\n",
-       " 50       0   0   0   0   0   7   0   0   0        5\n",
-       " 51       0   0   0   0   0   1   0   0   3        8\n",
-       " 52       0   0   0   1   0   0   0   1   1        8\n",
-       " 53       0   1   0   0   0   0   4   3   2        7\n",
-       " 54       0   1   4   0   0   0   0   0   0        2"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_valid[\"Topic\"] = topics_test\n",
-    "df_valid[\"prob\"] = probs_test\n",
-    "df_valid[\"pred\"] = [mapping[t] for t in topics_test]\n",
-    "df_valid.to_excel(\"results/peril_topics.xlsx\")\n",
-    "tb_valid = pd.pivot_table(df_valid, index=[\"Topic\"], columns=[\"labels\"], aggfunc='count', fill_value=0)[\"Description\"]\n",
-    "tb_valid[\"mapping\"] = [mapping[t] for t in tb_valid.index]\n",
-    "tb_valid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m7epfnhsEVHL"
-   },
-   "source": [
-    "This classifier achieves an accuracy score of ca. 70%, compared to 30% obtained with the dummy classifier. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
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-    "id": "ZF9x0l1xEVHL",
-    "outputId": "8585246e-643d-4e7b-f2fa-20c3ea94a77c"
-   },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topic modeling by clustering\n",
-      "accuracy score = 69.6%,  log loss = nan,  Brier loss = nan\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.63      0.95      0.76       310\n",
-      "        Fire       0.74      0.76      0.75        46\n",
-      "   Lightning       0.86      0.63      0.73       123\n",
-      "        Wind       0.94      0.59      0.72       107\n",
-      "        Hail       0.90      1.00      0.95        18\n",
-      "     Vehicle       0.88      0.79      0.83       227\n",
-      "     WaterNW       0.00      0.00      0.00        67\n",
-      "      WaterW       0.23      0.61      0.33        38\n",
-      "        Misc       0.76      0.33      0.46       103\n",
-      "\n",
-      "    accuracy                           0.70      1039\n",
-      "   macro avg       0.66      0.63      0.61      1039\n",
-      "weighted avg       0.71      0.70      0.68      1039\n",
-      "\n"
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-    "\n",
-    "This is useful to validate the mapping. Let's use the search term \"Fire\" and retrieve the three most similar topics.\n",
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-      "topic  1: similarity score 91.8%, mapped to peril 1 (Fire)\n",
-      "topic 12: similarity score 69.0%, mapped to peril 5 (Vehicle)\n",
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-    "similar_topics, similarity = topic_model.find_topics(\"Fire\", top_n=3)\n",
-    "for t, s in zip(similar_topics, similarity):\n",
-    "    print(f\"topic {t:2d}: similarity score {s:.1%}, mapped to peril {mapping[t]:d} ({labels[mapping[t]]})\")\n",
-    "topic_model.visualize_barchart(similar_topics)"
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-    "As expected, the topics which have been mapped to \"Fire\" appear first in the list, with similarity scores of more than 80%.\n",
-    "\n",
-    "The first topic that was not mapped to \"Fire\" has a similarity score of less than 70%. It was mapped to the label \"Vehicle\".\n",
-    "Indeed: Although the word \"Fire\" ranks second in the word score, this is in combination with hydrant. This is about vehicles hitting fire hydrants."
-   ]
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-    "<a id='topic_modelling_refinement'></a>\n",
-    "<a name='topic_modelling_refinement'></a>\n",
-    "### 5.2. Refinement\n",
-    "\n",
-    "Above, a relatively large number of samples was classified as outlier. All outliers were mapped to a single class, but this mapping is questionable, because we have seen that outlier samples belong to different classes.\n",
-    "\n",
-    "To mitigate this issue, we could label the outlier samples manually. However, this is quite tedious.\n",
-    "\n",
-    "Alternatively, we can train a classifier to the labels obtained from the unsupervised approach. To avoid label noise, we suppress outliers.\n",
-    "\n",
-    "First, we create the training dataset. We replace the true labels by the labels obtained from the clustering approach."
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-   "source": [
-    "df_train_unsupervised = df_train[df_train[\"Topic\"]>=0].copy()\n",
-    "df_train_unsupervised[\"labels\"] = [mapping[t] for t in df_train_unsupervised[\"Topic\"]]\n",
-    "ds_train_unsupervised = Dataset.from_pandas(df_train_unsupervised)\n",
-    "ds_train_unsupervised = ds_train_unsupervised.map(tokenize, batched=True)"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": 47,
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-     "text": [
-      "loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /home/ubuntu/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
-      "Model config DistilBertConfig {\n",
-      "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
-      "  \"activation\": \"gelu\",\n",
-      "  \"architectures\": [\n",
-      "    \"DistilBertForMaskedLM\"\n",
-      "  ],\n",
-      "  \"attention_dropout\": 0.1,\n",
-      "  \"dim\": 768,\n",
-      "  \"dropout\": 0.1,\n",
-      "  \"hidden_dim\": 3072,\n",
-      "  \"id2label\": {\n",
-      "    \"0\": \"LABEL_0\",\n",
-      "    \"1\": \"LABEL_1\",\n",
-      "    \"2\": \"LABEL_2\",\n",
-      "    \"3\": \"LABEL_3\",\n",
-      "    \"4\": \"LABEL_4\",\n",
-      "    \"5\": \"LABEL_5\",\n",
-      "    \"6\": \"LABEL_6\",\n",
-      "    \"7\": \"LABEL_7\",\n",
-      "    \"8\": \"LABEL_8\"\n",
-      "  },\n",
-      "  \"initializer_range\": 0.02,\n",
-      "  \"label2id\": {\n",
-      "    \"LABEL_0\": 0,\n",
-      "    \"LABEL_1\": 1,\n",
-      "    \"LABEL_2\": 2,\n",
-      "    \"LABEL_3\": 3,\n",
-      "    \"LABEL_4\": 4,\n",
-      "    \"LABEL_5\": 5,\n",
-      "    \"LABEL_6\": 6,\n",
-      "    \"LABEL_7\": 7,\n",
-      "    \"LABEL_8\": 8\n",
-      "  },\n",
-      "  \"max_position_embeddings\": 512,\n",
-      "  \"model_type\": \"distilbert\",\n",
-      "  \"n_heads\": 12,\n",
-      "  \"n_layers\": 6,\n",
-      "  \"pad_token_id\": 0,\n",
-      "  \"qa_dropout\": 0.1,\n",
-      "  \"seq_classif_dropout\": 0.2,\n",
-      "  \"sinusoidal_pos_embds\": false,\n",
-      "  \"tie_weights_\": true,\n",
-      "  \"transformers_version\": \"4.19.2\",\n",
-      "  \"vocab_size\": 30522\n",
-      "}\n",
-      "\n",
-      "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "PyTorch: setting up devices\n",
-      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
-      "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, WaterNW, Vandalism, words per description, Fire, Hail, Vehicle, Misc, __index_level_0__, Description, Loss, Topic. If Lightning, WaterW, Wind, WaterNW, Vandalism, words per description, Fire, Hail, Vehicle, Misc, __index_level_0__, Description, Loss, Topic are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running training *****\n",
-      "  Num examples = 3914\n",
-      "  Num Epochs = 2\n",
-      "  Instantaneous batch size per device = 8\n",
-      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
-      "  Gradient Accumulation steps = 1\n",
-      "  Total optimization steps = 980\n"
-     ]
-    },
-    {
-     "data": {
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-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='980' max='980' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [980/980 00:40, Epoch 2/2]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>489</td>\n",
-       "      <td>0.387200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>978</td>\n",
-       "      <td>0.093200</td>\n",
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-     "text": [
-      "\n",
-      "\n",
-      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
-      "\n",
-      "\n",
-      "Saving model checkpoint to distilbert-base-uncased_peril_u\n",
-      "Configuration saved in distilbert-base-uncased_peril_u/config.json\n",
-      "Model weights saved in distilbert-base-uncased_peril_u/pytorch_model.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_name = \"distilbert-base-uncased\"\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "torch.manual_seed(42)  # for reproducibility, set random seed before instantiating the model\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels)).to(device)\n",
-    "\n",
-    "# train the model\n",
-    "batch_size = 8\n",
-    "logging_steps = len(ds_train_unsupervised) // batch_size\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=model_name+\"_peril_u_epochs\",\n",
-    "    num_train_epochs=2,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    metric_for_best_model=\"f1\",\n",
-    "    logging_steps=logging_steps,\n",
-    "    save_strategy=trainer_utils.IntervalStrategy.NO,\n",
-    ")\n",
-    "trainer = Trainer(model=model, args=training_args,\n",
-    "                  compute_metrics=compute_metrics, train_dataset=ds_train_unsupervised,\n",
-    "                  eval_dataset=ds[\"test\"])\n",
-    "trainer.train();\n",
-    "trainer.save_model(model_name + \"_peril_u\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Rs65tNt_EVHN"
-   },
-   "source": [
-    "Then, we evaluate the classifier on the test set, by comparing the predicted to the true labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 961
-    },
-    "id": "HldJUXwtEVHN",
-    "outputId": "4e384eaa-5e89-47de-b40e-e92bf7ddfe77"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss. If Lightning, WaterW, Wind, cls_hidden_state, WaterNW, Vandalism, Fire, mean_hidden_state, Hail, Vehicle, Misc, Description, Loss are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
-      "***** Running Prediction *****\n",
-      "  Num examples = 1039\n",
-      "  Batch size = 8\n"
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-      "Topic modeling by clustering, refined\n",
-      "accuracy score = 79.2%,  log loss = 1.456,  Brier loss = 0.385\n",
-      "classification report\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "   Vandalism       0.85      0.93      0.89       310\n",
-      "        Fire       0.76      0.85      0.80        46\n",
-      "   Lightning       0.88      0.94      0.91       123\n",
-      "        Wind       0.99      0.80      0.89       107\n",
-      "        Hail       0.90      1.00      0.95        18\n",
-      "     Vehicle       0.87      0.89      0.88       227\n",
-      "     WaterNW       0.00      0.00      0.00        67\n",
-      "      WaterW       0.28      0.87      0.42        38\n",
-      "        Misc       0.70      0.41      0.52       103\n",
-      "\n",
-      "    accuracy                           0.79      1039\n",
-      "   macro avg       0.69      0.74      0.69      1039\n",
-      "weighted avg       0.78      0.79      0.77      1039\n",
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-    "<a name='conclusion'></a>\n",
-    "## 6.&nbsp;Conclusions\n",
-    "\n",
-    "Congratulations!\n",
-    "\n",
-    "In this Part II of the tutorial, you have first applied the techniques you have learned in Part I to a dataset with shorter texts.\n",
-    "\n",
-    "Then you have learned how to use zero shot classification in a situation with no labels. The beauty of this approach is that it requires no training and produces a reasonable classification by a list of user-defined expressions.\n",
-    "\n",
-    "You have also seen that unsupervised classification can be achieved by similarity scoring between the input sequence and a list of user-defined expressions.\n",
-    "\n",
-    "Going one step further, you have seen an approach that creates clusters of similar documents and represents each cluster by typical words. This can be used as a starting point to create meaningful labels.\n",
-    "\n",
-    "If you have enjoyed this tutorial, feel free to apply any of the approaches - or improved versions, of course - to your own text data, to enrich your structured features available for supervised learning tasks."
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diff --git a/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_3.ipynb b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_3.ipynb
new file mode 100644
index 0000000..5fac5ab
--- /dev/null
+++ b/12 - NLP Using Transformers/Actuarial_Applications_of_NLP_Part_3.ipynb	
@@ -0,0 +1,1896 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kmbK3sewEVGy"
+      },
+      "source": [
+        "# Actuarial Applications of Natural Language Processing Using Transformers\n",
+        "### A Case Study for Processing Text Features in an Actuarial Context\n",
+        "### Part III – Case Studies on Car Accident Descriptions - Unsupervised Techniques Using ChatGPT\n",
+        "\n",
+        "By Andreas Troxler, September 2023\n",
+        "\n",
+        "In this Part III of the tutorial, you will learn how ChatGPT can be used to extract features from text when no labels are available.\n",
+        "This is very relevant in practice: text data is often available, but labels are missing or sparse!\n",
+        "\n",
+        "We use the car accident reports already familiar from Part I, and try to find out the number of vehicles involved and whether someone was injured.\n",
+        "\n",
+        "As a user of ChatGPT, you need to be aware of its limitations, which can lead to incorrect results. Limitations include the following:\n",
+        "* ChatGPT may create wrong answers, due to lack of common sense, lack of detailed and up-to-date information, lack of understanding of the context,biases and prejudices,  etc.\n",
+        "* Results obtained by ChatGPT are not reproducible across runs and model versions.\n",
+        "* It is difficult to explain how ChatGPT arrives at the answer.\n",
+        "* ChatGPT is a very complex Large Language Model (LLM). As such, it requires significant computational resources.\n",
+        "* ...\n",
+        "\n",
+        "With that in mind, let's get started."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EnmTW4uhEVG3"
+      },
+      "source": [
+        "## Notebook Overview\n",
+        "\n",
+        "This notebook is divided into tutorial is divided into three parts; they are:\n",
+        "\n",
+        "1. [Introduction.](#intro)<br>\n",
+        "    We begin by explaining pre-requisites. Then we turn to loading and exploring the dataset – ca. 6k records of English and German car accident reports with an average length of about 400 words. This is a repetition from Part I of the tutorial.<br><br>\n",
+        "\n",
+        "2. [Extract features using ChatGPT.](#chat_gpt)<br>\n",
+        "     We apply ChatGPT to extract some features from this data.<br><br>\n",
+        "    \n",
+        "3. [Conclusion](#conclusion)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcc6Je4lEVG4"
+      },
+      "source": [
+        "<a id='intro'></a>\n",
+        "<a name=\"intro\"></a>\n",
+        "## 1.&nbsp;Introduction\n",
+        "\n",
+        "In this section we discuss the pre-requisites, load and inspect the dataset."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iEu2UBDEEVG4"
+      },
+      "source": [
+        "<a id='prerequisites'></a>\n",
+        "<a name='prerequisites'></a>\n",
+        "### 1.1. Prerequisites\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2mGZv-UxvKO_"
+      },
+      "source": [
+        "#### Computing Power and OpenAI Account\n",
+        "\n",
+        "In this notebook, we use the API provided by OpenAI. Therefore, it does not require GPU support.\n",
+        "\n",
+        "On the flipside, you need to [set up an OpenAI account](https://platform.openai.com/signup?launch) and generate your personal API authentication key. In the following, we assume that this key is stored in the file `openai-key.txt` in the working directory. Of course, you can use a different file name. Do not share your API key with others, or expose it in the browser or other client-side code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qXPfoNIUIpuv"
+      },
+      "source": [
+        "#### Local files\n",
+        "Make sure the following files are available in the directory of the notebook:\n",
+        "* `tutorial_utils.py` - a collection of utility functions used throughout this notebook\n",
+        "* `NHTSA_NMVCCS_extract.parquet.gzip` - the data\n",
+        "* `openai-key.txt` - a text file containing your OpenAI API authentication key\n",
+        "\n",
+        "This notebook will create the following subdirectory:\n",
+        "* `results` - figures and Excel files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3ahENX7-EVG5"
+      },
+      "source": [
+        "#### Getting started with Python and Jupyter Notebook\n",
+        "\n",
+        "For this tutorial, we assume that you are already familiar with Python and Jupyter Notebook.\n",
+        "\n",
+        "In this section, Jupyter Notebook and Python settings are initialized.\n",
+        "For code in Python, the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/)\n",
+        "(\"PEP = Python Enhancement Proposal\") is enforced with minor variations to improve readability.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "1wK6e7a5EVG5",
+        "outputId": "0043b6d7-65ff-4d36-86d7-6acda4a5dc3c",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<style>.container { width:100% !important; }</style>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Notebook settings\n",
+        "\n",
+        "# clear the namespace variables\n",
+        "from IPython import get_ipython\n",
+        "get_ipython().run_line_magic(\"reset\", \"-sf\")\n",
+        "\n",
+        "# formatting: cell width\n",
+        "from IPython.display import display, HTML\n",
+        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5V9gFHqyEVG7"
+      },
+      "source": [
+        "#### Importing Required Libraries\n",
+        "\n",
+        "If you run this notebook on Google Colab, you will need to install the following libraries:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Mxggg0WmFDuy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "010fad57-e734-4463-bd92-1333b1e887a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (0.28.0)\n",
+            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai) (3.8.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.2.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2023.7.22)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (23.1.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (6.0.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (4.0.3)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.9.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.4.0)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install openai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install retry"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aYrTzcumrSE9",
+        "outputId": "6b4b3aec-376c-4045-badd-2ae6c0e42df9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: retry in /usr/local/lib/python3.10/dist-packages (0.9.2)\n",
+            "Requirement already satisfied: decorator>=3.4.2 in /usr/local/lib/python3.10/dist-packages (from retry) (4.4.2)\n",
+            "Requirement already satisfied: py<2.0.0,>=1.4.26 in /usr/local/lib/python3.10/dist-packages (from retry) (1.11.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install kaleido"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "598t72H5Dxui",
+        "outputId": "84b8b633-3170-4afd-abea-86d7c54fe073"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: kaleido in /usr/local/lib/python3.10/dist-packages (0.2.1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7b2yEtZjEVG8"
+      },
+      "source": [
+        "Next, we import the required libraries:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LuwY5ubtEVG9",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import openai\n",
+        "import pandas as pd\n",
+        "import plotly.express as px\n",
+        "from tqdm import tqdm\n",
+        "from retry import retry\n",
+        "from wordcloud import WordCloud\n",
+        "from tutorial_utils import evaluate_classifier"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "44WiOM3SEVG-"
+      },
+      "source": [
+        "<a id='dataexploration'></a>\n",
+        "<a name='dataexploration'></a>\n",
+        "### 1.2. Exploring the Data\n",
+        "\n",
+        "You can skip this section if you are already fammiliar with Part I of this tutorial.\n",
+        "\n",
+        "The data used throughout this tutorial is derived from data of a vehicle crash causation study performed in the United States from 2005 to 2007. The dataset has almost 7'000 records, each relating to one accident. For each case, a verbal description of the accident is available in English, which summarizes road and weather conditions, vehicles, drivers and passengers involved, preconditions, injury severities, etc. The same information is also encoded in tabular form, so that we can apply supervised learning techniques to train the NLP models and compare the information extracted from the verbal descriptions with the encoded data.\n",
+        "\n",
+        "The original data consists of multiple tables. For this tutorial, we have aggregated it into a single dataset and added German translations of the English accident descriptions. The translations were generated using the [DeepL python API](https://pypi.org/project/deepl/).\n",
+        "\n",
+        "To explore the data, let's load it into a Pandas DataFrame and examine its shape, columns and data types:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DogoPiqXEVG-",
+        "outputId": "cab3c351-d8ee-489f-9146-eb9d187bff22",
+        "pycharm": {
+          "name": "#%%\n"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "shape of DataFrame: (6949, 16)\n",
+            "('level_0', dtype('int64'))\n",
+            "('index', dtype('int64'))\n",
+            "('SCASEID', dtype('int64'))\n",
+            "('SUMMARY_EN', dtype('O'))\n",
+            "('SUMMARY_GE', dtype('O'))\n",
+            "('INJSEVA', dtype('int64'))\n",
+            "('NUMTOTV', dtype('int64'))\n",
+            "('WEATHER1', dtype('int64'))\n",
+            "('WEATHER2', dtype('int64'))\n",
+            "('WEATHER3', dtype('int64'))\n",
+            "('WEATHER4', dtype('int64'))\n",
+            "('WEATHER5', dtype('int64'))\n",
+            "('WEATHER6', dtype('int64'))\n",
+            "('WEATHER7', dtype('int64'))\n",
+            "('WEATHER8', dtype('int64'))\n",
+            "('INJSEVB', dtype('int64'))\n"
+          ]
+        }
+      ],
+      "source": [
+        "df = pd.read_parquet(\"NHTSA_NMVCCS_extract.parquet.gzip\")\n",
+        "print(f\"shape of DataFrame: {df.shape}\")\n",
+        "print(*list(zip(df.columns, df.dtypes)), sep=\"\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The column `SCASEID` is a unique case identifier.\n",
+        "\n",
+        "The columns `SUMMARY_EN` and `SUMMARY_GE` are strings representing the verbal descriptions of the accident in English and German, respectively.\n",
+        "\n",
+        "`NUMTOTV` is the number of vehicles involved in the case. Let's have a look at the distribution of this feature:"
+      ],
+      "metadata": {
+        "id": "HbWiphKq49iZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig = px.bar(df[\"NUMTOTV\"].value_counts().sort_index(), width=640)\n",
+        "fig.update_layout(title=\"number of cases by number of vehicles\", xaxis_title=\"number of vehicles\",\n",
+        "                  yaxis_title=\"number of cases\")\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"num_vehicles\"}})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "sZJYuvAc5HIW",
+        "outputId": "2aa6cf0c-2caf-4bd0-9600-2682e277702f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"37c99339-a50d-4528-87f4-f993dc5dc454\" class=\"plotly-graph-div\" style=\"height:525px; width:640px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"37c99339-a50d-4528-87f4-f993dc5dc454\")) {                    Plotly.newPlot(                        \"37c99339-a50d-4528-87f4-f993dc5dc454\",                        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+              "                            \n",
+              "var gd = document.getElementById('37c99339-a50d-4528-87f4-f993dc5dc454');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Most cases involve two vehicles, and only very few accidents involve more than three vehicles.\n",
+        "\n",
+        "Each of the columns `WEATHER1` to `WEATHER8` indicates the presence of a specific weather condition (1: weather condition present, 9999: presence of weather condition unknown, 0 otherwise):\n",
+        "\n",
+        "| column | meaning | count |\n",
+        "|---|---|---|\n",
+        "| `WEATHER1` | cloudy | 1112 |\n",
+        "| `WEATHER2` | snow | 114 |\n",
+        "| `WEATHER3` | fog, smog, smoke | 28 |\n",
+        "| `WEATHER4` | rain | 624 |\n",
+        "| `WEATHER5` | sleet, hail (freezing drizzle or rain) | 25 |\n",
+        "| `WEATHER6` | blowing snow | 38 |\n",
+        "| `WEATHER7` | severe crosswinds | 20 |\n",
+        "| `WEATHER8` | other | 25 |\n",
+        "\n",
+        "\n",
+        "These weather conditions are not mutually exclusive, i.e., more than one condition can be present in a single case. The frequency distribution looks as follows:"
+      ],
+      "metadata": {
+        "id": "AR_3rVMz5lyz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig=px.bar(x=range(1,9), y=[(df[\"WEATHER\"+str(i)]==1).sum() for i in range(1,9)], width=640)\n",
+        "fig.update_layout(title=\"number of cases by weather condition\", xaxis_title=\"weather condition\",\n",
+        "                  yaxis_title=\"number of cases\")\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"weather\"}})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "aR7onfnc6IUY",
+        "outputId": "c5e55c64-39c9-48e0-c211-0341646d8abf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The most frequently recorded weather conditions are \"cloudy\" (`WEATHER1`) and \"rain\" (`WEATHER4`).\n",
+        "\n",
+        "`INJSEVA` indicates the most serious sustained injury in the accident. For instance, if one person was not injured, and another person suffered a non-incapacitating injury, injury class 2 was assigned to the case.\n",
+        "\n",
+        "Information on injury severity has been taken from police accident reports, which are not available in the data. Unfortunately, this information does not necessarily align with the case description: There are many cases for which the case description indicates the presence of an injury, but INJSEVA does not, and vice versa.\n",
+        "\n",
+        "For this reason, we created manually an additional column `INJSEVB` based on the case description, to indicate the presence of a (possible) bodily injury. The table below shows the distribution of number of cases by the two variables.\n",
+        "\n",
+        "| `INJSEVA` | meaning | count | `INJSEVB`=0 | `INJSEVB`=1 |\n",
+        "|---|---|---|---|---|\n",
+        "|  0 | O - No injury | 1'458 | 96| 1'554 |\n",
+        "|  1 | C - Possible injury | 1'112 | 1'298 | 2'410 |\n",
+        "|  2 | B - Non-incapacitating injury | 729 | 945 | 1'674 |\n",
+        "|  3 | A - Incapacitating injury | 304 | 373 | 677 |\n",
+        "|  4 | K - Killed | 5 | 114 | 119 |\n",
+        "|  5 | U - Injury, severity unknown | 44 | 122 | 166 |\n",
+        "|  6 | Died prior to crash  | 0 | 0| 0 |\n",
+        "|  9 | Unknown if injured  | 51 | 16 | 67 |\n",
+        "| 10 | No person in crash  | 1 | 0| 1 |\n",
+        " 11 | No PAR (police accident report) obtained | 231 | 50 | 281 |\n",
+        "|**Total**| | **3'935** | **3'014**| **6'949**|\n",
+        "\n",
+        "Now we turn to the verbal accident descriptions. First, we examine the length of the English texts, `SUMMARY_EN`. To this end, we split the texts into words, with blank spaces as separator, and show a box plot of the text length by number of vehicles involved in the accident:"
+      ],
+      "metadata": {
+        "id": "5JgGwQUt6Y8l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# statistics of summary length\n",
+        "df[\"words per case summary\"] = df[\"SUMMARY_EN\"].str.split().apply(len)\n",
+        "print(f\"Overall number of words by case summary: min {df['words per case summary'].min()}, \"\n",
+        "      f\"average {df['words per case summary'].mean():.0f}, max {df['words per case summary'].max()}\")\n",
+        "fig = px.box(df, x=\"NUMTOTV\", y=\"words per case summary\", width=640)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"text_length\"}})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "EqRcgK837K42",
+        "outputId": "583bc6b9-b925-4f2b-dbb5-08c6d21bc102"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Overall number of words by case summary: min 60, average 419, max 1248\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "                            \n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Not surprisingly, the length of the descriptions correlates with the number of vehicles involved.\n",
+        "\n",
+        "The average length is above 400 words.\n",
+        "\n",
+        "Let's examine one of the English texts and its German translation:"
+      ],
+      "metadata": {
+        "id": "aXS50_o07R_W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(HTML(df.loc[0, \"SUMMARY_EN\"]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138
+        },
+        "id": "TFgOi5y_7a7w",
+        "outputId": "c38cdfe6-5fa0-4ff0-ab3d-52fee97088dd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.  The posted speed limit on this roadway was 80 kmph (50 MPH). V1 entered the roadway by crossing over the two southbound lanes and then entering the third northbound lane, which was a left turn-only lane at a 4-way intersection. The driver of V1 intended to travel straight through the intersection, and so he began to change lanes to the right. He did not see V2, a 1994 Pontiac Grand Am, that was traveling in the second northbound lane. The northbound roadway had curved to the right prior to the private driveway that V1 had exited. As V1 began to change lanes to the right, the front of V1 contacted the left rear of V2 before coming to final rest on the roadway.\r \r The driver of V1 was a 60-year old male who reported that he had been traveling between 2-17 kmph (1-10 mph) prior to the crash.  He had no health-related problems, and had taken no medication prior to the crash.  He was rested and traveling back home.  He was wearing his prescribed lenses that corrected a myopic (nearsighted) condition.  He did not sustain any injuries from the crash and refused treatment.\r \r The Critical Precrash Event for the driver of V1 was when he began to travel over the lane line on the right side of the travel lane. The Critical Reason for the Critical Precrash Event was inadequate surveillance (failed to look, looked but did not see). Associated factors coded to the driver of V1 include an illegal use of a left turn lane (cited by police) and an unfamiliarity with the roadway. As per the driver of V1, this was the first time he had driven on this roadway.  \r \r The driver of V2 was a 28-year old woman who reported that she had been traveling between 66-80 kmph (41-50 mph) prior to the crash.  She had no health-related problems, and had taken no medication prior to the crash.  She was rested and on her way home.  She does not wear corrective lenses.  She sustained minor injuries and was transported to a local trauma facility.\r \r The Critical Precrash Event for the driver of V2 was when the other vehicle encroached into her lane, from an adjacent lane (same direction) over the left lane line.  The Critical Reason for the Critical Precrash Event was not coded to the driver of V2 and no associated factors were coded to her."
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(HTML(df.loc[0, \"SUMMARY_GE\"]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "id": "0LSITZqq7ffd",
+        "outputId": "857968b6-81c0-4fdb-cd26-9df898135987"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.  Die zulässige Höchstgeschwindigkeit auf dieser Fahrbahn betrug 80 km/h (50 MPH). V1 fuhr auf die Fahrbahn, indem er die beiden Fahrspuren in Richtung Süden überquerte und dann auf die dritte Fahrspur in Richtung Norden einfuhr, die an einer Kreuzung mit vier Fahrspuren nur für Linksabbieger vorgesehen war. Der Fahrer von V1 beabsichtigte, geradeaus über die Kreuzung zu fahren, und begann daher, die Spur nach rechts zu wechseln. Dabei übersah er V2, einen Pontiac Grand Am von 1994, der auf der zweiten Fahrspur in Richtung Norden unterwegs war. Die Fahrbahn in nördlicher Richtung war vor der privaten Einfahrt, aus der V1 herausgefahren war, nach rechts gebogen. Als V1 begann, die Spur nach rechts zu wechseln, berührte die Front von V1 das linke Heck von V2, bevor er auf der Fahrbahn zum Stehen kam. Der Fahrer von V1 war ein 60-jähriger Mann, der angab, vor dem Unfall mit einer Geschwindigkeit von 2 bis 17 km/h unterwegs gewesen zu sein.  Er hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Er war ausgeruht und auf dem Weg nach Hause.  Er trug die ihm verschriebenen Kontaktlinsen, die eine Kurzsichtigkeit korrigieren.  Er zog sich bei dem Unfall keine Verletzungen zu und lehnte eine Behandlung ab. Das kritische Ereignis vor dem Unfall war für den Fahrer von V1, als er begann, die Fahrspurlinie auf der rechten Seite der Fahrbahn zu überfahren. Der kritische Grund für das kritische Ereignis vor dem Unfall war unzureichende Überwachung (nicht hingesehen, hingesehen, aber nicht gesehen). Zu den assoziierten Faktoren, die dem Fahrer von V1 zugeschrieben werden, gehören das illegale Benutzen einer Linksabbiegerspur (von der Polizei verwarnt) und die Unkenntnis der Fahrbahn. Für den Fahrer von V1 war es das erste Mal, dass er diese Fahrbahn befuhr.  \r \r Bei der Fahrerin von V2 handelte es sich um eine 28-jährige Frau, die angab, vor dem Unfall mit einer Geschwindigkeit von 66-80 km/h unterwegs gewesen zu sein.  Sie hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Sie war ausgeruht und befand sich auf dem Heimweg.  Sie trägt keine Korrekturgläser.  Sie erlitt leichte Verletzungen und wurde in eine örtliche Unfallklinik gebracht. Das kritische Ereignis vor dem Unfall war für die Fahrerin von V2, als das andere Fahrzeug von einer benachbarten Fahrspur (gleiche Richtung) über die linke Fahrspurlinie in ihre Spur eindrang.  Der kritische Grund für das kritische Vorunfallereignis wurde der Fahrerin von V2 nicht zugeordnet, und es wurden ihr keine zugehörigen Faktoren zugeordnet."
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To get an impression of the most frequent words, we generate a simple word cloud form all English case descriptions. By default, the word cloud excludes so-called stop words (such as articles, prepositions, pronouns, conjunctions, etc.), which are the most common words and do not add much information to the text."
+      ],
+      "metadata": {
+        "id": "iQSybuXr7oXF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text = df[\"SUMMARY_EN\"].str.cat(sep=\" \")\n",
+        "\n",
+        "# Create and generate a word cloud image:\n",
+        "word_cloud = WordCloud(max_words=100, background_color=\"white\").generate(text)\n",
+        "\n",
+        "# Display the generated image:\n",
+        "fig = px.imshow(word_cloud, width=640)\n",
+        "fig.update_layout(xaxis_showticklabels=False, yaxis_showticklabels=False)\n",
+        "fig.show(config={\"toImageButtonOptions\": {\"format\": 'svg', \"filename\": \"word_cloud\"}})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "qkpolrOT7tHV",
+        "outputId": "58952a6c-a752-4f91-cee2-2b6485a7eeea"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
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ucE3Uu4xihAvDKQ9iGN43rFSSrWSBAtf\\u002f+behIwIp8VmHVgOt5OGLtXeovImhTouSEJH0eZutclqc89RWJSd8uXxi+Fiv\\u002fTwYIDeJt6yUemfHb3AYTJmZyY2dPXe4p7KU5IWEZwYGnDocmVTVzCLQZ8zvDcwOT45ekfutYSQABFv6JOhRidEfHkkb29+2fpZOWRff\\u002fypJ3YjPPjKom\\u002f\\u002fc+CBWe857JjAn\\u002fvetoc8PcMf\\u002fuPXxqqO9ibFh\\u002f\\u002f41T9QsP7FBc721Pw7x\\u002fznyW1rJ70jEHE37HvKU2H3Y0nDxQvvHPvQvA85PNbMZSNDIiXEDt95Ymt7k0LZqX3nia2iAP6jry+JSfbYw8BkM3Qa4\\u002f0z37PbHI+9scS1YesKgiKvf3XP12\\u002fvWzvpHbvdEREb9Mame31JXFZYmrxIFcHh8+ULJ6frjeaK+s6RaZF0HypxUrb4tvCJcfxQmlv1uV8QH2drqL7W8uRtn5KtAC9+ftek+b4qCyUPzf7APW8+ISPis71Pkoxe0Cj1jy34mJTLHp8e9q9v1kmkfq7G+6f\\u002ft62hT4qHl8HPXrA5HK45ASTsDsz9l8NxwHCchlK8TyjLU7L1zOWvj12KD5GES0RvrprltGuM5tUfb2fSaWsmDQ\\u002fxF6oMptLmDiGH9fCcsQDwzPcHamXd+168x1n+xa2Hr9S3H3ntPuIrhuN6k0Vvtn57PH9nXsneF+8WcFgCNovF6HmGn96y\\u002f1Rp3e3jMsckRtJRpK5TiWHYAzNzAADDcJ3ZojdZPjt04WxZw\\u002fZnVgs4LD6HSYSovJ\\u002fY0HKDQ3mLO2TDpD3qsL+qcmFScl\\u002f\\u002fXwutyrBi9BuHB37ruuL9ilVpT6mtMgbK9iJbG3ecD\\u002fTncznMy+Utr6yfTXZTcVVdx6Oz4\\u002flhZIcPUL9S3EnMjAgOF3e2urUKj5XciGB5GuUzoPYgAPiJ+S9vWPv8ii+d9awJczKf+3AVEeZ0hTLXwRO5NU3jEqJya5o4TEZ9l3JMXGSYv5BweVErAKBUHwShSCgloCxPyYKRKR\\u002fvP19U1\\u002fb4vPGudj8ue9tTKzcczvvq2MVurdGPy04KC7x7ik95UgBwqKjypW1HnF8Xv\\u002fsDAIh47LNvPUxYPrhn\\u002fg+nivYXVuy5VEpD0TCJ8K7JPTv\\u002f+dyV9\\u002faccW5LDFqMCBAdfPleuOET+5Mo6eysUSqGS0NwgLyW5tTAoBCBoLRTPkwaclnWXtHV3W2k6La+CRR3tQ8L7Elw31dfvijWGbG+BYiYYTpbF5cmIjtcWLtw9IGzpR3d2vuX+voGatDLqnTN8fxwAFgWMYns9oqvggUAE+Zm7tp0mmTMP1lhs9o91aX7xWO+KNUYRu8kD4964OWFG9\\u002fYAwArH5m+9tk5lG1ju20ATcIKWVdOXGSlrOveiSO5TMbVZplTsG4VIh6n6IMnyFYAABDxOC\\u002ffPu3l26eRHQAf3UOuX7y7Zq7r1wUjUxaMTHG1kKCh6Lrpo9a5pEE4WTM5e83kbJKxqK39+6LL2WGhmVKplxO7VSDEKHQmo1OvD+LxC9rb1o8YacUcAJDf1vbQyFFfFRaQtxki6jXKXFlTmiS422SYGZlwtLk6iMOvUnWlioODuLwSReewwNAieVulSt5lujWi6aTLXDdSckeF5jgOuKe+Dh6HuWI2+df3zm3hE2r1bYOrYfn6YgcPtR6TwVKcW0O2+gx1\\u002f2B6uPR6k35ALLp7wszbRz3\\u002f0aq7n5tLqVYwwKD78MiQbXlXsqPDTDZbebuciD3\\u002fjS9UyOX+HE5JR6fDt5jDTcaBYWIOJ7+1rbCtTWMx0xCkTqms6JKXyuUBHO6u8vKBzlfhOzqbJZjLz+9ssWCOK13tNgwrkretTMzK7WiScgU2QjQ7W+5MGs6ieasHCP15N9ge7BcJK7pIuQtFaJ7UCgB+3J\\u002f\\u002f5tdHXv\\u002fy0Lub\\u002fyD7PGN2WHe3nq3SNpMd\\u002feHtipBIyooMCvN379HLPVo6aoq3l7MnlHItZc7UxBtoYz7z3gqyqS8Oqti8J4ZHhW06XXDXuOy82iar3aE1Wcgl\\u002fsYD2WGhByurkgMDPbWCby1ZUqlzFLpzXPr7s+YAQHpQkNPyZ1DY2erHYtMQdGZE\\u002fAMnd2+atvRoU\\u002fXuujIpV1CnUZQpO0sVnQFs3q7aUovjzxJNHwnhpIRwUuTmWrLDhbULRwOAw4Ft\\u002fv0ijoOPl61O3yZk8Kp0rQmCcFLmSk11x++7ChAEsVrtKIqyWPRnnp\\u002fn9A5AsABg4tzMXd\\u002f2RisILh4ve+Lt2xGqWLJ38v4oowz5D6I96DuUnYmeQBDYeM9tADAuIcqBUcfLB4rOrmk01DQb6zvNbSqrQmNTWjGLFbPgACyUxURZLJTtxxBLWEESZmAQOzSGl+DHEJP3MkRguKPZWF+rr2g1NSosnSqrwopZbLiVjjDYNI4\\u002fQxLIConkxSUK0kLYvT2tvpAaFJQaFFQh79O\\u002f4SNWzFKjK6vSlcrMzXJLh8lutOEWBsJi0ziBrOAQTkQiPz1JmM5Ce3LTndAQb4FFEs4eXndtcre4YsHMdfrKOn2FzNzabenU2zVWzOLAHUyUxaHxxMzAIJY0mpeQwE+TsILIGwPclzbKKYg\\u002fzrwDABbFpjotH06YDwDpkuABieafccU6TJWd5hoHbusy180OfZ7svk57l8ZksTkceEObwoH52neU5hdzqvNKHD\\u002fUPc8uIVH6wosLt2\\u002fNXbRkBIfD\\u002fPmnC646OEDBmpflLlhqhb6sqDF9VAzJ3i+UCQ2Dbg\\u002f6CDbAmZ6c3KBa6eyaS4rT19QFzcZ6HKjPweQwmhxGAJBbZDX6MqddxJTE8BLThdlpftkcGsXQmUHQaW4\\u002f333ssipPb9eSfQBW3GLFLFqbuslYV6g6DwDB7NDR4snjA2b4cgKb8guJO6yyq\\u002fuDeXPIbs9obKoTnfvylWeJ6+CKBTdZMJPGpqzVV5zrOsZEWSP8x00LXhjE6u31Z6F\\u002fbuZEnb7iQveJa5oCG2Yl+wDMDpPZYVJZu+v0FXmKUwAQwY0ZK5k2WjyZgfbp\\u002fHFXIl8slPx5V0zKSeYzAvj0AKW1hexzobyuo1OhQ1Fk9byRPqoVAMTzw+L5YbX6NrLjOt3durZWJYfDbGlREnO0EfaBCVbSsMigUJG8XU2y5x4tGahgeZoQ+Ubag77ge3r3UKG0dh+W\\u002fVakynXgg6zhq62KK9a8K6o8GkLP8Btxb8xTTtexsppZaQm9RX1AbVXsbf\\u002f5iirPk25S0mlu39++\\u002fXjn3hnBi6YGLfDyZgaAhSlJUoEAAGq6FWSfB3DAT3TuO9rxuxXzqd1txSx5ilP5yrOTA+fOD72DjjAAgImy6AjDjg\\u002fBYAYSrabGPa1bXd8ivtBibGgxbj4s27kwdOVoyWQvkaBBcBOumMWh59MDyNbrVDXKdxy9DAhitdlRFG1sV2YkeEwZc+XX5lPEhag3yP6ZsprkJbh73aQ\\u002fjpSYzbY77xqHutQVfFVEJxPmUggKZezcO\\u002fknyylbZ5TtQTtuV1opbv0uS6fZYSZbvULko98cMNxxWLbznYpn8pVnB61WrjhwO4vWp1Z\\u002ftdnX8YAEF7pPvFPx\\u002fGVV7oDUyonJYdzf\\u002fsuHVS+3m7y9daUCQUlHJwAYbdRPAgmjw\\u002fBl7dv723\\u002fx8dlz4sAdJ+UHPq5+XWntJix8uqBvkRsFw7FDst8+rHploGrlRGfX\\u002fNz89Ze17+ioKrOD4yZcMa2ts0ix87Jyd7n6GOXdkhQd9OqDcyKl\\u002fv+4d8brD84Jlgh8fLamBQ+\\u002fI3LqHZFTV0Z67DjukuvKy9vGjIvv7DtD1IAFi7KvsLNVWV\\u002feTrZ6JfcohcZ5ag8SzROyFaBYXeS87gBQpMrHcMzFfytRWrs+qX79SMcuG+bTQ+sj4yR9fmM+m\\u002fXCjsMfHT3\\u002f0dHzrnZ3HLh9e\\u002fM3O1q+tWAmsm+AtJmaPql+rURTSHa4cKW9HQCK2tr67SXU2bWf1\\u002fy7WjdIOQCAVmPDpzX\\u002f6rZ0AACfPpR5J2aHaVP9+0c7dmM4xct1QFTrSt+vfLHD3Ep2DJybc8WEjOAs8eJh\\u002fosnBN3npW7YpdK3dKpbOtVNMhVlSNqdQJaI+BDNk\\u002fZxuHD8WMmceVl6vbm6Uma39z7UA2sSAkDSsMjAEFGXTE2yXzhaEpvak\\u002fDWL1aLvfBsJdnq0h402PWnu46bHaZYXnyGaPjJzqNBbGk0L85g1x\\u002fu2GdxmEM44dOCZgFAnuKcBTOP8M\\u002fh0DjHOw\\u002fJLR0J\\u002fOR4fmKf\\u002fd502k0tG+veoRTZG0HKDo\\u002fm9WkArh6TZaOqqJJw4I4tDZ+UaorIjsFiwcyb6z9aE\\u002fXISPEEsg8AACbFRH99qSBOIvbeS2jDrJvq3ms3UXQWDwi1VbGh9u1nk94WMPzgRgW5Bwtm2lD7drORInAxODQ25Wc1bzwW\\u002f2oop2dkzyC4uVcMRxFah6lKyukdBEbigWXjDp0rM1vs9y7OcW273SB0Bq29TcXlMmXtKjq9t141YMFCEGTC3Mzft5wl2XOPltz1tE+J+QBw+VyV2UgRtnS2Bx24o9MsWxfzMPF1pHhMsboIAOoMNbG8eCbKMl+vJqQKM+P5Cdubf1gb\\u002fUAoJ3x28HzUa3jlJtBpbvu85g2jo\\u002f+sPx5dEMAKFjHELJSNIjSzw2R2GBVWebelk7ISPrZv9QoA\\u002fDgeg6aubGv60otaIYBE8xJi+UlBrBAeXcBAmDbcqrGpOs1tdfrKNlMTeQMAAMAB39a8kYEys0QUw7aj\\u002ff0fzBlV1iknO\\u002frya8vmJs+KgCJoLC85QZAaxArh0HgY7jA5jN3WziZDba2+gtQaUlq7Nzd8RHXZBoMDt39d974XteLR+fH81ChuPJ8u4NEFAIjBrtXbdY3Gmlpduadf32DXf13\\u002f3rOJbwkZIrLPN27mFZMZy4PZiW2m0mBOAuKhNebHZ6+aO\\u002fTjFlatHnvk8LXODs1d9050tQ9YsABg4rwsd8FqrO5ob+qmXBbMHcqYF6k96M8Uuzh7SBKkfNfwVTQvbmrQTMLix\\u002fBjoEzi8fZScb1p6O3ar+ve83S\\u002fEiTw04b7j0kWZkqYFN3eAGDDbJ3m1hp9eZWupE5fSdxndIQxStznxwOAhm7VlnOFNodjdnri1ORYkpfgROf+IlUu2QoAAAyUOTlwzsTA2SLPmROd5vZT8oOXlKfdm9sYjm1t+jKIHUJKergqk5V2yK2Yo0Iuf2+ux17CEk1hgZJ8IznJEU+eE3K7mEl9R1kwc77i7NGO3Tq7xmms11e5FLkhdrf+WKevIFsBACCalzBbujRFmOXpfsMBL1ZdPNyxs9NMESdRWxU\\u002fNH72WMKrnjb3wk2+YtH8UQWKX8XMCE9qNWhOdl4WMLiBLFFed9kdkVPdR0HLZJqy0tbVa8Y1NymCg\\u002f2c9sEIVvLwyACpn\\u002fuqWblHS29fP4VkdAdzYJeoJkT21D\\u002fYZem80H2m3dQaxomI4sbo7DqtTV2nr0kVZpBKRnJjdrX9ki0aHccfWMfZELK9+RuF1WO1IlGQflvYmjBOFNnRFwbKCOfGhHNjpgbNt2HWMu3lQuUFDo3Lo5Nnd9lZUPKvxdNpKPrfQ2cmJca45160GhsOyH4hGQnCuTH3Rj8RwPIYRyAIZoeujHxgXMC07xs\\u002fU1jIf5oVs2yp\\u002f\\u002fiF5P+69txnhYRIBYJgPr9OqXQp2wc7btvZ+j3ZCgAADJR5V9SjlBU3JyyUPTFw1gjxuJ8aN5Rri8nuG6NEU3i+myJvm4bQl0fc617PJYEAMtx\\u002fbJYoZ1fr95T7qdVXnO86NjHQ1xYJwc2\\u002fYkaHmo4w+XSKsPINYsFsuM1Yo2sbLUluMcrdg1muMazh2dHOViFZ2HwBQZAJVKF3yrwqdzxOiOzSPyhk+C0J68lZD2QFLwtf9XjC8xl+w452HHgk7unlEWsKlHkAMDN4XignHADujl4PAJMCpy0JW3EL1apQed5Ty4uG0JdHrHs0\\u002fuV+1YoEA2UOE425P\\u002fbZO6N6Gsh9QKBdrVMaTCarzT1zBwf815Zv3WtGABDHT34i4bV+1cpJJDfu6cR\\u002fS9nhZAeA3CI70rGLZGzVaL8runy2oZFkd3Ku65iaqucXAeTe6Ce9P3tOuDT+A7HPpftlkx03gBWzUOoCA2U+Fv9Kv2rlBEXQ5RHrZgbfRnYAAMAh2U73zCnv3Pwr1mWuY9OEHeZqHChuoRshmid14I4oXtDF7vJQDkWVkIhhyTu1pBjWYAQLACZSJTdUXmlWyrVkqxuUuuapf5DEGMmE452HD7b\\u002fPi2I+u1ERwZTZxwS7Lhtv4e6DA2h3xf7zISAnmbsEHL\\u002fpFF7r5RvPluwdny2m15BkfJCs7GebAWQMIPuj33WS9IgJQK634NxL7jX8gDglPwAqV5Z091974jse0dQPxgYjp2WHyJbAQBgevCiNN8eJwIUoa2NfsxTO2gQnJQfoNSFOyLui+V7jD17Yl7IclI\\u002fCYHRoT\\u002fTdZhs9cwtuWIhnFSFpZFN41M2Casa5W9+feTNb46+uuHg6xsPD2gsoQPHdDZTh0m1JnomE6V4ZletHutwYO4xLIrz8IWU7CjSPFMAgON43h\\u002f9d7XmUQWwPLUHSURwoxaHLV8QujSCO7B6yk0gt\\u002fsk5Y0OAHdE3JcmHE62DgUNXcrHpo99fu4kjYmcj4YDfqzzd5KRYGXkei6NQnf6RcwMXBK2lmwFcOCOYx29x9pfUSk3GDbkXfqu6LJLqV5KNUVqG0Vr0Y8hniNdSrb2BwvlLA5bQ7YOCgtmOiM\\u002fQrYCZIlGjxZPIlt9AEXQFRH3k60AAJDbfdL3bIlbcsUcuHVC0H1JwqlkBwDcQB4WADQaOpZFTPIyt0xnp6ahXt7Wqqyt7nS1D1KwEASZMIccQgIPtSdXakpb3RPlwUO+6P8W57uPkU0AAJAlGj1GMgUATpzuE8StqCLnfLpbvOPA8MKGNgeGOTDsWGmNtW9+Q7WulDLomyoclihII1t9ZpR4YjgnmmwFKFCecyZGLkxJHh8VxWMyRoRRZ7p46gSYFjSfgTLJVh\\u002fIEo12HXEyaPIV54wOPcmIADJHejvJ6DuhnMgYXiLZCqCxKX3PpbolV0xpaVFb2w12CqF0Mog8LADg0lhbG\\u002f\\u002fY1XKW7LjOwf3Fjz81+4WXFhUW1DvnrQdPgrVx9wWFhhxmqmnpOpTbGyynnML4Wl6dQesxqQMGmC\\u002f6P0S9oYpSHWgIfUnYXaXlbfsOFre0Kq+WtBw6VlJW0d6t0FfVdADAhbwaADiXW+O0nDhdsf\\u002fw1dLytooq2cGj10gy58rx8pqTlXX\\u002f2nvijb0nogP8mfQ++Rz5Cuq7YXrwQrJpgEyj2oMDd7h2YHmZXsaO2yq0V0lGAKAh9BzJFLLVNxBARrp1oXqhUN62paxwSxk59zWfqg8uRZgVyunTDTpQPNXOSrXUNVASt+qK+TFDZKaKZsMVssOFB5aNK65sPXmpekB5WNOCs9dEz8wUxZId1wkMEtZUddTWdABAe5uq+\\u002friMtSCteXAJYWGHBFs69J8uqP350wdES0JFrr4AQDsdselk72i5g5lFYxS+\\u002f63uKamnu9tpHiCPzPgWlnrovnDmCy6vEublR6elhIaIOETI5OsNkd5Zbvd7nBa2mXqhXOzKqtlTS0KFpMu4HuMNM1OT3x14bQ3l8z895KZK0b3qaJiuKOEKvwvZgbG8VPI1gGS6TeKcgh0kfKC83N2WGh1d7eQxXJPHG001FJm2ycJ0il36yOZopFkk2eqVd3r0kauS+uzicIqp0y8yvAbwJ4poQxjAUCNbzWsW3XFwrmZKX7TJSxv4Re5UldaKxs\\u002fPLaju6d+3S8V2qa9bRd2tZzd3XqO7LtOcLCwrKztyuWmkFD\\u002fvNza6sqexgdFuMsTHBZDa+gNlCAIMn5O5r4fzrsUAQDIPVo67TbqRLK2xu7mmj4tUgLK4T7\\u002fW1C+AAGAaAz6+3GPHC+1WuwAwOEwAaC5RVFbJ6+u7Rw\\u002fJv7lf\\u002f\\u002f+9mtLnBbO9Wmd+TxWU7MiPjaod3duyHUGonV3qqJuakqc095gqKG8xdP8sgeR\\u002fkOCgTKSBBnF6kske5upSWfXCOh+ANCq0aYFB0+Po3iFNhiqySYAAEgS3NBtIGWH8+lCysknSOyrr5Cb9J9fzePRGa6aVamleJsCQKrfjcYfpexwJspyH\\u002ffXaW6zYhYmyiLZSdySK+bj9DKDWIQiRRgVyBIFsPyajeQsGSdTri9zRaL\\u002fvTu5WNooFvJcLZQ1o8KzlVYz9ei53CMUN8Rfqj14akce2XQdLy6DXUc5RsyP4U8EL+bOypg1Pe2eNeNnTksT+\\u002fMAIDJC8s9n5yXGBzOZ9PffWs5k0p2W228bCQC33zayobF79MiY3Eu1pN26UlDfc9zStk67S1O\\u002f1kPSYyJ\\u002f8NErVxIF6WQTAA64MygzKyE+VCB4aM++vkUAAFqoOi4BIPbGBlQhgIRzo8lWKhbFpsyNSno8a+zk8BhXe7WOIl4hYoi9ZNX6CIqg\\u002flS9cjjglJEEErfkikk5yXGCsdnipaMCVpJ9LjDotNZOdYdC1yZX+6JWBB1m5a6WswWKSrLjOjXVHQBQWNBQmN\\u002fnb+9Tw9p\\u002fvmzT3p7H8smPdzNcYiImi02tN61bkOO0AEDaiGhxkJCUymAx2YrOVY+dSfFg5FKt8EypereKtpqOg9+ejEmPsNscsvrOyOQwgT+v+HR53LAotVyrkKlaq2U8Ea88ryYuKzJtbM\\u002ft0mpq7LOX68TzU53VGfckqX5ZsWxUu0y9YtlossMFFoO+53K5P4\\u002fTotS43i5tHk4pkktR5RkEkdze2pwrLcb6Ef7jAOB4bZ3V4fjqtkXkEgCUjygCCGWS14AIYUdUaq+RrW606jWbyvJT\\u002fYO6TIbnR0xy\\u002fjStpoa+BQEAAvuLTPuIp7ab2qaIgD666c4tuWJV2lNKSysdZdox69jAu8ju6wxiEQq43ktItrrwx9ESsZiff7HWZnMMHxHtXBO7j2DNHZsSHiS6WNq45cCllJhgP5eF5NgsRmZc6JwxfcIfCIpMmJOx78feyAVB7rESd8HyOCHyX6k9yOQw598\\u002fbceHBySh\\u002fhkTkkPjgquL6iUh\\u002fqUXqhhMxi\\u002fv73\\u002f0o7WbX\\u002fk1Oi28uqjBKVieRqJ6ilz4CJNJj+5vqNPjM8aeq27UW6yvLprmapdRTf\\u002fCQjkipgT6rmfVL61abbiQHKwMZlN3\\u002fzmFckY8taLhgHdZOshWAH+mpN+WUb9ImIFkExXhfL+7U7JT\\u002fIPoLstKWjGLex4\\u002fAASwgsmmQcHuOy+Qk35HyN+qK5YknHpNdSDTf0G55g\\u002fc8yIURrN1+azhA30fE72EHBrLk2xhGP7tN6fW3T\\u002f58KGrOIbD9bpTH8Gi09DhiWHDE8P2ny976LbxiZEe\\u002fxgnE+dluQtW\\u002fslyzIGR1sW+eJxiQuS\\u002fVHsQAKwm67Efz4oChQDA4bMBoDS3mi\\u002fioTQUx7GFD0w\\u002fsf1CbGakQW1MG9srRkprl\\u002fOzK1J2GNk01NTKFRXtcgeGNSvUD0\\u002ftrf+qbL2z7jgJYAUBQIdeT6xntaO0VG02S\\u002fl8BEH0VkuSJEDE4eQ1N6cGBflz2GcaG7NDQhGAH68W3z9iRKKkj3QyUZaQIXJ\\u002f2BQW6kvhRG\\u002fXUU4NRtliGiiEHPtCp1Gf39Gqs1qeGj6esHgacJ6nOJmnOEm2Dh3ugS0St\\u002fCKBbBjCxS\\u002fcmkiT2oFAFWN8rI6GZ1GwzDsgWXjyG4PSNniacHZZKsL994\\u002fWac1BQYJJ01OpjN6m3rUQffxmTFMl0JeSBsZ494q1KqM5ZebSHOQ5h2n6BD5S7UHAWDNy0twDHedn37p43NcLZEpYQBAkmONTeX87Iqnsc1DyLa84lEx4XwWs6y9t3ZgsOspJ+ESMPwAQMrnWx0OALA6HHwm02izaSzmB0eO+qqgYFxkRBCfX9DWhuH4Q6NGAYADw5IDAklqRSCkUwiW2qb08jYGAL3LyFtXiFD9DeJ7NuyMiHgA2FVb6pw6nTIt8ybgabZPJ7fwioVyUkM51MFvJ2qdiUmnIyhEh1LcJJ5oMnb6MfkcGkvMFJB9AADA47F4PBYARMf0qTZRB8levmdmdIiYbKUCQZHxsykySC+d6CNPJqPlai5F8JjUHqytoGir32TcV9Nwt5Aqj67D310hBMI7nU0UVSFPuBf247BHRIU1dKm6dAaboydx1FO\\u002fD48mAADnelYIgtBQBEFAb7XuLCsL4HIL29o0ZjMNQeLE4s1FRVc7Omgo2m00UI5h5lHNVOnA7cQQuZKOzm\\u002fyC\\u002faWk2P\\u002fRruBZCHwFOIZEFx6n04hLxxvqd1SVgiAONsyOjfxvTn0OwVuv1fMbG\\u002fr66FGbjhANg3kinkiKsSfyaDRUFTmc1oDAIRyAiq0TZdV1L2fXqCuYQ2IifMy9\\u002f9EbhUWna26758LnF8vn6u2WcnVWvf24N5tec++tczV8j+B1UFRq0cRGhFiqL3aVH6pJi4zyk\\u002fCLz5TEZcZRaOjzZXtyaPjTDrzvq+PL3tiTnRqT\\u002fT08slSWb08NF4q8OcRWzGYdE+FAeCeCdliHjdAwJWK+M7FqG241VnAFSbKBIA4sfj92XMAID2opwL43ZXLS1NTiUfXWeNw4DiRQvXc+AmUEQpPCdY2zAo0XoVcvn70qD1l5aTVXzxVKJgDHNhICQNhkE0eCOLwa9UK1xwxM0Ye2\\u002fQXwfsV01lL27Vbw4XrHLjRYK3hM1PsmMZkb+Ywohmon9ZyhcdMQQD01iqrg\\u002fy2g4FcMU\\u002fUNHVp9GYOixEa1P\\u002fr2YnRblZb9Qb7gK85tWDZ7I5tx4rOX63vUhlIgad979\\u002fv+hUA0kfF+gcKVF06V2NDpUwp14qDeoK1hacp+i+d7UGt2vjTlycCgoQAoFYadv9wXqc1LV499lphQ3uzIihEZDRY1jw8zdUlDhT8sum02WiNig+euTj7181n7DbH9IXDYpN8jSUPITaqW8o5DPvMrkvRqeHVlxvSchIkUlFpbtXwKak6tYHDY4dEB8ZmRLgKkKxeXnW5AaWhl0\\u002fKia0yxiVSFr7S3C7mcWVq7Zi4yIXD+nSG2KnagwBA8zAyfFFSslNTnB+cDzOlWgEAsYqBO8SKMokBAVsKiwJ5PNLmns5tSEatoz7vpFwpfygjZ3ddmVNPPZ3YLcfTiRFXjM9I4TGTeczEVu134cJ7WzSbaChfZylBgKayX+AxEvSWEgy3RPitb9F8Q95Ff1es3VTWaaoO5aQGe55xdERa5LaDBV0qLMGHkLeTsQFpAHCso8B1RRxfoD7db\\u002fbkfX8oPyU6ODM+BDzHIwiIVuGBrbkk+5Xz1dOXjiQ+F56hFKye9uDxfVdmLs4Oi5J89u+9IjFvwcqc0stNeSfLuzq185aPqrzaouzSAYCrKyYpJCUrks1lGvWWY3uKAoKEIRHiXzadeekDbzkjfxI41fwtzgHusRmRBo0xLSehNK+aL+KiNNRhx\\u002fwkgpILlVNuH6Ps1LRUyyISe3RWKdfMumti8aly51aeChsstoKGapla5xyRkx3VE+P3EkKiRMIdTHPM05QjxNGNNhuPyWxQkaN7Hts\\u002fAztlatwXufNEkn\\u002fAt2UFQRy+U08x6GlQ\\u002f9XwfsUQhGZ1dBtt9UxaYKd+D4sebLQ1SfnL1OZcPjPZjmmFrGyDrbpT\\u002f7sDp2wHeLtizullgjzPOLr31LV\\u002f3DuDRkM\\u002f3XZm3LAYH0fn5HWXtZsUAgbXXa0Gs5Dq0UuVK2dmP7tqCtnhgYnzstwFqzivlhCsxiqZ+2x\\u002fru1Bi8nK5bE4XCYNRU4eKFZ26yJiAh0OjMmi0xk0FodB1PJcXVmjY99\\u002f8bekzPBFq8b+\\u002ftOFoBARk8VYcf\\u002fkPse4WdCp5sdw9uxMXT6GCNInjohxxu\\u002fjh0XR6DQAWPev5a4xsrteWgIA6WMTwSW0T1l4fHyU1W4vb5PXd\\u002fWIglOw6H1XwXNip+psGjSUXVdw\\u002feiNKnVJR8ey9DQ3L8W1AgCHz\\u002fMWDAkiFnthTEp+Z4vT4qnCODVoforwT+waCuxvSrJ+r1is\\u002fzMAKJcRC4A5o9J+7BEAgIMDAZqAleHq8p0QTmq19kwgO9aTWgEAgiAdCh2PzTRZbO7q4wkpRzw2II0y030wC6kqNIactEiy1TMZo2P9AwSq6wMUCa5d7BmWdfk8RWjNtX9w\\u002fMz0n786GR4TyOIwaXRae7PSbLKRAtsA4OoyGy0alUHZpSu\\u002f0jRj4fCtG0+GhPsnpIUB3IImIR2hiObYcRuGO4gJ5p1\\u002fi1NuCAFytbjj3IqyMILAtJS4UJEwOYRcFWd5SM\\u002fptwd9QHjaGxG5ixT5AUBuU\\u002fPI8B4ZJfCkC54aPgPC951clss0VrOYxbFjGJGN5SkkJ2SIkgQU3Uo3DR+umPNJIT8yiDN\\u002fyc1F4P2K0RD6hKD7vC+keteCUb8du2Ky2FbOyfZRrzrMyt+aT8fxw5RW7f2x8yllbmALqUZK\\u002fVvl5DqRFxAUGTc74+C2PpUseZuqs1UZHC4uvtBP\\u002f2BkbOBz79yOIAhxWuOnp7pmXkTH92TuTZ6T4XR9\\u002fd7B1z9bI\\u002fDjfPTq7mffWvbMm0vtdge973QFNw1PXS06u9aP4U+2DinuagUAAg8LHBjsfd4oN4ieam80hE5kSAbxeROio9y7Fz31BnqfBd9HPGmoOwkiCYogjutqBde7UN0Z2os2CG7VFSPWJQxgxxrtqvFB6zzFGZralUT6VUmNr138Urb4tvCJcfxQ96ncnQxsIdWHloz\\u002f\\u002fmB+QXkz2eEZyoT1kkv1DgdWWlBPsrv3D6Io2iuinlPAnK4ZC4fv\\u002fvHCto0nb7tzbI\\u002frFqkVAHgaa+ZpPr8\\u002fGxbKpsyr9pQvNjgo8wD8GP4IIK0a7eaCou+KLu8uLSN123vKHhoSXTA4fN1JZoA0XRKcFdhbHxdRLXoCQ3RiN8KtumK+rEuIYfiVqlaHA3M4sJP5NTabr+36REG4F7UCzwupUtewTl+uxXH8kQ92ctlMiR\\u002fPtdWy8517e7+4kDE6ViThqxV6V2NZYUNEfJDJQFbxG88XjUsJjUsJJVtvESJGH\\u002fF10mFpi+LFk603hQCWtNXYQDIqrHLvWZ2+Y8HMlOsYi5mBABDuJ7wre1hKYCDNZewLAZ8uQBGa+0ybKpcFcQeN3kZxSj7iacpgymExN5NbeMWC2QkAIDfXBrGpb+NTBTXniurb5RoAJDkmmOG5qjFQPC1CQS1YRrN1WEKf0EO\\u002foDR0\\u002fJyMg9v6TGlACJarhYCyOva\\u002fi6cxqC3GhhzxrekHCONEuQuWDbMqLV0SFsUvMlA6TK1kEwAAOJfYSA+mHoKHIjQJM9BdBRTWLgfuoN3YmpJe1ivqFz+GmEcXuFdbmo31GI55700DgH3fnopJDcsYl1hZ1JA8IobsBuhuV53enX\\u002f7Y7PJDg90NiuCIyVw666YL9PLTM9JDAvyS46h\\u002fq1vhIEtpPrfRxeSTT4wcV4WSbBa6uTuM7i7twcB4Km9h9aMyCIFaJ2cqKmrVSgfHDOK7PhrEOZhgvlaXTnZdLOI5MZeUpwmWwGajXVDIliUa1sAgJfpSpwEsUPdHz8Hbu80t9\\u002fgxJ5yt916olDedq1LBgCu82FFcGIqddd6CwEAgBWztJubKWeFdlJX2lJ9pTFtTLxCpq692pQ8Iub07nyj3hyVFOon4Refq4rPiEgeGdvb1wUAACW51e0NXZGJIUw2vTy\\u002fLjY9gsFiNFe1p4yMNerN+749teyRmdEpYXCLrpiUk8xnBPDpAd6D7n+GWoHnhVT7eW9YbXal1ujLYugAkDE61k\\u002fMJxlLC8jv+UG0B6cnxP1l1QoAAllSPr0nRdYVmbml2\\u002fMNcYOUt8tf2nX05V3Hnvv10As7Dr++57irl3K+KgCoopryaRBU6UrIJgAASOCnkk1uRHAoKiDgeZo63\\u002fE0b5Q7lDOOxguoT55ynixX4tIj4jIi4tIjJCEiYkSHrKFr3tpJ1VcajTqzROpXerGGvA1AV5syY1xCyqjYM78X8oScmuImFEX0aiObx4pNi4hNCyfUCm7dFbNhZr1d0WXu6e6\\u002fmdTXy5fePmrN3RO6+2akexSsvJLGtf\\u002feNuGhz2Y\\u002f9dW12nYA+GZvXl5pI7mcC0SrkGx1w7U9+Pn5i8t\\u002f\\u002fOXxPQc69T3Brwd+27Mlv+jR3\\u002fff8dMvBqsVALZevrr0+5\\u002ffP32eKPBrcckn53q6Iz84ff63q6UA8EtxyV3bd67etoNwFbfLHtq179Hf96\\u002fc+usz+w73ifr+CSCAJAupG7mUc4QPCamhQe8smx0VIHp98fR3b58j9euzZkkQK4SyJlWuvUK5TOGAsGFWymc4iBXqyxQCMR6mnav2III+YnQYKKdRdMc54yhpTndPq\\u002fXldp+gnMjBnZaajvrSltprzWxeT2ZJ6cVavdqIomhTZXtDWWtdaZ\\u002faCofHBoDY9HCj3pI6Os5hdwgl\\u002fNK8GhodVcm1LTU9b7tbdcVkpooa7Vkcx9wjaH8qGIZfu9pCxPLPnql0jeVTNwlzSxqe\\u002fmRPTKhk6eTMXad7Ksk6g3nb0aKx6dF9ivZl4rzMQz\\u002f3aRWScG0PNihVp2rrd969CgeYu+kHZxkWnb5hSW+bdE12loDFrO7q6XFblJa85PufH58wFnD8WHXt3nvXNKvV+8srt65ejgDc\\u002fcuua7JOAKiUdx1bfw+TRlu1bUdttyIhgNwIHVoy\\u002fEYUKnsk1ZXz3X9MD1400BUAfUeu1Tcp1DwWs6FLiV0f\\u002fUcwwn+869JbBBqbqlJ3LVU4jGQfEMXqSxaqkXfD\\u002fXPIJipieUkMlOE+n0S5ttiX+YI9UaG96qMWL4pNqVJ1J\\u002fkH1Gn6dOOGsCOC2WGd5jZXIwB0WTqqdaXes7GWPDQDACISpM9+cS8AxGdGOo3ObOHnv1znusm05WOID1OWju5NEs6MotFRALj3lSXOtLtbdcUkrCgEUGc64U3j3NnKvAvVsnYVgiAJiVLXWD51Deur33Onj0rc\\u002fu+1\\u002f1w7w2lMi5XWtHS5lKIgMyfOvVXoimt7sEmlTgwKQBGEhiAJgb2CMsJDJIuAw2CMiYo4VVt\\u002forZ+QkwUh0Gv7VY2qdT3\\u002fLLr7l92AQBRL0sNDmTSaAAg4XIJy59Kut8IyhVGDXb90Y7dZOvQ8dj0cUWNbUdLqh+amkNap36MeAplh+CJzv1k00DAAT8pP0C2AoDntWFIMFFWItXDb8UsBVSi7yOFynNkkweIGUe3lBXurCklZV2MD5ju+tXJQdkO78+2F7zkBjtxSRLu+eC61S28Ys2Gy2orWcFvnLfLfyzVeGyNTp6S8vhTc559Yf4zz89buLhPtZdasOpau2fnJJMSUP34HI2e4r3qCkpDx82mjp4QuLYHI0R+1fJuDMcxHG9QqJx2T6NtnawenrXrWtme0vJVwzMBID5AHCoUbFmx9IeVyzbfsSQnMhygn0FSQw4dYeSIp5CtAABwSn6oTl9Jtg4RnRrd1RbZlOTYdpWW5JKwgjKolkWp1ZeXai6TrT6TrzxLOcNqqnCY7wvfZ\\u002fv3ZM+ROCHf52nEj3dk5hZP64C4Q8w4ujYl+x8jJ5NuttHiyZSJmk2G2j8695CtN5FbcsW6zHUjJXfggPnYIh5CEpOo7yXqp5rD7rNADkGnQifxo07pdsVLTJ3UPxgnEU+Iibrjp1+e3Xc40l\\u002fUW84FB4Y9t\\u002f\\u002fI9wVXjlXVPL3vUINSBQCJgRKjzWa02omGXqRItHJY5t3bd979y657f91t8a2LYMiZFryQsnKO4Y4tDR97DxYMmn3FFUuz07RmC2kRCoI50mWUlawdLd96msPLO0pr157WrWQrAADMkS4jmzwzTJRD2U2hsMj\\u002f6NxLtvrA760\\u002fDeihsjl6c9xd4dC4M4IXk60AAHC0Y3e5tphsvVnckismYUUXKXehCM39LnqlZNPOltNvlH335OVPjQ4LAGxrOvZM8RfPFH\\u002fxW8sposyn1b89X7zh8csf\\u002f9BwmLD81Hj0icufvFn2vcLSz+3n0yIUTsZlxHx34NLwxLDwIBFh0ejN249fnjQ8tk85KjLHxAn9eVoVxbgBdy17ZvL4Z2C8q2XT8ttcv9JQ9IOFc1wtBD+s7PN4LM1IXZrR28UzLDTkiyULiM\\u002fOD382ArpwWtCCIx27yA4AvV37Wc2\\u002f74l+8kaWXAYAHHA7Zme4jG1m0GjNSg2PxWhR9VmEgiCMEzUuYNqF7hMku8am+qbu\\u002fUfiX6KsTXhCZ9d+Xfee+8LIADDcf+yAUmTpCGNi4KzDsp1kB8DRjt3RvITkgSxgtadtq6deS0\\u002fUahRiNofHYAZyyO\\u002fgyYFzLylOyy0ykt2BOzbVf7Ai4n5i6bYboc3UlK88syRsLdnhmVtyxXDAMdxux6gjKkyU8XravcTnMk1Dmabhw2GPAsDL177J8ItNFkY9Gr+UjtIwHFtz8c21MXNajV0XFWVfZD+NA35\\u002fwX\\u002f77MsNT4tQkG9xgsdvn2h3YLe\\u002f9P1db2wFgE9+PbP0xS1Wm+PB28aRi7pB89wq\\u002fD+WL+rOLOltIewIshUAAAx23Ze1b+9o2TyIqg2GYzX6st9atrxa8nCTsU8H+f2TRzkwrF2te3RaTwSXxMLQ1X5UI4eajXWfVL\\u002fue72v1djwSfVrlOU5NO7SgTx7BFOD5lFWGTAc21z\\u002fUYmmT\\u002f+dJxy4fU\\u002fb1lPyg2RHf0QJRFe62s+3N5IdAAyUsSbqEcqQAoY7tjd\\u002f\\u002fWPj54PLVpFb2k\\u002fLD71X+c\\u002f3Kv95Wt5T6fCdm3\\u002fFFJbGUZIVAEBZF0v3i3F+bjJ2JAoiEUAQQBIEEfWGditm\\u002f6J217sVP31cvUNvN2E43m7qjuGFIAiCImgUj7rF54RYhGL5ihx\\u002fMQ\\u002fHeo9OXcMK9Odv\\u002fdeabUeLLpY1hQQIAWDplMy75owUuqyj44Un31n+5DvLyVYAAGhv6vYPEHCud\\u002foCgM1qV3XrgkL9XUqBzWovPF0RnRQaEjXEvXsHa95zfnYexfnV\\u002fUwGBA2hr41+\\u002fJPq1yg70XDAL3Qfv6Q4M9x\\u002fzDBRThw\\u002fxUsFR2fXdprbGgzVdfqKBkO12WEilwAAgOqOrrvGDQeAK83toSKKu5lD466Leeqzmn+7Rzo6zK3vV740IXDG5MC5xJAaSrosHafkB\\u002fMUpyj7thFA1kQ9KvQw3NoLLJSzOOzObU0byQ4AK2bZXP\\u002fRaPGkOSHLPJ2YA3eUaor2tW8fnHZw6IxOo17KFZAdAAAQxYu\\u002fLWzN7tYfyQ4AAChS5V5RX8r2H5suHJEoSKOcKprA6NDLzbIOc2utvqJaV3qDYzlv\\u002fhVjotxCxQ4rZixW7kkSTuXSRa5e1\\u002fBfDC\\u002f0XNc1QteqdM05ktRiVY3OZnw17R6dzXhKfgUAQjiSBoOMmCqqlWpiGVcGtggFAAh57IeXjn94aZ\\u002f22o2Td6xkxOTk6Ovz1QGAxWRTdZFlgk6noXTapRNlt62b5Gp359yhq+NnZ7jPReMLpKNQnslACeVE3BPz5Kb69z11KtlxW4HyXIHyHAKIhBUsYQby6UImysQAtzhMZsxksOu6LB2eFMoVB4YXNrRlRYQAwLHSmrTQYOdkfq5E8xJWRNy3vfkb9\\u002fekHbedlh8+Iz8SyY2N46cEsUMFdCEdZdgxm86u6TC31eor3If4uDI3ZLmn9KV+GS2edFWdX6opIjsAcMAvKc8UqM7F8pITBKmBrBAOjevA7Xq7Tm\\u002fXNhlqq3Vl7ktbTwiYqbJ2l2mvkOzuuM84SmJy4FyltctTPQjDHYXK84XK8wgggawQAcOPR+dzaXwHbjc7TFbMYnIYFVa5+0CfG+QmXzEBI0hr6\\u002fRjhKSJZpN9fUkRRmWK4p4t3gA4PkqSkiqM1tgMPzf\\u002f8XLJNxKmXywvBAAiucEjxElPXPk0hC0O4QSQd9EXpUK\\u002f45eLdptj0tQU13UoPArWDbLvx\\u002fMzl43i8Fi\\u002ffnli4drxOzaesNsc05aMBIDjOwvMRuvE+cOyxsbbbY49350NiwlMyorEMfzHjw9bzbbMMfE509MCpX6yxm4AMOrNzs1j+w54rilp3bXpdFtDV0ZOXGR88P6fzht15uTh0emjY3d\\u002fe1qnNi6+Z+K1S3WUZxKbEoqgiPMormdy9kCxrFnR1a6aOH9YTHLIr18eNxmsUYnSxfdMdD26J1KFw9bFPP19w2eeZuMmwAHvtnT4\\u002frpz53h5zcnKuhaVBgFIC6NWK4IcyRQrZtnZ+j3ZAQAAOOBNxromYx3Z0R8zghfNli4hWwfCmqhHvLRMMRyr1ZfX6n0a4RTJjVsSftfxzn2eHj9X3GccdWdJ2Fo6wjjeuY\\u002fscAEHXG5pl1t8nVnlxrmZV4xoEpapj+JuY+bfynjA9SsArIqcsSpyhvOrH4P3yfAnXPwAAOti5ve3aGwPhw4UP\\u002fXsXBoN3bjheM6YeOcMM9QVk7PFdQ6MuoLgIzFJIWcPFNeWtrK5zGO\\u002f5UuC\\u002fYaNT\\u002fx1w3EAGDE5ef0ri0\\u002f+XggAdAZt8sLhXe0qACg4XREWHfjAy4tzpveJTJM2dyUhIzw6Sbr8oWlpI2PsNkdrfdf9Ly2aMDdTJOEvuGt8Zk5c3h+lXs7EFdcz6ZKpErMiiJOsuNyYkh09bla6f4DHmr87GX4jH4l\\u002f6c+eDGt2euKrC6e9uWTmv5fMXDG6n\\u002fjgxMDZd0U96mk2uIGCALIobPXC0FVkxwDh0LgPxf3D93wIT4SwIx6K+ycdYXgf9OfEgeMoIN0miq4hVxaGrloRcb9rL8ct52ZeMaJJqLHJipV7jHY12f1ngiAg79Rq1Eazqc9EptSC9exne+c9881nO842ypRkn2+kj44tL2o4d+jq1MXZRr2Zy2czWfQVj0wHAP9AAZPNwFwCaQRGvVnoT+6yIeyum5NwFf4AqR\\u002fx4eSeonMHi7lCjsOOeTkTLzhPMnNs\\u002fMk9l+sr2kdNSyEX8kocP\\u002fmF5P8ME+WQHUMKg0776tSlDSfyNp66RPa5MVI84YnE14NYfWqpg0DEED8U\\u002f8\\u002fpQQvJjkHhzwx4MuH1AXUykkjgpz2e8CqRuOucLsI7lGMJKRkXMP2ZxLciuf33jw8IyqC+j9y0KyZihgKAmBk5XLyEFMD6s1mxeuyxI9d++Tlv2fJRrpVg6qv21QvLx2XE7Dp1dfnL39\\u002f3zi97z5UaLd5aN+4gCBISFWA2WoX+vBlLR127WFtW2ECsJeFKe1P30V8vlVyqyz9ZPmpq6ul9l3\\u002f66MjF42Wt9fI\\u002fdhUUX6guOlvpZXMAiM8I3\\u002fzu\\u002fvKiRlcjnU5rb+yuLWlBaYiXM3E9iuuZuO7KbLBqlHqlXFte6C2UQwmfLrw35qnHE16L5SWRfQMHASSOn0IarLctrzhc7JcWFtzv8nYEUdy4F5L\\u002fM1u6ZHCjhRgoc0rQvBdTPhhQJ3q\\u002fCBmiJxP+NUu6ZKAVQAbKmBty+yPxLzoj3\\u002f7MAMohByT4DKb7WEJPhHIin0l6a1Xkg5TDMwdKIEu6MHTlG2kbyA4AAOjq1m3fnU+2unFzrtgtTBxVKvTd3Tq1ytDS3KfOhLgvH+\\u002fEbLWfLKw+kFteVNHCYtKnj0xcNDF9eKK3cTNe8HEKY0\\u002fFPNkBwGa1M5jkYJzd5vA0c6mXXbnz9Zt7Vj02UyDifvzCL8+8P\\u002fgWUIux4aLiVImmSDPAFYZpCC2CG5ssyBwtnuT+wHx09PyqnKwjJdVNSvXLC6Y4lybsF71de777j0uKM0prP8OtCCSsoFH+E8cHzBhEh6DvKK1dxzv3FiovUHazusJEWaPFk6YFL3BfXntD7duksdmLw+6cFrTA1TI4cMBL1IUFqnMV2qvEamY+giK0KG5coiA9RZgVw6MYydzarrp0uSEtOTQ5XvrL7wUrl4yqqZeXVrTFxwb5CTmXrzUnxAbZbI62DnV0uCQtubeO\\u002fKdeMZmpot1UzqeLk4RTXb03gS8+Pbb+4WkMBv2zj4889uQsZx6WN8Fy0qXSnyisPllUe6W6tWDLM2T3\\u002f2nqytrOH75Ko9PGzkyPSxukWDvBAW83NTcZaltMDQqLXGXt1tt1Ntxqx2x0lM5AWEwai41yxMwACSsogBkcyomK5sVTJtATKA1GMY+7v7iCTkPnZgy4HocD3mZsrNKXthjr5WaZxqayYCY7ZmeiLBaN7c8MCGRJI7lx8fwULw2HIceCmau0JdX6MpmpucvSaXIYbJiVgTI5NG4ASxrCDk8SZCQJMwZXSRwSrJilwVDdaKhpN7UorV1qm8LisNhwCwIoC2WzaGwWyhYxxUGskCBWaDA7NIoXx0IpJq12sm3XpTuX9UQPCMH6+oezMVEBWp0pPTmsW6lvaVNKxPy0pJCwEIrA6F\\u002f\\u002fig2UX7dfTE8PZ7Lohw4UL719FIfLDAgQgC+ChWH4xbLGIxcrL1xrMFlsud88SS7xN\\u002f93qdbVNhgaEwTxsbxosu\\u002f\\u002fJ05X1T+8da\\u002fz6ycrF8xOS3Dxe2TK+5u69YbSN54iO\\u002fpy\\u002flJtm0ydmRrGZjN+3pV\\u002fx+KRTa0KvcGSHC+9Vt4q4LO1OrO\\u002fiDsyK0pMFef9v8fpk+VyeW8IKDzcf9yERPCe1lDfpth\\u002foexwXoVCY0iICLx\\u002f0Zi5YwcWeP6bR9duuufhqaPGDj4+6iPna5omJAy4ElShkm8oy30kbWyXyTA5lCKu3Gxs4dP5DfrGaG7kjcSJ\\u002f9fJigj5fNVCldG0\\u002f2plQSN1SsGNMCEnHsNwovP+5afnAUBCbBBhSU6QekoW++vz5nO\\u002fNtbJN\\u002f\\u002f+ONnRH1Om9Y60c4VasH47Wbz\\u002ffFlFY6eAy5qdk7xoYnpKdDC50ADBcavafJrLSGLRB\\u002fxcWW32Sfd+RrYCAEBmYug3r60kW\\u002f8\\u002foEWpPlVZ7\\u002fx6sa7FXbCuVrX9fKiopLZdozdz2czk6KBlM4dNGdmrnr83lC6PzdRaLSVK2XhptPt44AR+3EVlQSQ34v9ntQIAfy5nRmo8ADR0q\\u002f4MwQIA9zWTe5OP\\u002fjfV6s+AWrDe33ZyZHLkm+vnThuRwGRQl1EaD\\u002fpz57is1NgPCEJHgKY2nQgWrCP7\\u002fu9CuUjkkFDdoYgUi6R+PX097SpyF+qRCxX\\u002f\\u002fuqIs\\u002fdQZzAXlDUXlDXfv3Ts\\u002fUt75iphoGiTXsWnM5v1ane1AgALZvVjCDU2DdnxN39zK6AWoz3\\u002fvS80oCeniRKDtaRDu8libxSwRhttVVxGitleT0fFXGZap24LAB7IW8VmkJoYKJMWYrE3AYDNoejUfWvH1MGCu022Wou92Wpv8+cu4DEzZNovcbBJuEu5zFvT\\u002fPxta57RYJG1Kq9dbsJw\\u002fJvtDwv9ODWVsg0fHJF3aARC9rpHp+eMTwCAI\\u002fuu7Pgx12y2MZm0NfdPnnF9aHd5Seun7x4wGqzDR8UQ07CtnPvRj3ufMBmsd8z54K2PV48aF\\u002f\\u002fIXd\\u002f8+6NVhXm1pD1cOl+9bcu5z7bcR+zq8\\u002fcOifx5dz0w+frZ9TIxMZpOQ53v3hU5ffIMDCbre9+doMx12Px73syxyVEh\\u002fgDwcNrY3+qutRm0T2ZMIJcDAIA2U\\u002fsc6Uyy9ToWu33Se5sQgLMvrCfl2e++XPby78fuHT\\u002fihTm9g6s6tPoNJ\\u002fPO1TQqDaYAPndGavwjU8aIuL2R4BaVZu+V8rPVDa0qrd5iDeBzx8VHPT5tbLCwT787EVHadPfSEZGhnxy\\u002fcLSshtjh4mGpT87of3w+ibL2zo2nL11uajdYrRH+fguzUu4dP8LLsAEvnKtp\\u002fPLUxcqOLg6DMTEh+plZE4hZJP9mqKAWLKdaWW12vckq4LIYfX8\\u002fHjODy0ySCh9EgK42ncQwI4ZbLNBssF4L4q9h0kOblC9Hiz3OIMGgSYL4d+ks+SrjHyjC4jEzgwXrmpSvmmwVTJqUxYiSaTfEBXxB3uxmcWBX4X++WPPPN5dq1EahH8dktL76zPbnX188IieuvUX5zPrvP\\u002fj67vBISebwqHGTk4V+nObG7qfu20IIFobh776y6\\u002f7HZ06ekVpe0vr0A98BQHxySENNp7xTOyInrqS4efioGIPeEhAocN\\u002fD6PGJX3\\u002f6R02lLCE5xGZ1nD1evuFH8hgIAuKJqpJ1fX0mf\\u002f3k0d06Q0xAb\\u002f\\u002fRheJ6o5m63x3H4eSl6ntvywEAPoN1b\\u002fIocgkX2DT23rYDLBqLUrZYdPrCzORtl4pPVtbNSe\\u002fTW3+opAoAFg3rfevUdSnXbt6hNprHxUWG+\\u002ftVdXb\\u002flHflbHXDL+tXOTXr+wtFP1+6mhwSOCEhisNgFDS27ioqLWho3ff4XSw6+V6VqbX3nMhVG82TEmMwHL\\u002fS3G6yDSxbEABOVNQ99esBBo02NSnWn8spaev45PiFczWNm+9Z6n5E75yoqHti+34mnTYjJV7AZl2sb1696Ven919Pbxf580aNT\\u002fj+yxPtLUppmP+a9ZOnzu19zTy8YuOUOekjxsRv\\u002fOBwTVk7k02ft3TkuidmEN5uuXbLZ8cLzteYTNaY+KC1j0wbNd6nqD8BjuH7duQf3XO5tVEBCARK\\u002feYuyb59bc9IYS+HNhmte3+5dP54eVuzwm5zhEVJlt89fvr8LOeeG2o6f9hwoqKk1aC3iMS81KyIex+bERLeeysyGLSLZ6q+33CitUnhL+FPnZOx9uGpnlKO+sXjT5JX0rjx9wuVTZ04Dl+9sHxEcsQ3e\\u002fMy4kJc5nR3NrC5OGAIggJOd+B6GspHgI55HUanMPxuc3SxGXE4OACAQQtEETYA5sD0THoYCqwQ4SPkbQZIu1IbKhaSrb4xbGR0XKIUAPxEXACormjn8dkjcuIAIDRCPGxUTGFeXXikpKWpe+e2ixiGIwjodWaHA6PR0I52tVplnDwjFQBSM8LDwsUAkJIeVlPV0VQvX7Iy55cfLtTXdsYnhwAA5R6Wrhqzb2fBs68syj1TmZgaGnQ9fZ8S1wn8xsRFOqfEqm9V9C3Yh\\u002fq27lJlx\\u002fdVhQggFoedhiBsOuPdnLnkcgBjJaMBoNHQRHZcZ\\u002fnIjG2Xin+\\u002fUuYqWEqD6WJ9S5I0IFnaO2z1hZ2HVUbTN2uXTojvibV9fjLvy1MXPzh27q3betTw4Sk5a8YMdyqv3YHd893Ooqa2U5X1JEEEgE+P505LiXt94XTn3NBmG3lGCu8oDaYXdx\\u002flMZm\\u002frF8Zff2gxFl9ejzXtW7YL3YH9sb+EwgCP6xbnhkuBQAHhj\\u002f6894zVQ3O07t4tqqipGXFvRM4XNbRvZf\\u002f89Iuf4lg2OgY506K8xuO7StecPvIlesmtrco\\u002ffx7JvPQaUxP37OZTkcfeHqWwI9z6kjJq09se+vzNSPH+dqZ88Hre04cvDpr8fCV6yYymPSq0raA4D5Ph6dDM5i0wgs1oyYkrEqahAO+84cL77\\u002f2e1RcEHED26z2Fx74Pi5J+sTLCxlMeltTd2FurVDUJ4FDIdd9+tb+FesmhIaLL1+s+\\u002fW7czwBa0Xfxbt8h1qwfFmEgsvMaFG9K+bOY9EjbY4OJj3aZKsO4K1oVX+AIhwxd55zbwRmW323YafF3sRmxCMIw2xvxHAj0jfVPoC3rE3ziZUeyWMNJpG6rKWzrlORFRVisFh\\u002fvnD1nskjjBZrTYciOTSwWaE2mK3xUsmw6P4HpnC4ffKeEIQi+UOjNr75z50bt66PiA5Qqwx3zP6QVICAaBKmpIfnnq2qrep48MlZP3x9uqqsPSU9zNMeZs7P2v7dOb3OfHR\\u002f8fylI\\u002frszg1PE\\u002fiZzN5eGCazLV0s\\u002fWDsgi\\u002fL8u5KzObSmV+W5uLOV9B16vT1DYYmO25vNrSsj6OOPCZJA9LDgi\\u002fUNsl1+iBBT8PtaFm1A8MWD+vt6ClsbCtvl89MjXeqFQA8MHHUlvOFB69Vvr5wGpHyGsDnBfB7u+3pNHTxsJSipraGbpXT6MSBY\\u002f+YM8l1Jnu2h3irJ\\u002fZcKdeZLU9MH+dUKwB4ZErOb4Ul2\\u002fOvPjZtLJfpax55Xn1zl84wPSWOUCsAoKHIY1PHnqlqcJZRKfQffHtveHQAAIybknz3wk9+\\u002fvaMq2BdK2z86rdHIqIDnBaCXVtzFXLt5j2Ph4SLAWDM5KSmOvkPG074KFhVpW3HDxSvfmDy3Y9MIyxjpyT3LeLx0HQ67YPNvT99YlrYXXM\\u002fulbYSAhWt1yn1RinL8gaTwxcm5Cw5M6e8KgTrcb4n6\\u002fvHj46FgBGT0wsK26+dLZ6iAWLWITi7QfnIwg4BSstVnosv8pZJoh\\u002fJ45bEYTJZzkfqvkAEC1+GwfMPRjPZsRG+r\\u002fm\\u002fOrPmY30HVUQI\\u002fkQAGIk7+O4HUGoT8w7KIpoTRYOixEu8UsKDYiXSn48U7R28ogtpwoBYN3UkVtOFfgiWCQSU0LNRmtBXu2osfHtLcor+fV3rptoMloRBMQBAgDYv7N3hEdwiJ\\u002fQj3PmePnkGak1lbK2ZgUAJKeHbd18ls1m0Bm0xJSQ03+UrXtkmqc9sFj0mfOy9vya31gnHzORXK0gcf\\u002fkUb8XlblP4MfleHvSBNfnNes06hq1Sh6DWa9VkBbdAYA4fqyY6e\\u002fP9G83dbjaSdw+Iv1f+07suVK+ftJownLwWhUNRRZk9j4V+Q0tADAyqk\\u002fmLZtBDxUJ67uUTQp1fJDE1eUkUMADACvVtNeZ4VIOk\\u002fFBwTmzwz4mJHJ4UMi3JYVqs+nu9OxLspYmjTpMINRZrU+NGKe3Wr8svmTDHEsTUlMkvXnehU2tADAmNqJ3pwA0FB0VHX6opOpaq2xMbKSrywtl7Z0AkB3Z5w9MCQmi01Dn285fwifUCgAQFBkxNv7M0dLe0gDRCUHukgEARXl1MYnBhFoBAIIgacOiDu0qtFrtTLcxHu5cvlgHAHOXZJMdLng6NIkgqR+LxdBqjMRXaZgoc0T0528fqCptm7VoWCJVZjWHxyLUiiAsKqDiWouLf2BQ\\u002f7V1rd33LRxD6uByX4QCQZiuX6+DuKuVOyS1cmVwagUAdgfmz+MU1bfNHZbUrTU2yJUBQv7+oopgP369XLmnoEzM76noDgg2h\\u002fHvj1Zt+ODwx28f4PFZT744n\\u002fhpF94+6oGVG7lc5qyFw0KvN9ppNPSlt5Z98u6BzV8czx4dO3p8AgBweSy9zjx+SjIApA+POrK\\u002fOCElhMmkU+4BABbdMeqBFRvnLx3hHJHgidzaJmICPxJx4d5uvuSYnof2qcyJuxtKTHbbo+njSWpF4M\\u002f0BwCb1wb+gszk\\u002fx45+\\u002ft1werQ6i83t42PiyK0hkCm0QHAu4fPvHv4jNPoRGe2EB8cGH6ktPpERW2NXKEymoxWm8VzKy\\u002fET3i6pSHGz39ZYjphuSt1WH5H6x+NtTKDbnVK1hV5u9FmBYAd1SVSHj9KKNpw5eIXMxY59yDXGuC6JrpCWDo0epLdC0qDCQAC+t5gNBQRslkaU89TI\\u002fDr01YS+nEMerPrwDLi7eWORmXobFfPHv46yW7QmZkS6mGArmjURgCQBHmLkHg6NI7jR\\u002fdeOXO0tKWxW6cx2ax2h8vSAQiCvPvV2iO\\u002fXz7wW8H+HfkxCcHrnpg5ekKf4Jq47xkiCLhPfOA71NJwI4tQ3EIyIqWp4UE0FAWAJ+eNRxEkJkhMJN1tPXdl0chUX\\u002fJZlq8h12kBID5J+vGme0nG9U\\u002fOXP9kT\\u002fDljrt6O6fSsiI2\\u002ffKw8yvBN9sfIj5Mm50+7foU0p72wGYzrBbbnEUUSkTiarNsFlXK9bhhMTwO02CiiLtz2IzZ1xOA\\u002fVmc+5JHA8DJttp4P3Id56DsCDEZRrOx9aG4+0heJzwWc05a4u9Xyi43t2dHhh4uqcJxWDy8tz0I0DN2dmpybLSkV5edBAn5AGBzOO7\\u002fYXd+Q2tckGRqUmyE2E\\u002fAZlXI5JvOFpA3AAAAGororRZ\\u002fdo9G\\u002fF5T3mUyxInEDhxj0+gMlMahM4jj6q3WML6QRaM\\u002fMrxPPdTT7UBs5cnrDbdNiLuRQK\\u002ft80xp1Ea+gO0+DNYdgR9H4Md58Jk5ZLuwjwJ6QiBkA4CqW0+KW\\u002fnC5k\\u002f\\u002f2L01b\\u002fUDk9c9MUMSKGBzmEsnvetagE6nLVg+asHyUTUV7T9uPPXaE9s+3LIubVhvzdSXVc58h\\u002fpi3cgiFIOgqr4zKfZGE1MJnPeHU5uID\\u002fOGJ\\u002fmiVn8FcBx2bssbMSbOtc7lCT6b9cKOw0Q21jOze1MTuGzmP9bN+NfGw6S3GZNBf+PheSIhp92g7TLrG3WqdoMWAC7Jm6eFxbuWBICxkhwx0x8A2kztJBeJ20em\\u002f36lbM+V8uzI0COl1TwWc0ZKn70ReQljYiPXjvWowrsvl+U3tI6Li\\u002fpm7RJnZMpq91jDAoCpkbGvnP+jWN6eEShloGijRmW02dxzXJclpn1SlBsp8MsMlIKLLAcL+aVtnZ1afbh\\u002fn56NLp2B8LoavePP5QCAUt\\u002fTViLAcNxZvQIAZbeupbGbqJ5jGHb5Yl18Skhvac+MHBt\\u002fYGdhdEKQ0G8wTYSskTEAp47tu7KaKj\\u002fGOxfPVKUNj1zz4BTia01Fu+sM664kpIS+9sGKhWPfKitudhWsoYVasB6\\u002ffeJ97\\u002fxy+0vfJ0QEAMAnv55p79YKeWz3RShO5lVPyUlwT9IdEL8dvvLKo+S3x9AyuMbgzefU0dKNHx0NDhW9\\u002ft\\u002flZB8Vq8dk2ajiOwAwa2yyVCLcdrDwWk271mAW8tij0yPvXpQTGy4BgAAOj8dgnmqrWxidCgAqC3nyXAAg1AoAwjj9BP6yI0PjAsVHy2oemDSqpK1jyfA0Uvx7dEz4l6fgUn2LF8Gq7uwGgHmZSa5x9MqO7t4SbgiYrE+nLbBjPWt2zY5JYKC94YgkcU+7OIwvfH\\u002fyHGcxJzkxEScq6i7Vt4xwCa45MKygoZVFp6eHSV3K9kNaaBAAXG5uv2d8bz9JrVxhtTucfw5fwH718W2r7psoFHGP7Lnc2a5+5vXbnIW9cPva8WeOlT1333dLVo8JlPpp1caq0ja+kH3XQ1PJRalIz44aPy3lx69OKbp02TlxKB1trpdjDnzV\\u002ff13g8YkSgsv1Jw5WhocKmqsk+\\u002f5+SLfpVpXmFt7aFfhiLHxwaEim9V+7ng5AkjWyBiXHQwx1ILlZREKvdGy6ZcLKILMmZwKANv3F7bKVMNSw6PCxFv35OM4LJqRERkqPpFb1dGl7ezWLZ6Z2diqcH6WBgp\\u002f+j3fbnfMmZQaKOFv3pEbKObfkNrdXDaXFu2sKZ0Xnfj4cHLL8bGT++9OHT5KGk6y+4hzzzuOPkv2ecaP0xM+pyQzMTQzsTdk4woTpTGZtEfTxxFP+J0JHnXER5aNSH\\u002fvyNmvTl\\u002fC8T7pVwQ5MREZYcEnK+v2Xa1YlNXrNVisLSoNkf0gFQoAoFWpcXorZPJf8q86v3rCKUOuauWOeyr\\u002fbcNTN56+9EPelXkZSc6Owo2nL3XrDatGZ\\u002fFZlCFaasbGRUr43JOV9ddaO4iOQrsD++JknmuZ0EjJ0jVjt359uqNVJQ33\\u002f+c7y1y7CL0g8ON8+tMDP208tfWb06puvcCPE5soXeYSQ+iXl9+7Y9ePuccPXj269wqNhkrDREvduvMoefj5OV\\u002fYHZ+9vd9mdSSkhjz7xm2\\u002fbD7n9EpDRWaT9YcNJ3RaM0\\u002fAio4LeuPT1UnpFKH3oYKiw947Mrlm847ch+6cGODPB4B3Nx57Yf0MGg399tfceVPSggME731z\\u002fMWHZ\\u002f28rzA9MSQ5LvjDb09GhYmdn+OiAmgoGiYVHTxVmp4QkpUSHh4iev+b4288NZ98JBcGMZbwmqzTYLXmRIZb7A7OAHu7vbO1olhlNg25YIHnPf95lCo70sXSc7IGHPBJITfU3lcZTZPf2wQAAXzuiWfvd29\\u002ft6g092zZ2a7WZoZLk6SBxOpkxS3tc9IT3106GwBaVZpFX\\u002fxkczgWZCaH+Akau1XHymsXZaX8fqXsock5rinsRKb76pysVxdM6z3AoMira35k214EQaYlE4mjnVdbZGmhwT+su513XbDsDux8baPeYtWZLYdLqgsaWxcPS82OCuWzmCIue1xcT6LGsbKap349wKLTZ6TEsxj0K83tNruDzaTXdylL33jqX09vl7Uqv\\u002f7t0d5j\\u002f5kUKK+1mmRLwrwtHlGmqU7z66cbmuCj6s1zpVNShHFkBwAAFKpKdrQc4tE4yyPmpQrJgQVP+H50Vwb8JIcE+a1fNWHbnoKx2TGjs6Kd96XZYuNymDQaar\\u002feQmEx6Qw6nfjq\\u002fGwwWqWBQhaDtnbJ6AtF9VwOg8tm3mCL0p1PzuVebpMBwKiIsAd27tm66nZyCd\\u002f4\\u002fEreubZGAJgRFb8+YxTZDQAAn1zOPd3aEMITeJog\\u002fK1Lp812W2Fn+5zohEMNVf8aO31caORL54\\u002fVa1Qmu21SePSzI3pjT678XHl1X10lhmM5IRGeytwguxtKAzn8U+11NodjvDSa5hb98X16GX8uZ3pK3JHS6kXDUtzVCgAi\\u002fP12P3LnlvNFxytq9xVXMGhokJC\\u002fKCvl9pEZRIFwf7\\u002fv7ln2yfELJyrqbA5HXKDknaWzFmamnKis67unoWRsXOSOB1dvOJV3obbJYLGF+QsfnTrmvgkjOS4ZWDqLxXVuGQDYW1y+t7gcAARsVv7LjxDGWWkJG++87cvTF\\u002f8or2Uz6JMSo5+bPXHDyYv1XUqiwADrBjfEKHHmKOgnmXF7y\\u002f63\\u002fAZQnffEafnFdTG3Jwuo5cwTgzv6gAWrvrn79KUam91BXP3k2OANW89OHZO4YHrGN9vPs9mMaWN7VHP\\u002fiRKAksk5Ca0daufnxJigLb\\u002flhQb7JcdJp4xJ\\u002fG7nxchQMZvlMcVhcFxqad2++o61v+yio72j7QZKYWdbYWfbrwtWAcA9R3aOCg4fHkQOkdZrlCea6\\u002fYuXoMDzNi5heR1MiEsKsZPrLWaXxsz7Wxb47jQyDfGTWegNAeOj\\u002f\\u002fl62dGTHA\\u002fxSatem9dxS\\u002fzVyIAqw\\u002ftuNrVkRU4gJCKj2A4\\u002fl7x6eeyJu2ou4bhOM3tPAY0vQyxtWu+KAk\\u002fDvvpmeOfnjne1ai3yZyfsyJCvruX\\u002fHa59BK5yzUytL7izaeJz13m8kB27xENdnm97niG\\u002f2qnpV8SgiWfrFxAtrrgz+U4D+edyUkxk5P6tPLeWDzjjcUzXC1Ovq7b3m7utDisw0SpKyMXAMCu1iPXNJU4jgezAx+NX0Np2dl6+Kq6AgBGijMXh84AgDJN9S8tB2gIzYxZ\\u002fpX6JJvGOtxx5pT8YqZf0pqo2yiP1Wxs3916tE7f\\u002fE7FlwDwYsrDCCB\\u002fdJ4\\u002f312I4ViaMJE4nx0tB6+oyyVMkdqmI\\u002fZDosPctb15f4W2Tm3TcmmcJxPu5dE57n8X6QzlFoX70d3\\u002frncqvszwS6rU1ams2ldTH+PQ2DAIwYqNDIgI9UcRhEgRum1Wls3mYDBoAPD8+pk43jOnDwDcMT87LFhEo6E\\u002f7yt0fgaAlx6ZbXdgRFr2q4\\u002fNRZChn9GAhqDEy8yB4w4P6wP2S41KkREoJU4tIyC4UtnlLliNWnWyOJDQxET\\u002fnhCvO2I2V20xs2g0Fo1mcdgtDvu\\u002f8k4abFYWja61WhxuwWAAqFErGrWqOw\\u002ftIL4abBQJCjfOs1mT1BZTCFc4NyKJMgDk+\\u002fQySoPpRGXdiKgw1yGNJKo1ByyYlksPFDBC1ZZ6MSsBAKlQ70r3X2nDTYTFjpu11jYRKyqInU5s1aw\\u002fF8mf2KQ\\u002fy2dI5aZSMStBY22u0uwTMWMEDGm3uSqQnaq1trYaLwax0wPYyYRyKizVROFgTk8N7q\\u002fGfbHL6Qgdw7GHil5ZETkfAeR016VnEtfF8CKcc6iTLJW6ukpt3b\\u002fTnwaAt8s3pAri4\\u002fiRn9f++E7Gc2KmyLnnudLJPBqn2djbt0s6ViQ39PGEuysv172U0lM97DB3nesqeCP9KQSQf5V9Wqtv4tLYharS\\u002f2a+gOP4k8VvOnflipQd+HTiuv9WfnV7+Nw4fk\\u002fTmHQsHMdJZ+h+dPe\\u002fK0EQDQAMlPF80nqiDMGABQsASAOhCbUCAATpnU0lIkTEYTMJhXL9TOAcRDLkjUGCBSlJd\\u002f78W5NavXLbr6uG9VMx9kSyOOBwYzVx41zt6pgWSVHjjRT4VSq7iEkR6jU9NX93SH9kbnuz2mLeOH2R2mLeX1\\u002fZ19lDgkgSyhP+NHc5DUHsGDboeqJ3BAyWgMECgERRINkHAAOZXuajP85b7Y51E0aQHS44cBsD5dkxk9xUku6\\u002fskS5Lc1\\u002fpZgV78+KLVX9Qli49AApN0vI6A0FOnCb3FyG4fYG3Ql\\u002fZmy3uRJBaEl+i8pUO4I5GcSo1Ub9mUzxnb1HAnAW\\u002fosI1r8+XuX61YbZNjf8ZnZYGCjD4DARwwz+kfzg3rY\\u002fOi2KxaEzRvinAwDJ0mLsiONHEclxsfzIRmObP9NPQOe5qpU7lMcilWk1dsjMXW+U9USKTQ6zxqaL4oYigCAIEsklv6o94X4slVXT7xm6\\u002f12EYLlHzQYjWL4wcVRv7M31881h5bCMcdERtd3K+ABxpEhEdvvG8KDQnJDwFQe24wBTImKyg0INNutLF\\u002f6oUnbbMEeNWvGPUZPiRZJJ4dFL92+LEPhFCUXkXXggKzDk8+KL9xzdFcTlJYsDAcB9z1FC0Z0pWasP\\u002fUpDUAzHv5u9lEMf4oazL3ifXgYAXt97nEmnlbfLLze3z0yNn5ZMvsNcQRAEBRQAuPSAWu1RLj0QRWgmh0JjbXJaAHAG0icHJZI\\u002f4Xj7izNC38UBszp0wZz0TlMJ4dJYm5SWGoWlWsSMLFX9EszJpKMclaVWaakRsxKIwq67uqKqPSYrImor\\u002f0yl6Ku5aZRoqvR2w3NJD+jthgvdhYRRyg54NP4uvd3w5JU3N4\\u002f6j7sliht6UXGFOP9afdMI\\u002f3R\\u002fplBrN6htWhFDCADuK56Ch2OhCGLFbM7y4VxpIEv8WurjKII6cAeKoG2mziZjO3GsNlOn6w694H4syjMkHd397yL25v63DLiX8JYwiF5CJ21abZhQiNurEXoibq9D6N6eqL8hkafIl5vlnqaXAYC5n34vU+vEPM7s9MQnp4\\u002fzffgxDhgx9N39gxe8lHF3uVs+qtz1aOIi1l9gYVStTf+fyq94dK4\\u002fU9hilL2V\\u002fiyKIC+XfMhA6TiOZ4iSlofPwwEnWQBgd+vRK+oyHCBblLY0fDYAXFVX7Gg5xEDpdtzxz+SHODT2F7U\\u002ftpk6jHZzHD9yRcR8Pp1HOhbRu\\u002fJtw45qXX0gS\\u002fJ80gMAcFp+8WTXRRQQHPCXUh5hocyfm\\u002fdd01QGswIsmHVx6Ez3+g6Ba5PQ\\u002fe+iISjpDPl0Lrgd3f3veqfiyzVRt0Vy++QA\\u002ft8XrGf2H\\u002f5wXgau\\u002fxzoyYB1o4JnPa3G+Dfu1Orrq3TVKKBzQ2aRff9TGB2WTpPqiKxgtCRZzBQAQAx\\u002f6Dsx\\u002fubPxtf34f8KG3IvkSxlnXKEFg7cuxBGCvgwKvv\\u002fKwq7WkcGhgNAUVfriMDesJGTJkNzCFtqsBso2xr\\u002fQ6isunxlpZglqNW3EZa\\u002fBet\\u002fkZshWDgOdS1d+aXNVU3ypnZll1JvNFstNjuHxeCymdIAYUyYOCVWOjYzWhpAPTjTdQSpd45U1ZCi7D1z1OIWzPAjAKC8e129gwbD8KvVbVer2xpaFQ1tCrXObDRbjWYri0HnsBkCLisk0C8iWBQTJslIDI2LCPiTouY3ggPHL3U2Dw8IBYDDzVUZkhCmW0dhql+yyWGWW7r+p9UKAMI4ASsip1Tr2hIFYQXK6hvMiVJqjFcqW0trZa2d6ja5Wq0zmcw2i9XOZNDYbIZYyA0N9IuQ+qfHh2QlhhIp1n9Z\\u002frfu5D9XsNRa097TJXtPlbR3UXQzGUxWg8napdKX1LTvO10KAEnRQctmDJs9LpnVdwg7jYYyGDSbjXrQnCtLM1JXD+8jWEWtbQCA22vcpepqVduDb\\u002f5KMjIYtLNbnvTyozR3qH4+VHS6oEatoxh\\u002fZ7LYTBabUmNskqkuXjfyOMzslIgpoxImZcc656IaNCue\\u002f65JpiJbfYbDYpza\\u002fDgAHG2pOt5W06JXAwIZYgq1AoAQthQAaAiFi8SKF75vaqfuJ930+sqMhD6RiKHFZnPMe\\u002fQrndFCdgAAwK\\u002fv30tMYA8Ax2SFYqbgUneFDXdkixPcE2W9o9aajuZVHD5fUdlAHYQ2W+1mq12tNbnO+JoWFzJzbNL8SWmCvhNDDiH\\u002fo3fyIPizBMtmc\\u002fx8uOiHffmeZhanpKpR\\u002fs63x77dnffoyomzxyW7uoRctkJDnUruyr0js0mWN2fPAAAE5WH6DYBw3WWLhM3mUOuM\\u002fsI+HVUESo3x022n\\u002f8irolzcwQsGk\\u002fXc5bpzl+sYdNqhLx\\u002f6827cATEvMjmCL8oQe2wZ+Ti9DMGCSWkbfjlHtgIAwMFz5X+qYJ29XOdJrTISQp1qBQAY4JvqDt0XO+eQLJ8yUdYTSo3xpwMFu09ctVjtZF9\\u002flNXJyupk3+zMXTo9857FOfyb9evfqjvZ99nJO9X6w0WV90wfSXZ4hlqwbHbHtmNF56\\u002fWd6kMpKj8vvfvd\\u002f1KSVO78pUvDtY0d5EdviFX6l7\\u002f8tAfeZWvPzTHqeIiAccXwXKCA2AYBgAsImuMFgGOjuszHfWDXKl3\\u002f5lPF9b+Z\\u002fMflO8i34mPCKD8jW8VXtQKBjK9DADMnZC6ccd5yrnZTlyseuauKZ7Wi7txDp0rJ5uus3BymuvX+2LnaG3GILZocmAmZaKsOzgOe05d+2L7WcrJxXzHaLZuPVh46Hz5U2umzBrb52X853Hz7+Tyls5fzl1dO3VEm0IzOT32TGk9j81s7dbESMXtCq3BYo2XSobF9ry9gkV8InOzUa7Kr25ODg9qu16GzaQXN8iSwwKdhQmo76Fv9uR9fyg\\u002fJTo4Mz7ELe2xHwpKm1\\u002f4ZK\\u002f3OcV94fyV+rWvbP30H8sipf4AIPJtrjKCT87l7iwp01osOI4nBQbuvGslbitD6DE4pgHA+u0llCt1SdFBrpYf9+d\\u002f+et5V8vgGP\\u002fnTCg2CIq6WiVsbptBO14aTfZdx\\u002ffpZQAgQMQbmxlzobie7ADQGS3nLtdPzxnwSFdfUGmNF0sayVYAAGAz6dNzklwtnWbVha4yB46hCOJL0F1nML\\u002fyxcFLJU1kx2BRaoyvbThUWNb83N3T\\u002fjwFd3Lz7+SksMDE0ID4EMnFqiYAaO5Wi\\u002fmc7PiwyADRparm9bNztp6+TNIgADCYrYF+\\u002fMt1bVa7gyij0BnjQyRlLZ0+CdbRS5UrZ2Y\\u002fu2oK2dEf56\\u002fUv\\u002fjZfl+CTb4g69I+8taOL15aHh0qdn9ReOFcQ9OZh+9\\u002f5cgf\\u002f5g66b+nzgEAwhwLuB7sLf2qFQDIlXrXr59vP7vtYE+63Q0ywcPPfPMx2K2XmlvaDRoWreceILoLb4QFk9MoBQsADp0r\\u002f5ME62hupeukva5MHZ3I4\\u002fTMuECwu\\u002fV8liiOT2dX6Vpd7ZQ0d6ie\\u002fWBPS8fgw4We2He6tFmm+uj5JVx2n9Mbcm7+nUxDUYXO2NCp9OOx9+WX600WMZ9DrOXBdRsyXNehqG7vqmrrulzfJuSwaCjiLJMYGqgzmYfFkKWNWrAUGkNOWiTZ2h+VDZ2vfH5gqNSKoFtteOq\\u002fu75\\u002fc414IHMtchgMGoJY7A4Rm91lMAAA4GrcWgCohFyUCrlS5\\u002fz84\\u002f78ofqNA0S8pOhgsnXg8DgsBLnBbi6YGBJrcThKlbI6TU94+MYFa8LwWD8+mzTxP8HFkkalxjigH9FHDp332B5cMKlPexAAhHRupij2tLxYadHZMIeXVmFrp\\u002fqRt3Z0qwcQhRgQxVVtj727c8NLyzluj\\u002fEQckvu5McXjEcRJCa4Z3Zyp33NlGzn\\u002fwRxUsnba+YAQFJYoGthogyG9Q5MdkItWFEh4la5hmz1ikZvfu6jveb+QpIsJj07JSIlJjgsWMTjMHEcN1lscoWusV15tbpN1qUlbwDQodC9+Nn+0CA\\u002fssMzMWJ\\u002fm8PBZTIe23NAa7YAAG6rRnkPYqadvjUJe95LJy5Vb9zhrf7MZTOlAQJ\\u002fIZfFpGMYrjWYNXqzTK6hjGWOGxbjpcvGd7b8e7XDgWn0Zo3epNaZNDqTRm9W60xqnUmrN6l1ZrXOpNGb2rs0lBElAgRgZnhCGE+Y6u\\u002fxzhsoDDpt9viUHUevkB0ADgd2NLdi1dwRZMeNUd+qqG6Uk60AABASKMxOiSAZl0dO9mfyxUxBIEvkRa26VPpH3\\u002fmtX7UScFlZSWFJ0UF+fI4fn81iMbR6s0Zvkiv1Vypb61q6qO6CXsrrOl7bcOi\\u002fTy\\u002f68xIFbsmd7PxzBvR3uRd2VyvwJFgPLxn\\u002fzg9\\u002fxIVKRqX6Ws9677vj3ao+9U8S\\u002fkLuuttyFkxK57A9vlKqG+W7T147cKbU3reSf6Wy9Upl\\u002f3V4J2\\u002fOng4Ar06fktfUkhUqBQAE5WOGbwDXY8YfUfZC71Ut4r3U0qF665uj7r8Xi0mfmB03cXhsRmJoaCCFjFqs9vo2RVmtLO9qY1F5s1PEPTX7BwGNhor9uN4rLL5kPyCAPHZ+zyNpY7tMhsmhQ3B6CyalUwoWABw6Vz7kgnXoXBnZdJ35E9PcHgEoUddPCsqcKfV2GnYH9tJn+zsVvXUTEiiCTB2dsHreiJRYqftj5kStMx0+X\\u002f7j\\u002fgKV1kj2Xefc5botv1+8f+lYsmOI+OvfyQOlV7Buf+k752cGnWZzOB75YCeXzZT48VyVbuc79\\u002fZ+uc6F4voTl6rJVhemjkp46YFZlN0KriRGB\\u002f1z3YxVc0e88dXh8roOsttn8ppazjc2Wex2ADjf2PTK9CmAcAEYAHSUew+5tBtypd7hwF7feNhk6dN1wGUz71owatmMLCHfWwYKi0lPiQlOiQm+feYwm81x7krdgbNll8tbRqdFkYvean5vKF0em6m1WkqUsvHSaPdZbgZKYlRgYnQQZa2nprmrtrkrPjKQ7BgsGI4fza0kWwEAAEEo2oMAUKZpmhTUJ03PnS+2ny2pkZGt18lMDH1l\\u002fWyiI8g7IgFn1dwRS6ZnbdqV66Ut9t3eSxOGxybHDFk915X\\u002fe3dyr2DFhQW42CEyuP+fhADD8S9\\u002f8VbbXDYj67m7p3t+FZGJCvH\\u002f+tUVr204dKqghuzzjTdPnHp52pSehAYAAEBY0xAW4KbdvjUJdR\\u002f+eJKkmDkZUa89NGegC50xGLRpoxOnjU7UGy1eqpa3CgaKNulVfDqzWa++cbUiWDgp7UMqwQKAQ+fLn1g9mWwdLAWlzV0eKvUjUyMpR03w6Oy3y34OZPkBwPp4ilm5y+s6PNUQAWDJtMxn757musJ2v7CZ9MdXTYqU+r\\u002f3\\u002fQnKzgGHA3vvuxNb\\u002fr2a7BgK\\u002fu\\u002fdyb2C9d9HF7rYB8CJi9V1rd1k63UmDI8dkFoRMOi0Nx+b\\u002f\\u002fi7OwfUEnQyJymxqLUtgNfzk4wMD8MtJ3F7E4KK+lUrALBY7btPXHO1rJo74onVkwf6V7hy09IFB8TDaWN\\u002fq7vWZtA+mTGB7Bsss8alfPbzWcq1fI5eqHxs5STK2MQgGFC4neC28PF2nOLECDAc\\u002f8+WPyijNgCwaEr6P9bNIFt9Y\\u002fHUDI3e5CmfoLy+42R+9bTRQ9+L6vudfOpIydQ5PROHVZa2Jqf39sB0d2pPHS1ZvrZnntghvJNdD+qFznZ1cKiI+EwdwxoQO45dJpuu48dnv7J+tvvV8QU6DX3jkbkrXvh+ECldf1TXLs1I7VNlQANR3jTcdrXX4jN3zB7+5J1DVi\\u002f4S8FnsO5NHgUAZ2X1YTyKKMYg8OOzJ2bHncynCBEoNIZLJY1js2LIjoFjMtvOeKiA8zjMKaMSyFYAABAyvEX9TuXXVDdRZzunxkqfv3s62ToQ1iwYdaG44WpVG9kBAAA\\u002f7Mv\\u002fMwSLhPNOzjtdOXZK8oVTFcGhovKrLXFJ0rZmxaHdhVFxQcEhopqK9uT08LZmRWFubUpmRGJqKPEI11bJiMJpWT2hbadFLtOYjJaouCCRP4\\u002fYymyyylqVkTGBgCCNdZ1xiSFcHrO4oCEuKUToxyHKOA\\u002fa0tCt05oCggQRMYGkHTKZ9H2\\u002f5i+7a1x0XBB4qm60dWn0pt6xDldr2n88XFDWQBFUqm3u8tLgv3\\u002fZOJHAp4TP42cryCaAILFg7YLRZKsPZIeFhgqFMRL\\u002fWIl\\u002frMQfAHDbFQDArUUAHl+wlGSnRDx15xSy1Q0ct6pMxyz2JrIDAADXmM\\u002fqLUVk8y2lSa\\u002faUlng\\u002fPdD1VCenqcKDgAc9JyVPiBO5ld76pKeOZY8FtVJbnfP0S90k6P1OA5b9lwkGQkQBF5eP8s5s+7gQBHkSc\\u002fN4apG+Y0EbX3B9U622ewVJa0Ou+PMsVIen1Vd3o6iyLylI6vL2gKChHabAwAunKxYvDInMbU3E8pZ2N0ia1USmzu36urUZGRHp2RGlF9tnnvbiOKCeqPeIgkQlF5pcpZxHtRms3N5LLPJ5r7D2ERpbGIwoVbgSbA27Dr\\u002f0saDxOcL1xoe+M8vG3aeX\\u002ff29gvXGvoWhOOeY+0iIWfxlHRXS2Fx494jxUVXm8qq2g\\u002f8ca2ytqO4tOXg8ZKyyna1xtit1F8paXYtDwC3zxo2iEQVNoN+ua39eE3dHzV1f9TUAQDCmoQZvkbokQOaYYbFpL+yfpYvTRgEoSNAU5tOkB0AatNxo7WUThMTX5XGgziVaHqy\\u002f0lUqbqiBf5jgiOJf9ECX0OWvjAmM9rTFAVni+r0Hsb9DYhBtAcBoFhVR3yo0raS2obFla11LdSRjamjEuPC+0R4B0dqnDSxb965K4c9\\u002f0UHr1Wdr2mslSs2nS1wYBSBsH4h3cljJidv\\u002ffrUmMnJsQlSk9GalhnB4faksLY0dtdVddRWyiKiA3Ztza0qa2uqk9fXdNZVdTgLO3frvrlzKwAgjOIAwYmDVwOChKXFzTqdCUURZxnnViiCoDQEEIod0mioUqFvaez5aahfRNdq2++c3dP1u2HX+fEZsf99bOFb3x374VDB+Mw+9fkzhbWuX11ZOCmdNPigrUNdWdOBoqjRZF2xeOTPu\\u002fMDxPxhaeFhIf4VtR1bd156aj251i3gsiaNjD96gaL+5YUXp05yXZcQAADTobwHB9okvH3mMMruXipQJi2EqGE5ML1M+yUONgl3KQDebdjNQMVWezubHmOwlnRoN1nsjQLWaD5rlHNjV7vV0cljZrHoETLtxkD+KrnuRweu4zOz\\u002fblzlcYDFnuz1d7mz10gZN9QX\\u002fiUsDg60rukEDuB+k4YHCiKzJ2Q8tP+ArIDwGqzn7hUvXhq\\u002f5ELL3QqdJcrWshWAACIChWnx4eQrddhoYwjskIRg9du6qb3nYLioOcMiXsWDaaaT8miyekfNJ4kWwEA4NyV+mfvnka2AgCA2WbDcby8XT4xMbqhWxUf5C0phxLSncxk0t\\u002f+4i4AmDonA8MwFEUT08IAYMnqsQDw3BtLACA+OYRwAcAL\\u002f14KAHFJUqeFgHJzwpKUFuYsg2M4giIA4PzgWobYyon7Dtc9OoPYCjzVsFRaI9FLWN7QUdPStf62sUw6bfrIxLq2Pq+gLpW+oU3hanFl5pg+w7gAQKkyzJ2e3q3US\\u002fx5R0+VBUr4AMBhMwEAx\\u002fDb5g47dpriJTNtNHU8wgufnMv94Mz5jXn5GI4\\u002fsHMPDKpJiKLI4PKGug07mDSpkD1Bpt3AZab6sSdLeLcJ2eMBgMfM4DKTpMIHXdWKZLfa2zHcCABmWy2O28z2+gjRy\\u002f7cuQBgtbfzmJkR\\u002fq8qDLtdNx8ETJTmmkMUK5QAgMbW2VuiP2Qm6pQCggWT+lSuXfFSOfKRwxfKPUTGvVWvAODe2NkiBs\\u002fgMD+dtMzVbrM7TuZTR8TCgvy8VIsGyrCknsfYnY5urae8uYTgADuGxQVJzlQ1RIh9fIP24v1OdhUgEu6uwVmcctOrO25lnLi7nFuBpxoWn8sienn2XyiLDw9IiQ4GACad5hrYAoBr1b2tWRISP577z3zvqvEAkJkaDgCktP3li0YAQHQExasjOyUCRRBPfTeUuK9LeL1JGOd7k3BsZkyAiEe2+oAD0zPpYSiwQoSPkH0AngeTX7cjKI47cLDjYAcAJq1PfYFBC0QRNsBg2gWUdJirFZbmUE6yFTNdUe4bKVlmw0zdlsZAdpza2m7FjAGs6FBOKqkwA2V3mmtCOMkyU2W3pZGBsgDAipkkrKgwThoARIX4ZySEltRQ3CFXq9ra5JqwgQxdIHH4PHWNG0WReRN7TpWSBoPsaEfhnVHTyzSNoyXJTvu16nZP8yCNGxZLNt0AseEBbCbdU\\u002fStor7DdTIcJ3YHpjWZtSbzQ1NyyD4fGPSd\\u002fBeELGYE6bEhO04WH7hQvv982bKpWYRRptAKuX3SzLwIVnZqb7coJV5ShEkIuKyYcAoh8wLFuoSYDgABzNCnnFcmZA\\u002fgTjXb6rsNO7XmCxrz2QDeMp35os5SYMO6yOUAuMyMFtW77jF4p53PzG7Xft6u+Qztu3jMnwQCqMWhY6CcQFZsIDs2gBXdZirPEM1tNhRrbfJM0bxWY6l7YT49wIHbAUBpbaGjLBYqUFtlmaJ5HabemCZpahdXvMRr+qWsTuZppsBxWTHe04uOyYrmhozS202kGFZ+aZNLqT6MyYwim24AFEW8pM56mhewVq5YOy577bhsssM3BnQn\\u002f8WhrmE9tGTcw+\\u002f\\u002fVlDenJ0UftuknnBDXmljclSfSlO95\\u002fSrZM9jIwdBfGSgp4AoJe7rEuL2CpS3Hjft8SVxlGBkam9wsV\\u002fYjNhI\\u002f9ecX2Mk7+O4HUHoABDIX9lbDiCIfyeOWxGkJ6ZIaY9jfo4gNKLOFS76h7OMVLie+BAj+dBpvEEw3MGh+bUaS5KFUwx2pdLawqeLyzUnBPQAhbW5VH2MSxO5Fw5ix8nNdZ3mWhbKU1ibA\\u002fmxSis5qDRjTNJHP56irE0cPl9+35KxPr+z+uBl9ivv7UEAoKO0NpOCS2O1mxSuMSwvr97YvgnVN46XfvM2DwN4eSzmV6cvcZmMwWnWgO7kWwKOdeFuPewocyTJAp4EKyEi8NBHD3ap9KEBfs67aunkTFIE2lOTGwC8vEYGQUyYmGzyivu6hAg9ETNsQWiBPqoVh80I9znXnxJCrShxVysCp93LtkNOCCcpmB2PIjQAmBi0DgFEzIwglpy4rPw9TTTTdTZ318JzQ58DgAZ9fix\\u002fdJ3+Yo5kBQCMEC9xFuaymVNHJ1JWptrkmqvVbV4COp6wO7DjF6vIVgAAEAk4\\u002fY5xWx017bCsoNOsuid2lqu91sPrkMGgBQcIyNYbw0viZafL5AquzM9MAoDydurxA9658Tv5JoCZj9k1r5KMrJB6kgU8CRYAMOm0sL7yNDYj2vWrxWrvUlFfXwAIDxaRTTeAVEIxzIKSgpa2URFhWoslUiRyXUIVYQxDGMOcX\\u002fslKkQ8uPf\\u002f\\u002fyKEAAGAU5uID8nCqe5rTzgLE4yU3K62ykaJb3c1OlkwKY1SsADg0LnyQQjW+Sv1lNPXAMDscSmkNcndKVJW3x4xkWTsVOh0Bup9hgX6+R678BEvgqXUGMkmgGutHWVtnTaHo1LW9c6y2WR3f9zAnUzESX16wd80PApWvyg1Rk9xcASBIDF1Gs7g8H1vX+Re\\u002fGHFsmf3H950+21k30AI9JBG9P8VXLqIbHKDjjADWB6jPNkpEaGBfpRLkJy4VPXs2qmeMjw9cdhLe9BzyMwJ5eDndg8NMQBobFeOWfMR2fqnYek7RJkgM1waLOQHC\\u002fn1XdSRO+\\u002f4fifj9hrMfAKzFeH2OtzRAbgZAAGEj9ACEXoSykhFWZMQRnpv79CtYGC3iyteJs0Q8Nj9vusGhNDn9Tm0ZktRa7vOYi3p6I1fZkgHHFDzEmj4G99BEJg3MfXb3XlkB4DBZD1bVDdzLDn3xQtavTn3agPZCgAAidFBCT5EISgHP3ep9X0K3TrMNop4HwB06QzBQr7BQt2P6R1f7mTM\\u002fIdd\\u002fyVVliIOuA6363B7PWY+DLoPEVowyl5M461GaJHksjcFj4JV1Swvqmxxr34\\u002fvHQ88UGjN\\u002fX19DLk6\\u002f\\u002f4vsPHJ4z5rvByo0rluqLqV0sX4dYi3F6CMLIRBvkFSwnzxsZh\\u002fE\\u002fQZjhfovyWSROk+68L4gwnu32m03Q5mOMxGLxgUtrm3\\u002fMoK+MHz5UNSLCO5VVSjqkGgIWTPaZ9uUI5+FnR30R9Nw3K6RwA4GqLLD0suKipLTU0mObDuAtXvN\\u002fJONZtVz+PWc6QHR7AHZ0OwzcOw7coezqd\\u002fzTC6E0NuTlQC9ah3PI3Nh\\u002fFcJzFpFusdiINSshjj0zu7W6weJ4KmTfUs0+wfR6dMy0udlpc7NP7D328cJ6rHbdXIIg\\u002fbitBGGm+pGLRieVXvXL7+Xd3TniRbPVKmaZ5e9OZtzLvIjsGyJDsp0F3cETg04HsnrSVQXNN8dXM8G\\u002fI1utIA4QjUyMLyprJDoD80qZutcH3FCFPGacMOm22D+vQnOwsPt9VOjkoc3LfVuENLodzE5iQEP3t2YLYQPFA1Qq83sm4vcqmvAd3UOdSeAXDzH9YzSdovLvogucA8fUXvHGoBWvr0aK0WOk7Dy2QSgRj7v\\u002fkxOePNHeq\\u002fr3l6D3zRzvLeJm7nT6k7UEAGNAMRADw9MRxJAvCyMbNBxFGsi9q9X8eva31qvJruanYZFcwaPxxwW8wUcFp2TNSzsgu8zWTvXtq6GcMlFuq3NJhygeAMN7EFNGd3ebSctX3CEI325U8hnRc8Btqa3256gelpfK07BkAmBzyAUIVo10wKY1SsDAMP3qh4s75FL3X7jR3qDwND540Is77RHQEBYqq19LXfFD528TADNdQ+iCWGrzJqI0mFEGalWqy4wbA7fVWxSrA1GTHAMAchh8w8wm66EOU2Wfkxp8HtWA1dajeXD9XKhEAAJNBM1ltKdHBK2dmf7T99KYXVxBlbA6PguX7yvI+MlDBcu0f7AE3ARqAO7oH\\u002fIbqjzp9x8dVe15OvWNXa67FYSvRNE4KTD8tL3kycREDpW9vOsNE6R1m1Shx4v1xswBAZdW\\u002feu2nbqsumC16PX2VezfcFzUHhmQ\\u002fXuAzwscHv3lW9ny6eJ2YleK00xDmROl\\u002fiM9d5qtd5mszwjYCwKn2p4PYwwBAZalZGPUbijCPtz2osTaImHFjg17vMl2dEuItMj1lVAL\\u002fh5OUY54PnS\\u002f3UbAOnaWuXoEP6VcEFsxWom7AcShUVnForAxRDGG3en71\\u002fkWokMmjA\\u002fw1JjNpfMjgwY021X03plY94I5Wm2IVXfAkjf\\u002foTehSpD4Ag95b9RTy2MS8jnFhkoqm3tqjF1XysvbB4Ljx34hYqr7fZZ8HBB2llWuav6k78k7m2hCOGABGihPmhYwEgCcSF+YrqwGgzaR4PX31xpGPXlRUNhnkANBpVr+WvmrjyEe6zJpGA3VmzVDtZ6AEcoY5P2usDRJ2CgACgIhZySprDQD4s5JQhAkALJq\\u002fDfPY60KCxaTPcBtYSlDX0u1pCipXcByOXKAWrAB\\u002ffk7fhBtPjBAnNBvlqX6RXRZNs7H3it343fVnkxMbGSTka82WoVErALv2XfdEzRsAs+s+tinXAaYhe4Ya6hpWZLB\\u002fVbN86ogEAIgOEZ+\\u002fWp8SHSzr1jJd2npegnl2z5WvwUEZsh0Qvi9V7zsWh+0\\u002fFTtnSYc7p4UTMXlam5GJMpgow4rZASCSG0jcZLF8aZtJ4cfgJQhCGSgdAPyZfKOdotIBQ7efgdP7PIiY8S36UwA4ACgt5WG88QCAIG5vKQRx4BYcMMrGoJMFk9L2nLxGtgIAwKFzZYlRU8jWvlyuaOnwsCrEvAmpvsz\\u002fAwDzQ6kH4rGZHiOk44fFLpnuUy\\u002fNkECpRwUNrQI2Kz0sOD1swJ3dlOD2WofxF7K1BwRlTUZZExBGJoL6A8IFXIdjatxeg9tKMcs53NFO3uI6mOWstXsxQ7wJoQ94tgLfoRasaSMScq+vpjtzdNK7PxwvqmqtbOocldLbl+lFsChHY9wIQxBloEWDQ4agYrL9BkAR5Lucp1669uPxjuIZ0mHgknjppNHQieEYAFKn71gTPVVnM9Hcn3k3hmo\\u002fN0IAOz2Ik3287WEAPIQ7LoCd0W0uJRcCQACN5M842nIvjxHibE66kx4fEh0qbqQaA3g0t\\u002fLxVZNoXlv9nsLtADDft\\u002fagF7zkgomEHE8rhtbUdCQkSG02h0plCAryNbGZhC+bV3d2l7Z1poQEAsDghuaQcBg2Uc5ZgjLH0v3+5SY3IQgAMIngNY5ZixzGrZjpAOXwe9zRbO1exvD\\u002fHGVNJvuGCOqf6o4Zw9fM6YksLJqQ3iRTnrtaPzY9+tnVU51lvORGecobHjSkNT8GwSDGEvYLAggNQf+dcedzV7aIWdQDOAQM7uulP3ea1WMlyZHcwDJNM7mEbwzVflyZFPK+61f3OFSa\\u002f91p\\u002fnc7vwaw052S5KpNIwOfc372wvxJaRt+OUe2Aqi0xrxrjZ50AQDMVvspD3O\\u002fZCSEUk5vMCD8PAfsDVRxN4Lduwv\\u002f8Y8FZrNNqdR7VxwSZ85UTpyYRNQKfdn8zjHDKmVdySH9Z5n5BG5ymA6QjQA07l10v9f665JCUOZIlDkS5z9m176LWU6R\\u002fQCA623K++l+\\u002f6Jx7yS7hgJqwXJdVBpB4Ik7Jj1xxyQXPwCA2PPa8Vr90EUHAQBAe8MKONCxhL5A5DSwUMbnIx4EgGz\\u002fOADIFEUT3kxR9DV1YxDL742M3l8uzS\\u002fSmYvgKSnhsYQFMBT7+asxb2LqV79doEw1Ony+3ItgnS2q9TT3i4\\u002fpV97xMlpQ7rYqj1Zr+v77cwEBAgRB7HbH7t0F4eHi5ORQnc60Z89lg8GSmho2aVKS0WjZ\\u002fnOezY7NnJkeGxv43XdnLRb7sGGREongtx2X2lqVmZmRySkhzs01GtMv2\\u002fNwgPnzsyIiJKdPVbTL1PJOzZSpKcOGRQ2ZWgFg1jzAyRmUKHs63e913x8NhB7PEG\\u002fGTHttmtcAd2+qO+yaV3FHO13wnGuQYUigFixfEHuex8PuwBRqg+9jAvrFS1Y9icLWNrIJYGR42EDHEv7NkCPx443NjD5\\u002fpZ7sADh3uV5vtHgaZOdptDObSZ8xJpFsHTjBYo+C5b4U+bFjJbNnZ4SHiz\\u002f++AidTps6NfXChWoAsNmwlhbFSy8tIoodOXwtIEAQGur\\u002f87bcmbPSw8PEs2ZnEK7omMAVK8cQTWDn5r\\u002f\\u002fXrhocXZQkPDjj48899w8eZc2OSlk2bJRn316dNgwjyOfBgFmLSCbECZd+KbvauUE5SxmMkfYVI\\u002fhtmtkH4BDvxEcHXTRe\\u002f3V2gYGtWBt2Hk+ISJwVg51zw4Bh83wF3I9SUm7XDOEguVlGV4SP12+CgBynV5hNCYESOqVqkiRaGR4GLncTSFTFO2sKN0IQ7UfJ1aH40xDQ1JAAEX+x5\\u002fJgklplIJltdlP5tcs6rsCAIHRbL14rYlsBQCAaaMTuWwm2eobBYqqUZKe2zsyxJ9GQymrfiqtUakxuq6wbTbbuFwmh8N0j\\u002fQHBvYKn8FoDQ4WMpn0VavHNjcrhH6942MoGx5ms43DYdJoKLEABAD4i3ksFn1A81b6Am6rIFlonNsQmpRk9BGEFs6U\\u002fGbX\\u002fsth3E72AThMv+OYmuG\\u002fARCPje6BQi2ru89cU+molciV6FAx2XQdT\\u002fN1DI6WDjXZ5IFPF837dNE8CY97+P67NyxZeHDdXZy+88r\\u002fDQDQUZSGoCfqKLTDRw5VVTsG\\u002fixNyI7zNLTtiIdp+88V1Vk9jLDzZbSzk3aTYmfLOee\\u002f3a0XnC4mgx4bJnEp24eKvotFTZqY\\u002fNNPF7Zty2WzGG1tqsOHrl692nzxYq1rGQCYNSv9anFzSWmLSmnIyYk7eaL8++\\u002fO5ubWAEBiYsg3X58qK2t13Xze3KzvtpzZ+OWJyVMoUvZrOhUAMLjBzyRwRyvJgrKmkCwDA2HQ\\u002fd6m+71FWfvBLKdsyruomo2DhOIYAGCx2SN8mB8mJkzsaaHTqsZB5Pt7ZECz9wFAh07vfI21aDSurqHiVFldfafyvmnUCb7XmmTfny766O4FJHtBXeuouHCSsV88Hev5nw6tmpCVHTPg+iOKIFKBoEmtBgCF0bi5sEhjNq\\u002fNHl7brWjWaNq12vlJSWMiI\\u002fRW61eX8m0Ox21pqSmBvWGU0s7OzYVFjWrV6PDwqq7ulKDAeqVKzOGkBQd9V3QZx\\u002fEVmZmxYopYOJ2Gzh6X8uvRy2QHQHFla5dK714rP0G1viEAhAb6DXcZKNYv9XpZGEcSyBIRXztMfR7+tPiQmmbqdLCC0ubxLrMkR0ZJ\\u002fvnPhQiCEBWlBx+a5nQ98EBvl1RwsN\\u002fzL8y32zE6HQWAl15e5Py8cOFwm81BLBrmuvlTT8\\u002fFcZyouN1xR08Gxj\\u002f+saBNpf3+QlGSNKBbb3xq5vgbDA3jDvKf6ePoWu\\u002fQuKsReqxN9bB7KhZmLbIqVjPEPwxJHz11DSs1OripQ0W2upGeEEo2XaeoglrIBkd5PfWYDE9Mio1e9uP2fx46tuzH7eOiIsnuoWBqWpy7gvTLF4dzySYfGNyxfETC5a4ZNmx0RPjx2rp2nS5TKn156pTfy8sBYGdpaTCfPz4qauPF3pHkAJAeHJwYIHlw1KiRYWHtOl1RW3unXl8sk22\\u002feu3OYVkvTJq4qcAtUHIdT9UiDMfdY1Ums81Te3D+pLQBPbk5kuQcSUq8IJT4tzh8nKt3TGa061dXzrm1YVG0R636hVAo98+USxwiCLg3MwEgzF+4ekzWnWOGPTNrwg2qFQC4R9yHREcAAGWOYUp2Uc7igNvKbIoVuGNgTzEl1DWsZ1ZNfeXrg2PTo6NDvP0xWZ4Fq61T3dKhipBSvGYHSl1Lt6dImSeeGD9mQUpSk0r9QM7IOIm3P8EXHv12T05CxJWG9i6d4ev1S3ks5vYLV\\u002fcWlOUkRD49fwJR5sP9Z9uV2lalVm00vbRkqj+Po9Abn\\u002f3hQJfOECIS\\u002fOfOeXWdik0n8staOx\\u002f9dg8AfH7fYsqbb9OJ\\u002fIvVzRiOR0j8\\u002fr1iFgC4H2vjsYvnKxulfvxufc9l2Xmx5PCVKgeGjYwLf2xOn0exX\\u002faUV3QZDHFisR3DmDRaII\\u002fLpveETvQWa5hQyKLTHsohp1w6M8W4DAaO4wgCDBpNb7HwGEwaitqo4kEECZGBidFB1Y0Uqfl\\u002fXKwiLe5y\\u002fko9ZXsQQWC+18Um3CGSbOv1sgtdZQ4cQxFkbcxMpzcnI4rJoFMeq61TXVw1mMlRhxAek6kwGAsb2uZlegsrDxIP898OAoQeywjYZVPe5x6Gx+11NsUdDMlWSkXzHWrBuljWKJUIV732Y3x4QIBfn1r6x0\\u002fd5vwcFizyND0bAPxxsWrdbWPI1oFDGabtl1ixP9EqadNqw4RCsnuAMOn0j+9Z6Py6anyWgM2s6VAQX20Ox5nyhn3\\u002fuFtrMt+3cefk1NhrTbIOlW7Tg8uYdNrdG3bUdSripZJ3Vs2e19C+4f7bnPtxZ19h+Xtr5qWEBTmjraRjNcpVZ8rrf35yFeCw+P0fAKBFoT54uXLLw8sRBO7\\u002faldpS2d6RD8p0fVK1e6ysia1Ok4iZqBok1ptstlobgK6JC31s9y8SJEoIzgYoE\\u002fPero0+D9nzs1NSojw8+vU66P9\\u002fKu7u5dnZHx4\\u002fgKXwZib5K3zbuHk9A+p1uYrr+vo6NZKA3p\\u002frFMeFqMfmRbpWsx3dreezxLF8ensKl2fFgCHxRg\\u002fLMbT4bYeLLi1gnW1RaY1W\\u002fy5HAeGeRkS5xMoj9xqww2AeOwnHSgIKmFKfrapHsMsp0ku3NFq676DIfnJLTd1AFAL1rFLVQAQEypxYHin53mQAWDyyPjth4vIVgAA2Huq5O6Fo71nMPvCIc8rXPrCh2cufLRwLtk6QLJjvd2vDBptdHzE09\\u002fvB4A1E4cTxuSwQGIkk4TPNfo89dqn9yz67nRhm0Jzz9SRk1JiyG6AZoU6ISQARRBAID5YAgB1HcrmbvUDX+8iCvgyzVus2P+VqVOcX2clxDP6TkLy\\u002ftw5ABAmFP53zmw7htHdHpLVWZlWh4NJo2WH9tSy5yUlAsBbs2ZgOO6ufa7MHpv82c9nKGf7OFVQ46xkWaz2PA\\u002fT9XlZ9NA7Qjo3UxR7Wl6stOhsmIOB9v7VS2dkeRKs85frr1W3ZyZ6bE\\u002f82cQFSVAEGQK1AkBQEd5XsHB7E8IY5PWkBuEyxJvs6hccpt9JHhyTWxUrmOLvBx04oxasn9\\u002fwNRdx2uhET4LVqdAdvlDh40h6T5y\\u002fUu9lqQt3XOftIyjrpGh9DBTK5psrSr3xyXkTooN6m8Du9xaCIGab3XtKbUSA35srZmmM5sXv\\u002fXD6Xw+S3QDhYr8aWTdR\\u002f2rsUgFAnFQc4i\\u002f4Zv1SFEXsDowyDuIdklqRcFcrAibVVgiAd7UCACGfPSk77sQlimi6q2BdKmmiHOHA\\u002f3\\u002ftnXdcFNf2wM\\u002fMbC\\u002fA0pbeexEsKAr2LhpjizUx3eSlmOSlt\\u002fdeTGJiqikm0WieGluixt4rIlZQigUp0mGBXdhdtu\\u002fM\\u002fP4YHIZhd1kQEl9+fj9+\\u002fNw959w7d5iZM3fuPfdeEX\\u002fUoAi21DnmBI2U8STuPKkX343prQAgJT4oLMCjrLqtJcti+dqj\\u002f1220MEknj7FbMWv1Sr6B\\u002fWCx0SwENa0Z8JyA+tdhwUAgHHcPgfUHdetZWuIFrNyIdf9Z5TH7mdwhru9AImRvhFBXiV2Rlh+2JY1alCEvYDALrHixI+\\u002ftw8\\u002fO8OhomJqXy8am8\\u002fV3YATxLtbj5QpVDqjqb5Z8+yEoV6uYoIkP9h+DEVRg9ny8YJJ7DwAAIAiyMTkqPlfb\\u002fF3d+k8gAgABEk+vmo7j4MRJDk\\u002fLRlsHStM7j4sKvjhb7b6e7gGergBQKCH25zUfk\\u002f+uB1FUZIkv3\\u002fiQcFf9Fw5z9QR8TYdVmFxXYvG4OYiBIDTl223d8anRvfYcdzQVA7zjBvvM\\u002fBsk41m+5I5aW98tYctBQCA2zXKZasPL3suoytv3CfcrG90EwoKa+pjfb17sIYfE4QTDh3XFyXMWZhoDlPSSyAcl3cQ1N2q\\u002fYytIXUW1WNc2aoeRFT08MIzmTM+efnao2wpAAAo1bqPfz768Ys2Hk5nWLMj254rtMfMxLgF\\u002fTs4rBxbse\\u002fdgtXrhKHo8o4uaUNmbmpk0Ly0JABYeeDs1du101PiaJfE9E1vzxhNpzuDIsjGF+YyJZ2PBQAvTkkDSGNKpqfETU\\u002fpXif0X8uQxBBPmaSp08QXgiTP5d+enB5HEKS9vkt744zOcLW5dJhnHAAUaaqHeMQwtyYEgJEDI5Kj\\u002fa8W2b5hjp0vEvA4bz85oQdtWMdU1Tf7eLo42Aahf5DvwYJb0T5ed+mtAADlpeC6dUwJYTwOpAEQ2\\u002fFxdwkmeRZQmVX9LnuyNGm0qJ7myr5CBRkd5F1hu7X\\u002f66HLVDcWk3X7Luw5U8gSAsCU4XGs\\u002fQqZnLh4a+WmDh7dSbYfvbp+z0W2tCseGzSAJVk2cRxL0uuMjA09ml\\u002f8wrrdz675o75FO65fDz9Y\\u002fv+AosiUdNse9nx+OQDk3arpvJ8AAIT4uceH+7Kldsivrz9XWUmQpOHO8B8f5R6qu3y+6UatoYnlrSjeenKCg\\u002fbpvsxrz338u\\u002fPzLhyjM5j3nCp4+oOtc179xfEyzSYL\\u002fsqEdGp3wrsE4Q1mz5Uh9bjBdruyV8BE87iy7wDhshVgtTQvxfW\\u002fs8UOse2wdmUWlNQ0sYQoimw+kssSAgCXgz3zUIcXPostB3Pe\\u002fW6\\u002f40vCxGC0rPjv8c\\u002fX2xhIcp682vpteQVXaupE9lc7coZGk+1ODSbBXrK1z87+9vHpPzw1Y\\u002fmCSWK+jXFim+VkK7vtkf822GsoUYsp229eOdvb8nVW9ueZWT+cv0iQ5FM723p\\u002fHwub6MYV63Djy9GzOpq3Eewre3HhKLaUwZWb1XNf+2XNjuwWDTugyUkq6pp3HMt7c+XeKc\\u002f9+PHPRx1sOk1T1qiqVLY0terYiu6DoDKUP5QlxHU\\u002f2VxwprdABZO4snWAiNgKIKzqN1ktPsfYfpkoVNr4UPb0ojA\\u002fj9qmDuMLNBOGxhw4c516N9rk2Pmi3BtVj00fkjEi3sH8r6YW3cGs61sP5irVHa4NggCHg9kcV7LJzxdzsisqE+Tyg0XFqUEBz6QOZls4x21dxRHFiQzfCVqLrsHU4Cf0lXDE19Q3Q8RBDaYmI24MEPpxUE6NoS5CElauqzTghgChP4ogVfqaYHGgCTdTuTgIhyrHSuDFraXBokAApMpQ3WK2\\u002fff8\\u002f0CQjywx0q+gmP24qtT6yvpmm\\u002fcShqGT02PZUjtcqKreMv+hR37bzkFRepQDRZBUz1gAON90g0p0ZubYfrcqGuwtNwgARrN17R\\u002fnN+y7lJ4clj4gvF+kb4BcZq9vy2LFqxUtFXXNlXWq0mplzvWqzh\\u002fCXRLo7ppXVQcA05Jt17lboMIZhCmLKSGt5bh+MyZydqitB6D8NJ7HZovqMZJgjaGRVs2Hzgdn2XZYJEDn\\u002fZRwnLA5QZTi3acmLHhrg8ZWM55CpdZ\\u002fseHkd1vP9I8JiA2TB3i7iUV8BAGjyaps0VU3tFwrqSuuaLQ52\\u002fPhqYNJkty47xJbYYfjJaWbFzyEAJAACzb91mOHFSQKDBYFBgj9s3TnY6RRcoF3ma5cxnO7qS22EtYH\\u002fTMO1R+LkUbprHoBylealVN9J+2tPYQh2BTf8fvqDrlx3ahcOElQ5Wyt2hEg9C\\u002fTVVgIyzS\\u002fSXtrD7EPaR+SBIPJ3Ko36QzmVr1ZZzC16k2telOrwdyqN+nohMHcajDp9Ka6Jg27CAAAMJotM17+WSzkS4Q8sZAvEfHEIr5YyJMI+WIhTyLiU4k2oYgvFvK6u6a+k0wbmdDZYQFA9tXbNidjDU0K8bC\\u002fRggLDEWoOwknSZwgFcYWlVlTo1c2GJsB4GpLmT2HBQCvPzq2qaU1K9d2K4\\u002fCYsFPXiqmIiGEfK7cQ+ruKhbwOQggepPZYLToDWa9yaJS6+5+xfCU0AC26C7ABFNxbAVrsxyr9ktUMBFBvZnC3gXh9uN6bLOoHukc8k7ilSyJPWw7rGAf2bnC8nEpHcL\\u002fLl6vdLABvadMsvzFaUs\\u002f3WG179QAwGS2ns8vt\\u002fn+tEf\\u002f2IBn5qQd7TRvw1nsvPqcAUPQFou61lAPAHyMDwBF2mIxJkYBFWBtQ584iUs5kpvaYgNuPN141oUr5aLcrKZzMq6MBJLKRZcTJArUW\\u002fVR0vAqfc2Zpmwz4exn8q\\u002f7L6\\u002faesamN+8uJGlj1RTHSEX8o6ufY0vvmnGpUV9uONF5fdrV27NZEopuhV9NjYleuPW3iuaWeZu3zk9KcudJRBz+haabY+TJAKC2tE0SsAmKIstfnPbvHw7aHMrsjMFkKa9V2VxP9V4E4WLiJ62ajzoICbW15U2ueze+znoAwongemy3qB4hrY5eBg6w7bAeSE\\u002f4YstJLzfx9BGJ3jKJUq3bm3V956n8f8xKZ5syGBgX+O7TEz\\u002f46dDdv1JookK8V7w8HUURZ\\u002fb1pUkPCX7i9z8SfeT5dfUjw0LY6u4wN3AmAoifsO0DebLPeBJIemLKJJ9xABAiDsYQ9FC9ZoTXMErFtKGgyyFIAkXQMHFIZxsHWCx4r3irnmG0WE+cvqHTmUJDvFxdhLlXKyIj5BiKllc2xcX66XVmKmE0Wgqv10SGeSsaNXq9OTTYE8PQsoomAZ8TFOhBqXCCrKltDg7yiI\\u002f1Fwl4YwZHdV7+2OZyfW5Su6sV22ReUr9hwUElSlWEhzu1ig4X5SwMGUuFXz0Q0MUcDC4HW\\u002fZ8hre71F6Y4f80mOhhXLeR1a4hTKfw1lWY5B9MYa+DYH5cj98tqsWkxcYIXpfYdlhzxibfKFes3Xth7d72OMxRAyIWTWoL6rPHpLRYAY\\u002fz3qoDzvc3OSAu3OfLV2dIRXwACPaVcbnOdmM9N2xITnVtiVI5Ojw02c\\u002fZQSWbdPYpnSXU8urDPAbTqs42tAS9sxZ7Z5t7GUWDZv6cIZt\\u002fOz8gOdjTQ5JfWDUgOUTbahQKeAaDhUocPFwQGuJ5s7jeYDA\\u002fPH\\u002fY9j8uSyQCPo8jlQhOnr5JqVxdhEmJgf5+MqrYqSPjOzssm0xKi+3Wl+m5ysqs8gqTFc+uqASAd8eMAoCbmkpqd69mc6u\\u002f0LNjDjYogixdOHJgbOCy1YdsDln+D4PwOK7vW1RPssS4bj0mftRW73hvgqAynscWi+ppwnyOresK2w4LRZB\\u002fPzlpztjkC9cqWrQGF7FgYExg\\u002fyh\\u002ftp0tRqVE\\u002fvyv+e9+t7\\u002fKifUeHDB1RPwbj42j57VjGBrm71Fka9KsTQYG+A0M8GNL+xIXrpQt+huh15sOHimQuYkLCqulUgGKojhOuLqI8gurfH3cqER4mFdrqykhzr\\u002fwWlsok0TMr6hSRoR506qqapVQ2D7q0j8m0N\\u002fbtaZBTUvs0d0pE8uOn3pnzCg+I2yYIIm8lrI412AAON2QHy0NoGZEOyZ9QNi2zx5bvf3srpMFvfjpQCMW8sYPjRHbH4nqI1D+GEw0G9dvpyUIJ5LrvravvVUbiJjr\\u002foulZSlhPMxWOcTRBYsP9ek8VugM0SHeGz5atH73xS2Hcnqw4Y2\\u002f3O3VR0YPTQplySODvJx3WPfpXVxdRRPHJ1DDbfTsoqgIOTVXlE4QBImiSEyULwDMnjFo45bs1MHh2edLFsxNZapoEASmDI9fs8N2pxVNdIh3RHf6BABgUnRkTnWNp7itk35QgH9mY8HZxmt1BiUAEu3ilLeicJMKX39s3JwJ\\u002fTcfyDmUfcPJZr5jEASSowOmjkwYOyTKQeRXn8Jx+RdhziGttwEA5Q\\u002fjyn7oxVnQXYPwuLLvrOp3cP1vbJV9+uovJeRzn3kobfaE5D+O5+05VUhtxdolceE+cyf0H5sabbPxHxncvVv2Pr3I2FFxzOAAKkHPbKcTrCjwubMG19a1zJ09uLOKJmN43Nqd5xz30DkffkVztLhkZkI8Y0dgGOWd5Cf0iJL2cMQt1N\\u002fjnacmPDd3+PGLt05eLM69WdWDBpevl0tKfNCg+KCU+CCZ\\u002fW1c\\u002fiQQMVf2s1k5AxNMsrdkaB+DcVw\\u002fAcQN161ma+yAkA5vlF6BIMmi8obLhZVFFQ2VdarGZp3eaLZYcAGfKxbyfDylof4ecWE+w5JDve1vB\\u002fAXcl65\\u002f2zTnqVR33FsROt2zV1mt8n3xS\\u002fXG8sBQICJ3onbxFbfB+BfR48PCw5yF4koj9Xr6\\u002fobjJZrZXXXSurLa1V1jWqFSqszmI0mq9WK83kckYAnFHBFAq67qzhQ7hbk5x7sKwvxc78H73ASr0awHjrx3gLXrcZbf2YJeXIbYdVd+NSaZo2\\u002fzIUttYVC07o\\u002f7+bjwwd1lqAIEhsqjw3tYpGmexYSAAGEIHFb0wsAJ\\u002fG8llMDZGPZijs4zt4zng7\\u002fRI9rjtRvvKX9G45h9QoCLie3tj3Ii+WwHASOOolQwB0UFzQoztmIx3uWv9xbAQAmfhoTP82W2sKRw7pWq9h8Pu\\u002fRtIEtekOVSh3m5e4qElwoq4z19Va26sfEhh+7XuIvc7lSWRfj4zUg2I+1cKzcRUJJbjc1U7mqmzU6kznC20PI49C5mFloWvWmn3ZkIyiSkRYXGey1eke2yWwdEBs4ICZg\\u002fb6LVis+OS0uMsjr6IWi2gZ1vVIzbkh0TIicqWKXeBcM9cgY6mF3ima1vuikYpsDh+U4e8\\u002fgonxX1EuISdiK+9zhrVEj6XSZqrlbgaN\\u002fPrWlCpncVWh\\u002fS9d7jdoyhcy7Q4UtJmtzg9o70INhBRaT9dKRvNCEQN\\u002fQ3glJtdFVRBPj4xXt4xkp96hXaweF+CcH+epMZm+pJKe8xmzF86rqrDhxsOCWlM+7VtshapYFnatK1fJQSmJhTX2XuTQ6Y6vB9MiUQdEh3ufybgf6yJYuGDm8f9i+zEJvmSQlPvi\\u002fey8AQINSGxvm89KCUQfOXmep\\u002fjSKW6+wRfe5x\\u002fg2+5w7TxIg8qrWN6Z7JaR7JYSKezKaBAAH\\u002f3tq+aOrvnjm59Pbe\\u002fM2y96Xo6i0EdwPAFdOXntr2oofX9908rduBAFk7rxIOAzhdpJPHvuBKufDRd8xC8zem8uqsMlgalawR3s5XAzjYOcPdP2MZP7hVIUdtbAwFG1q1VObC1F7QedU1LgI+CiKjI4Ne2HTnm8XPoCTpNZo7B\\u002fkV9KgLKpvZO6pTUvoXCKs7Zso2seLynXnUGz8vFyfmZ224cDltKRQncHsdseRtxrNvh4ufC62eFrb6l8eriI+j0MSZGcVBQnkBeWBXNXxJnMNAOLK9RzoPi7d80FK+33xy4luwyMkyQfq1tYaSjgIL8V9wniftklVv9z+V1lrPpVelvgHlaDZVbPqljZHa1EBwHsFMyjhSO\\u002fZ4+QLqbTj7A4qZiaM55X7r6nPKU11OGnx5PuneU1PdhvFKqHHOD5rjUV5pH5jsTbXTBjlguAx8vlR0gGUqsuK1RvLjyu2VOmLTLhezHENEsWM91kk47X3BtQby4\\u002fV\\u002f1quu0GA1V8YOVY+P0TcFq+wtXIFDxXGugw5rtisNNVKOG793IaPkc\\u002fHEEd3aWd+vHDxmSGDP8s8Q0sK6hVclNOtwFEHTHt6XMKwqOWPrhowNmHPT8d0an3ckIgBYxO2fb7PYraOW5AWlhhEEuT6ZTvMRnO\\u002f4bHxQ6Nos4S06B3fHNI2t05\\u002fdrx3oMf6D3agKDpuQRoAHNl4xqg3jZw1JGkEu+k3eu7QCYuGA8CeH4+OXzRcKBFs\\u002fXzvA0vG0UesKqqrK29oqFJS2YuvlO\\u002f45mBNaX1iWkzCsA7zVbpL0si4\\u002fKybofGBMrmrUW\\u002fa9sU+ixkfN38YABz5NcuoN42cOThpRKzVbP1j1ZGACJ\\u002foQWEkQa7\\u002fcKfZYO43PCZ1Sn9Pf\\u002ffaMgUA6LUGOntYYodP6eIr5Tu+OVRTokhMjy6\\u002fVh2WGFh9q97VUxqeFPzH94dJEqY8NjIg0hccOywAoLYVCvNyp34uHjaAHtJevXgmAGT0i8YJklqm55PZHVZuivD2oCQxvl7MZTYfGTYAAOhcNimtbjp5qdhqxUmSHJYUumL98Wul9bGh8oy0uDW7zvl7ucWGyqHjd5891c7qb\\u002fOaT\\u002fWXjRnhPQtDuDWGYleOJyMflLXmX2k+keI+cYTXLJW5Toy50qqHQ97VWdWnGn6\\u002frDrCyNFGmuf0IR6TTyi21OhLHg59jxJKOTLawHF2BxXDEG6x9kqUdICvdxhJkmebdu2s+kbOD\\u002fIVhnUso50b6prbrQ0JboEG3JzfUhkl9RVgXCqRJAtmW9s\\u002fawPeuqb0LRThTPRdLMSkBS1nfi3\\u002f8OGQ9yKl\\u002faGrillJyy9l7\\u002fsIQx\\u002fwf4aDcJtMtcWtV4RYezezwlixpvQtuSBomv8SDsLNb8n85fa\\u002fFgW\\u002fQxUOALe0OcXa3BHes915PqWtVzMbd\\u002fIx8QivmXQJzhDp4QEA2RWVjw5s87NnyyupxLmmayO8+wFAl1GjDji2KevY5qz06SlWs7X6Vt0b654BgD++P+zhJ\\u002fML897y2d53Njx36Uief4TP+IXpANCsUNNmADDt6bGF2UXn9uWOmTtMpzY8\\u002fsEcD1\\u002fZ1dPXUyb0i0uN\\u002fO7lDZ0d1untF8qvVadM7BcSH3h6x4WIpGCBiH94QyZ9xOiBodEDw2Y8N5HKHtk\\u002fJCQu4KGXMzD7a2w5Sfr0QZs+2V1f3pg+fdDhjWc8\\u002fGR+ofItn+2NHhiWMj4xLjXyu1c2JI2I5fA4o2anZu\\u002fNAYBLR\\u002fL9w+XUuTNhZn9nQ4eZXncqPAXjYBcP5Rl1JrPRUlfeUJR7e+qTY70D3b9Zuv7l7x+HLh1W58V8O0sc+B2a7uYKD\\u002fAM8pGhCEKNl3\\u002fw7BQrTlCxDu89OZFOL5zS1sf\\u002f\\u002ftOTWCqKan3x1eaTo7znjJUvoCSxLuyJ0OW6wuciV3rxbYwicRCuK9dTzLE97EBlEWISDOH4CkLZaofZHVcMQ7Anwj6kfwaIIj6\\u002f+fRtXaEDh4UgiMZqEHJ4e2pywiXyG5qaJpOWSth0WPbO+mzTbo1VtTTqO3eeDwDEuKQ0FFceV2y+47AcVUxrUelxbbLbqDiXVACIkg4c5jmNNgaAY4rNXJT\\u002fWOgyLsoDgHjXoWtK39pft+Yl6SrKQGdVPxb6nzBJPwCIlg6s1N0s0lzqrsMaGxEOALMS4qfHtT35mbfLqcQ1dQXlsO6G9AcHDRybiKBIs0Lt6d\\u002f2LtdrjfIgD56AO+\\u002fVaQCg0xhcPdp7GGmz41uzmxUtgVF+uJXwCfF69F+zf\\u002f\\u002fqQMrEJACQyV35Qh5J2PgsGjl7CNXCIkny6Kas2lLF7Jem7F19nD7ilZOFrOzO7kTWFVKZ2NBqLMopm7BoeGH2LXmQ550jXrtzRHaYgU5rcPWwMRiq1xjo7GwdtG+ILRDxCYJAUITDwXRao0gqwDiY5U44Z\\u002fuz3TOO3iw5dP2WGe+FUDoWXA7G3MCC6YZsRmlRsFSlrXkAMNB9PFPIQi4I7vzc9jXOVIzGlevFRXl63NG6cThJuHFFV1TlkVIfvdXUzy2ITrBNAcD+WZdor\\u002foIgilvBQAIIEGi2FpDqZW0dDQE6FQxN553iDh+b+2Pe2tX1xhKOtoCAJS15kdJB1DeiiLedajSVNdsbuvN5KNCyltRePD9tNZm+me3WNQ\\u002fmU6vmDyRSog5go+ubV5dsn91yX5a210EYgHS6XU7fkFaXubNwuxbzYoWABg8KenEtnMblu04tz+XacbhYLWliuIrt1EMLb9efXjDaYvZ2mVo0clt56g+LARB\\u002fMK9jXqTi7uEdUQWkf1Dfn532\\u002fXzxWwFAJBmtsQhsYPD+QIeiqHjF6TlnblRmH2rc19VbZni0PrT+Vk3Lx7KGzwx6cRv5zZ8uPPc\\u002fivVxXVHN2VdOXX98rECB9kBIDI55Od3t12\\u002fUOwb6oUgiH+EHCfISYtH\\u002fPeD7T+9uXnEjBTKrBfisC5V1qzNvjwiImTewH6dW1J\\u002fLQfr1mU37f1PwnbU1vKSAPB98cuuXI9FIe+yFQyOKTadbtjeuROKYmf1N7dbC\\u002f8ZYzfyzWZ2xxUjgcxtPl7QktVkqjHgrVbSQpA4s3eMYl\\u002ft6ryW03QcFk4S1KxGan41M8HCwVl\\u002fUbSkxWxjOsEbsb9IOG5dVgwn8RzV0YuqQwpjhY8gZLzPoihp2\\u002fxTK2n5T+FD6Z4PTvRdTBeb15K5veqrJeGfBoiitlauqDeWvxTV1toCgO1VX1Xobjj42zrA5mYfGoveSra9XN15NloBd4nVgnMYm6SyfrYJzVbOndB2q9mKoMjdfLjZPASFxWTl8m18Qpnq+2GC8agwA+UN70GojYMjMrFnZk8OdipMkiRJkOidhghb7YCjG07\\u002f8t4WXYt+4ISkN9Y\\u002fzxfxAeB4USmCwPdzH6hQNtdrtH6uNj5\\u002fehG1Ums14x6+bmyFHQSYBABarS0uXA+27i\\u002fFccWO1G\\u002fIbto7ynvOBJ+HpVx3Hsr\\u002f6NoitlEnKG8FjPnVNr2VY0SYRCSUTPJ9jCWnQii6rBiGYIM9Jg32mFRrKD2h2PJr+UdPhn8cJIoBAA7C5aPCVrzD27XV2gIAYk5bDxpy101+miU7d6+Z9SCVfn733m+nT11dcmBJRFt8ybbKU3ODRt2x7YDN+5yJlbRiCMaau24lrRqL2p3X4WpSTyZLRXsrOm0lrYXqPH9hoBe\\u002f22P\\u002f9h5+AOj88LdBtuKGP3DDH4C6YPwJqHAKyk933hU4OCITe2b25ACwXfHd3MAXUQTdVPHF\\u002fKCXTzX+obGoHvR\\u002fCsHa\\u002f9TO3h+GVuPXz\\u002fzUWKXUaw1ndpw\\u002ftO4kJfeQiMqUzRsu5IZ5uvfAW53ZnePMWCZNWWF1oc1Wrh3CxAkAkNt8gq3oPbiowEya2NKucFyxIs2lYFHsaO+5fsJwKUemNNWRcLcNYSeJkPRvNjfIBcGh4gTmP2qozvmK+QnD5wW\\u002fAQAVuhu0MELav1ibYyHa\\u002f1zX1edkPDlzGLHXIUiyWq2xEgTTwajNOrqpxcTefU5hJa0bytftqdm5s\\u002fp3phwAzIRJbWn3xTnNFwmy7cZmqTqDIRiKYHktV9gKgBuaa1\\u002ff+mxb1aaLqm7ENDCP7ghCgxu2W1SPmxSDreo3CdOZXlwoWWfVHqjbsL9uQ6OpNr\\u002fl7MmGnX\\u002fUrC5tLTThhsP1m\\u002ffXbagzlrOyREgSb+uu6awaKccNJy0JLkM6313OutWmGpXZ2N6LUXe7rdPhRn1DqLtMbTTabIF3RqcxbFi+G0GRcXOHkiS5Y9WRmjJF4tCooGjfvWtP6TT62JTw9GkDTv9xKXpAqE+w57aVhx56YeKGT3abjJZ+aVF8AQ8ASvIrK2\\u002fVjZndIXzBJsHiuDiX1BOKLVqrKlyShALaYKomSWKk92y2aSdIII24zoTrdVYNADSaaoSYmI+KmF0wABAkir6oPHio7r+R0v5mwijjyX0EIV1md1wxuSCkWHulQJ0l43orTJXnmvYxY0QJkjAReiOuM+CtJEk2mWoEmFiAiXtl6k+614OF6rNry94d6jHVjeupw7U1+lsCTDJGPg+6qlix9spl1ZEIabIb19tKWq6psxEECRUn0Abj5At+Kn193e33Uz0yuCgvr\\u002fl0lb5ofvAbtEGvcKGq6qus7MJ6xciffgYAo9U6JSaKi2Jqiy6zIT9C6l+hUyiMLTY3obB3n1NcUJ5LcO03QDYIAHRW3anGY3qrIVwS0c8t+bjiiFzgEyoOA4AKffmR+kMKoyJKGh0iDqNVJJC7a3ZaCHOUNCZMEn60\\u002fpAO143xHu8vDJBx3RuMtsMSB7unDvMcDgAnG44N9UgXYIKDdftGe489WL\\u002fPSuBDPYbVGeuaTI0qs3KQ++BoaSzz6BES52IaiBZc\\u002fxuu\\u002fw1BZahgEirIQPlDgLVXRTfJVh5I9ZjoxvX8o2a1F98\\u002fUBSRLpq6u2ZNvbHChevuwZOfbNi5IOgVZpZ41yEnFNtV5oZ41yFclO\\u002fJ94UeOyxPf3eegEtfy5D4QCqRGhLUajJXtaid8VYAoG3W6TSGx96b4eHjBgDBsf5zXpiEcdDmBk11Sf3rPz5BmTXWNgfH+AFAdXHdpeOFfmHy8fOGAsCV0zduX6+5fb1m8dvT2wsFAIDaUsXvn+8GgPlvzfQO8qTlc4NePdu0+2rL6VzVcRTBZDzvYZ4PtGezT35L5vaqr+mf39x6HgBEmPStuA20EAD6uY1oNFVfaT55XrlPiEkn+i6mHFaX2R1UbIrfE\\u002ftqVu+p+REnLX7C8JkBL2Q27qCLOq\\u002fcf7BuHf1z5a3nAcCd5\\u002fNy9A+0sMcIMcmS8E+PN2w92bCt1doixCQ+gpB0z7a\\u002ftuOKyXjeZsJ4TLHZiLfyUZFcELww+O0AUSRt4Mn3fyr8k6P1G\\u002ffW\\u002fEgA7isIeyT0\\u002fQhJMm3QLexd8SGBgVvnz31l34HPpkwCAARBqJvzpehZxxS5+2ov+Ahkr8XOoe2Z2LvPKRTG+uFeI6k0TlrrjXVPhLYFK6S4p15tyaHSwaIQf2HAJJ8pVAclrSpU58sF8qEe6ZTZKO+xxdpbV1ty\\u002fYUBlMQml5ov1BhqEl37+QsDLjdfCBIF8zH+2aYzblyZN19+oH5viCgsRBw6Tj5hc+WGaGks6+jdgiSacf0WXL8FQT1QwSRUmIHyBjv\\u002fHcbETJj4qBBFMJy0AoCU48ZFeSSQRtwg43lxEN6oToO\\u002fIkxiIgxV+pKBstEsFY2zDksoEbz80zPr3tms0+jHzE+fsHgUJa\\u002fXaKua1a1ObI9O4RPsufjt6b9\\u002fezhlXOLA0XFML+d5Z1E3AEARBMcJ3EpYLbhe22GEuLlRgyIINUOPSfaui\\u002ft+OgoAk58Yy7x9UQQb7jVzeKe\\u002fDsVzkV+xRXdIchuZ5NZ2dzoAAWScfCGrOxycyO6gYlKOjNXumBf0Op0e5jmNFS7QXRycNQCIOC7T\\u002fJ6e5mdjbpfjinny\\u002fReH\\u002fouhtIE3P3Bh8NtsKQB0LIpiduDLLAkTe1ec4pnUwazNt7koNtm3bbDJHvbucwpPvmedsZbuaZJx3ZnaLjHgBgmnraf\\u002fvDJbY1H7CH0JUxdfYSmyIVQLiwTynDKrwdgw0Wfyqcbj7jxPDsqd7DPthuaaC9eVi\\u002fIcr3jRLUhCies34fpNCOqFCiahwqkobxD7kXNIivuYw\\u002fVbeCg\\u002f0XVoo6mWlg+QjTyu+M2dLw8QhgOEtGcAAIAgUVS9sdJB36uzDgsAxj08YtzDI1jCclVLQW39rOR4ltwe5TdqsvbmWs1WIEkAiEwKXvuf7enTBvqGdIgCjU0J3\\u002fLF\\u002fqBoX4GInzIu8bvXNt3MKYtKDuELeQNGxvoEe274dM\\u002fitzo0snKP5zN\\u002f3udvj+MrHuXJdmFOYvM+pxjiMWxj+boS7S0JR5rqMYyWN5gUWU2nawzV\\u002fsLARNckAAgWh2yv3jZQliLlujBVmyrWl7WWhohDMQRrMClMhAlF0Hpj3TllVoNR4Sv0i3dJbD8eAABcVJ2vMlSGisMGuw\\u002f14ss1FrWYI0n1SNtXu8uT7x0iCmXZA+Po4ZL25m3PIIlGXL8R129EMDkqmIwKpqK8\\u002fs54Lm9+wIyAp0mSRBGUDoSdE\\u002fg8AMwOfA4nccxWAzDFfSydRhFshv8ShhLg7sMazpZVVDarm1p1L4wcytbZwWq2IiiKcdqcqMVs5dpawMxqwTEOSse\\u002fORgNBQCr2TrD41GjzgQA31\\u002f8JGpQONviPn8v\\u002fsIrbu9hY2ElrRxbk4ro7PYMnMdBTRwUblE9TpizuxuNRYNgPqhgCirMQLn92bq+x\\u002fYpOU9aWHAaW9YFzGFdALDpraDT8KcDbwUA17KLqHv3Pv9P+AuvuD0fwcKev6Cz2zNwHgc1cVA4130dkDrCeAI3HiZMp4DUsy0cQuL1uG4drluHYP6oYAomzEC47bG+fY3ds\\u002frfwvHXwX3+fty\\u002f4ncFIkaF01DhNCBNhOkMYTyMm44D0cI2cwiJ1+C6NbhuDYIFosIMTDAF4baPCPcRfweHZTKYLx\\u002fOY0vv8\\u002ffl\\u002fhXvNRA+KhiHCsZxACdMFwjjIcJ0lLXHapeQeBXe+iPe+iOCBaHCqZggA+HS3Va9jN0+rPmBS5pqVGzpHb7KXJaQHgMAhXWK87er5FLJtMQYttEd9v14ZOU\\u002f1gAAX8jbq\\u002f2VmodlNVsvH7566rfssvwKVV2zTq0Xu4k9\\u002fd3j02JGzh7ab2QcuxQGjdXKsryKsrzy0vyKsvyK6lu1nWdg2mOPZqOTy6Qpa5vP7rp48WBuTXFds0JtNlrcvFy8Aj2SRiekTU+JTolgZ7BFa4tuhvujVHrZnjdTpw6kVZcOXT217WzRpZKGyiaTwSyUCLwCPcOTgpNGJQx7YJCrV9dRuLgVv3ry2vl9l0uvlteW1uvUerPRIhDzxa4i3zB5YLRfQlps0uh4rwAPdk6H9MqJszAbLblH8\\u002fIyr1\\u002fPLlLVt2iUWpPezBfxpDKJPMQrKDYgLjVqwLh+HoyRYiZ9d8WdvM97RkHm9QsHr+SduqasVakbNVw+193HLSguIDVj4LDpKS62Zgiz6PVnxwlIwnKVMBwmTIdJawVb6RwIJxQVTMWEGQjHuUAwp7lbh\\u002fX7lcI5\\u002fRN259+YlhhjLxSL\\u002fqMDwMbS731CvYtzyj555JvKGzUdDdtJHB771qal9p60RaH\\u002fUFQ0sqXO4fj2pdBrDJuX79z59X6LqT2GkMXgyf2f+fLRwGg\\u002ftqIjTIf10o9LMp4eBwC1pYoVi7+9ll3EtGQy7uERb6x\\u002fgS3tyNldF1e\\u002fvrG2pJ6t6ERCesyi9+YMHN91R0MvnjiNSW\\u002fa9d2hnV\\u002fvU9W3sHUdQRDkgecmPv\\u002fNE2xFX15xJ+\\u002fz7nLt7M2fXtt44\\u002fwttuIOIhfhgrdmznwpg8t3FO7b689OtyAtNwnTYdx4mLTcZOucA+FEoIIMTDgF4dzteCWF3U\\u002fCiP6hPAFX3aTVqR31yUV6e\\u002fxyPsdLIrHnrVhUXK+uK1O8PeUjq8O9kgrO3Hh+8JvfX\\u002fqUXpfjT6OxSvnmpGUObgiKiwevFJ69+d62VwZNTGbr7KCsUwFAyZXbb05cpm7SstUMhkxpb4jZZPNHO395bwtbaofCrJsOHBBNX5x4baniP7M+K8t36kVNkmT0oJ403+4GJ+\\u002fzbrHr24OrXv7FcRtQrzH8\\u002fNamc\\u002ftylu1+Q+reHmbogD\\u002f\\u002f2UG4MRg3BpMsJfFKwniIMB4hzFc6R587gLSW4K0r8daVCCcKE2agggyEE8Y26g52HdayPW9SCdyKq5u0GqX23anLO7\\u002flDGaLmMcrVzWz5PY4s\\u002fN89u5L1F9cJBUOmpgU1i\\u002fExUOq1xpK88qzd18y6duGflT1LZ8+8u1nx20EIv5752usJ\\u002fDfMz+jX+CvrXsuMMbu+1\\u002fQaS4rE2Vt89K0dxqrlbQkNjWq34hYD193FENViubCrJsFmTeoZqleY3hv+qdfZS6LGezUY6asbW6sUr6T8THlrRAECUkIDIz2k7pLTAZzbUl96dVyk8GMcbBBE5PYmRlcPnyV6a1cPKSDJiYFxQRQ931rs666uLY0r+J2fgVVT69Aj8GTuxiB7osTr7pZ82LaO63NOqZQJBVGD46QyV1FUqFea6gtVVRcqzK0GgFA7CoaMcd2cEzfXXEn73Pn2fbprp\\u002ffals\\u002fAwA4PE7SqPjY1EiZl6tRb1ZUNFw6dLWurK2T6NrZmy+PeG\\u002fl2Y\\u002fEriI6iz165dnpGQgWRO0TQRINhPEIYTxEmC50a+Ihab1l1d4C7VcINxYTUJ4rmG3kBHYdFg3Gwdx93Nx93HgCG23X7gaOHv7lJJWY8tS4pz5ZJJGJmdpmhXrZ3C8LMq9TP6+eLLx+7lbcUPZncER\\u002fdrAcs10dkhDYs6gckiQ\\u002fXfwt\\u002fdAGxvi\\u002fueGFzkWV5ZV\\u002fOO+rqqJaALCarcse+mJNwZciqZBl1hlFRePyRStV9S0YB5vx4uQ5\\u002f3zA3bdDl43ZaMk9ll+cUyZx6\\u002fBnYbFtxW46PfGx0S989ySfsZcyjbpRc\\u002fHglRNbshLTY+nVOWzSFyeu1xr+NfMzpreKTY16+P05A8f3Y1UGt+I3LxSf2XlB7CKyeSLQZ1ecieP73EkKs26ue7f9XZIyKXnpD0\\u002fLO+6nSZLk8V\\u002fPfPfiWqpNV3G9+sunf3xv2ytMG5v0yrNzlyCoNyZahIkWAaHGTccI42HClAWkkW1nH9Jyw2q5AdrPEW4CJpiCCjMQLJBtZB9H97EzBMlcE\\u002f18sssq2QqHzH5l6ss\\u002fLWH9xQFAJnf9YNfrzMf41LazDH3fcmjtiSvHC6i0X7h8ZdaHNh+DsKSQz0\\u002f+h+4Ub6hs2vvDkY4mtsk5kldw5oZAzP\\u002fk0LtLPl\\u002fM8lYAwBNwU6cOfPhfc1hyJiRJFp5t61AQu4qWrnrK3kPu6uUy\\u002fpGRyw++s+CdmWxdR\\u002frixDd\\u002fvLPqZvvX5YylU1ZmfZgyKbmz68Q4WHxazDNfLHZ84vc+JEF+8eQqevWRIRkDlu15k+WtAABBkHEPj\\u002fjk0Lu0Z8z8\\u002fVzm784uxnCvPDuoKyacxZWt5ssvc2Xfo8JpgDj1YUtDWgqt2hXmhpHmpgdx3RoS76IvgoJ993QXb4lk\\u002fsB+GfHRbIV9\\u002fCN9n\\u002fh4IVt6B4mbeNqSCfRPBz3TvQtJktu\\u002f2kv\\u002ffPH7pxz0LLj7uM19bTr9c+fK\\u002ffbGLjrz4vdPJY\\u002fpebiKQWu03lku1sVD6rjL1hn64sR1av3eHw7TP4dNT\\u002fnHV491XqXzb8a5vZerb9VRaZFU+MqaZx2szBczJHLeGzPon1tX7GrX2edefHYQESqYzHVbyZfncN3XYaK5CNq9\\u002fn7Skm\\u002fVLDc3DLc0zcR160jc0TjSXTmsmhbNuvM56y\\u002fk7sy77vzEy+nPTWIFu7NIGt3+gakob2Bo+pCCzBt0f3NgtN\\u002fACY56kQBg1Nw0Oq2qa75d4FQbM6xf8PhHRrKl3UEoFdBLytXfbijLK++g7j59ceKntmXrNQYqjXGw5799oqP+78mBn4\\u002fR6bGLhrv7uLXrbDFj6RT6fVOcU+ZgSJHm3nx22kC4KH8Ux3U5T36B67EFEz+KYP5sG4cQlqtWzYfmhjSLcjau+4WtBoC7dFj+bi6LUpIWpSS\\u002fOjbdyVFCAEifOYQt6oh3oCedbm3ptbEbx1w52fZNBADOjH95BXow42gKs5wa92XN\\u002fu8BCILQXfIkSb415eNzey53NOkefXHi+ZnX6HTq1IG9MsR+j0MSJPNPMXxmKkNpG4mbeMC49qnOV08UMpS26Ytnp7ZJwxZ15ML1Cur\\u002fosqG305crVN2YQ+AorwhHJf3ed5neJ57MclShNut0DCSMOdaNcvYYgC4S4cFAPG+ctYiHo5x9ZR2efsKGFEzuBXHrd0YjOgx1862t5+DYh2tT0Tj6tn+3FIhC10Sn9aToB4WC9+ZRc+sVNU1v\\u002f\\u002fgp0uSXz2w5hg11tZd+uLEmWUmj+759+\\u002f\\u002fEOXXqpiBEZEDwhhKuzDNCrJuMDQ26Itn50aFYs2ec6U1TReuV+w4lX\\u002fxRmVBWd2uzILr5e3B7kUVDQRBFlU2RgZ6TUqNOVdY3p6\\u002fKxBuPEe6lOe5j+edzXH9GBVMAITd+9YtHDUv+wK\\u002fCF+26N6gtrT9y3nls6tXPruaoewajbKVLbJF5y7YHhA5IOz1\\u002fz6\\u002f4tHv6HicsvyKr5b89MMr\\u002f02bMWTCIyP7j010fpenXj9xkiSZ4RGdB\\u002fj+ljRWNdFpVy+Xzp3iNgmIao\\u002fGqC\\u002fvIpaiL56dqECvyECvcH\\u002fPq8W1127XoyiiN5oXThi44eCluBA5ZdMvwm\\u002fr8SvJkf4oguzLuvbA8J68gRDMBxPNw0TzAHDCnEeaswnTOcJyFci2rgMn6UbjqFcQudgeBf\\u002fLYYULdRezwcwW2ULi1nW4jTOMnp++6vKKxBEdWtpGnen4r5lvTFj2cPhzW5b\\u002f0dri1Bn1+onr1HrmOv3M5tjfGK2q3XE7E1RFIWY8DswSbNIXzw6Gokq1rrxOpVTrHkiPb2xp9XQVHzh33UvWPvCSFOF\\u002f4XpFYpjv1mNXmrWGW5VdONauwFDeAEzyPNdjI89jGyac1a1mUzdMewV7Y\\u002fB\\u002fOXQncZ\\u002fSeVC\\u002fx4QmBn156j9XTxRuXbEr92g+c7ROUd647p3NWz\\u002f5Y86rD8x9fbrjkcReP3FWyLjQTqDW3wwDY60b529y5pY8ek0XXU7OF9stnpuVjiLI09OHAkBypD902iENQWDl0hkAMG9cF+HHTkCQluuE+TxhOkdYcoDosjuMzZ\\u002ftsO5Z+CIevb7S7H9OC4ru3gBHQFTvN9edIXlMQvKYhLoyxcG1x4+sP6WsbaZVeq1h\\u002fb+2nf793LLdb\\u002fiEejMydaDXT5wVWU6HX\\u002f+9YbaVnD9l5pJezrfLepfOw2WdJXcJidcQpkzClEmYz\\u002fXASTG577DacPGQ0ndPXGrU8Fldj\\u002fLcO\\u002fiGyR\\u002f\\u002faMGjH8zLOZq\\u002f54fDF\\u002fbn0BPZygsr35jwwarLK+w9D71+4lJ3CYK0T6rv8kvn7wEzeM35aYlMSwfhb\\u002f+bkIQljzAeI4zHSGvXERudsO00e+0L5X8d5viLoqvuz3sTFENTJiUv2\\u002f3GzwVfDZ4ygJbXlip+\\u002f2IPw7ADvX7iKIYy18Zxcubz\\u002fzq+YW1d1ABAzUlkKO1SWdQe3t0rAzL3ADhhPmfV\\u002fNvckGZpmom3ruqmt0JQbn+Oy7s8b9th+vcdVhsJw2PpND335c9HUW0jSqC7BMX6f7TvrclPjqUlJ7favvzQNyfOnMKWf6aL0fq\\u002fB\\u002f6RvsxI0eKcsnadfZhm8cN6IeTlL4M0E6aTVv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                       {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"word_cloud\"}, \"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('3242f25f-752e-4b21-983d-a31cbaeda77f');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
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+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7I4oINQQEVHB"
+      },
+      "source": [
+        "<a id='chat_gpt'></a>\n",
+        "<a name='chat_gpt'></a>\n",
+        "## 2.&nbsp;Extract Features Using ChatGPT\n",
+        "\n",
+        "Imagine the following situation: We are building a model to predict the severity of accidents based on features available in tabular form. We believe that knowing the number of vehicles involved in the and whether someone was injured would help improve the model. However, all we have available are accident reports containing the information in unstructured free text form.\n",
+        "\n",
+        "If we have sufficient data with labels, we can use supervised techniques such as examined in Part I of this tutorial.\n",
+        "\n",
+        "In this Part III, we learn an unsupervised approach that does not require labels.\n",
+        "\n",
+        "More precisely, we will use ChatGPT to extract the following information from the car accident reports:\n",
+        "* Was someone injured or killed?\n",
+        "* How many vehicles were involved?\n",
+        "\n",
+        "Let's get started."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Djm3pEdfEVHC"
+      },
+      "source": [
+        "<a id='chat-gtp-api'></a>\n",
+        "<a name='chat-gtp-api'></a>\n",
+        "###  2.1 First Steps With the ChatGPT API\n",
+        "\n",
+        "\n",
+        "The idea is very simple: We specify a number of questions and ask ChatGPT to provide answers based on a given accident report.\n",
+        "\n",
+        "The prompt might look as follows:\n",
+        "\n",
+        "```\n",
+        "Read the following text, and answer the following:\n",
+        "1. Was someone injured?\n",
+        "2. Was someone killed?\n",
+        "3. How many vehicles were involved?\n",
+        "4. Summarize your last answer by a number.\n",
+        "Text:\n",
+        "V1, a 2000 Pontiac Montana minivan, made a left turn [...]\n",
+        "```\n",
+        "\n",
+        "The response might look like:\n",
+        "\n",
+        "```\n",
+        "1. Yes, the driver of V2 sustained minor injuries.\n",
+        "2. No, no one was killed.\n",
+        "3. Two vehicles were involved.\n",
+        "4. 2\n",
+        "```\n",
+        "\n",
+        "So all we have to do is to extract the desired features from this response!\n",
+        "\n",
+        "We begin by writing a short function to call the OpenAI API.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "@retry((openai.error.APIError, openai.error.ServiceUnavailableError), tries=10, delay=15)\n",
+        "def call_openai(content):\n",
+        "    return openai.ChatCompletion.create(\n",
+        "        model=\"gpt-3.5-turbo\",\n",
+        "        messages=[{\"role\": \"user\", \"content\": content}],\n",
+        "        temperature=0.2,\n",
+        "        max_tokens=256,\n",
+        "        top_p=1,\n",
+        "        frequency_penalty=0,\n",
+        "        presence_penalty=0\n",
+        "    )"
+      ],
+      "metadata": {
+        "id": "uxKexE7OueWd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Note that we have used the `retry` decorator to retry 10 times with a waiting time of 15 seconds to handle some of the most common exceptions (you are invited to develop more sophisticated ways to deal with such issues).\n",
+        "\n",
+        "The parameters have the following effects:\n",
+        "* `model`: Specifies the ChatGPT model version.\n",
+        "* `messages`: Specifies the content of the user prompt.\n",
+        "* `temperature`: Values in the interval $[0, 1]$. Controls the randomness of the text generated. A higher temperature results in more diverse and creative output, while a lower temperature makes the output more deterministic and focused. For our purpose, we require fact-based answers and therefore go for low values of temperature.\n",
+        "* `top_p`: Instead of considering all possible tokens, GPT-3 considers only a subset of tokens (the \"nucleus\") whose cumulative probability mass adds up to this threshold. With `top_p`=1, we allow all possible tokens.\n",
+        "* `frequency_penalty`: Penalizes repetition of words in the response. For our purpose, we don't mind word repetitions and therefore set this parameter to 0.\n",
+        "* `presence_penalty`: Encourages use of a diverse vocabulary in the response. For our purpose, this aspect is not important and therefore we set this parameter to 0.\n",
+        "\n",
+        "You are encouraged to experiment with these parameters.\n",
+        "\n",
+        "Please note that the results may not be reproducible between runs and model versions.\n",
+        "\n",
+        "Next, we specify the location of the API authentication key:"
+      ],
+      "metadata": {
+        "id": "RGBNLjDauh90"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "openai.api_key_path = \"./openai-key-at.txt\"\n",
+        "openai.api_key = os.getenv(\"OPENAI_API_KEY\")"
+      ],
+      "metadata": {
+        "id": "g7vgKfVMzebY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we are ready!\n",
+        "\n",
+        "We specify the following prompt ..."
+      ],
+      "metadata": {
+        "id": "Ah1X_jCzz0Y4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prompt = \"\"\"\n",
+        "Read the following text, and answer the following:\n",
+        "1. Was someone injured?\n",
+        "2. Was someone killed?\n",
+        "3. How many vehicles were involved?\n",
+        "4. Summarize your last answer by a number.\n",
+        "Text:\n",
+        "\"\"\"\n",
+        "prompt"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "aPQSgdAG0BTM",
+        "outputId": "973a26a4-3eeb-40cf-b28c-400ff583cc82"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nRead the following text, and answer the following:\\n1. Was someone injured?\\n2. Was someone killed?\\n3. How many vehicles were involved?\\n4. Summarize your last answer by a number.\\nText:\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "... and apply it to the first English accident report:"
+      ],
+      "metadata": {
+        "id": "rf9pctAV2Dyr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "response = call_openai(prompt + df.iloc[0][\"SUMMARY_EN\"])\n",
+        "response"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s0ZTa-0b0keR",
+        "outputId": "f972af7c-5014-41bf-fbb5-c8fcb461d88f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<OpenAIObject chat.completion id=chatcmpl-7vkj3d4NvHiu3Uy1BUIQyqVRp7FV2 at 0x7f6200275f30> JSON: {\n",
+              "  \"id\": \"chatcmpl-7vkj3d4NvHiu3Uy1BUIQyqVRp7FV2\",\n",
+              "  \"object\": \"chat.completion\",\n",
+              "  \"created\": 1693998665,\n",
+              "  \"model\": \"gpt-3.5-turbo-0613\",\n",
+              "  \"choices\": [\n",
+              "    {\n",
+              "      \"index\": 0,\n",
+              "      \"message\": {\n",
+              "        \"role\": \"assistant\",\n",
+              "        \"content\": \"1. Yes, the driver of V2 sustained minor injuries.\\n2. No, no one was killed.\\n3. Two vehicles were involved.\\n4. 2\"\n",
+              "      },\n",
+              "      \"finish_reason\": \"stop\"\n",
+              "    }\n",
+              "  ],\n",
+              "  \"usage\": {\n",
+              "    \"prompt_tokens\": 618,\n",
+              "    \"completion_tokens\": 33,\n",
+              "    \"total_tokens\": 651\n",
+              "  }\n",
+              "}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As you can see, we received a chat completion object, from which the response is easy to unpack:"
+      ],
+      "metadata": {
+        "id": "spUqVn8U2cCw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(response[\"choices\"][0][\"message\"][\"content\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "44Hmpy2W20bU",
+        "outputId": "8214f583-24f4-4f92-9f60-5b7e946484ec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1. Yes, the driver of V2 sustained minor injuries.\n",
+            "2. No, no one was killed.\n",
+            "3. Two vehicles were involved.\n",
+            "4. 2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Indeed, the text says that \"[the driver of V2] sustained minor injuries and was transported to a local trauma facility\". There is no mention of a fatality, and there were two vehicles involved, namely V1 and V2:"
+      ],
+      "metadata": {
+        "id": "JlYtHQzM26wS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(HTML(df.loc[0, \"SUMMARY_EN\"]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138
+        },
+        "id": "GOKidim53JUS",
+        "outputId": "73288c85-ae70-441c-f13c-4f2261265f22"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.  The posted speed limit on this roadway was 80 kmph (50 MPH). V1 entered the roadway by crossing over the two southbound lanes and then entering the third northbound lane, which was a left turn-only lane at a 4-way intersection. The driver of V1 intended to travel straight through the intersection, and so he began to change lanes to the right. He did not see V2, a 1994 Pontiac Grand Am, that was traveling in the second northbound lane. The northbound roadway had curved to the right prior to the private driveway that V1 had exited. As V1 began to change lanes to the right, the front of V1 contacted the left rear of V2 before coming to final rest on the roadway.\r \r The driver of V1 was a 60-year old male who reported that he had been traveling between 2-17 kmph (1-10 mph) prior to the crash.  He had no health-related problems, and had taken no medication prior to the crash.  He was rested and traveling back home.  He was wearing his prescribed lenses that corrected a myopic (nearsighted) condition.  He did not sustain any injuries from the crash and refused treatment.\r \r The Critical Precrash Event for the driver of V1 was when he began to travel over the lane line on the right side of the travel lane. The Critical Reason for the Critical Precrash Event was inadequate surveillance (failed to look, looked but did not see). Associated factors coded to the driver of V1 include an illegal use of a left turn lane (cited by police) and an unfamiliarity with the roadway. As per the driver of V1, this was the first time he had driven on this roadway.  \r \r The driver of V2 was a 28-year old woman who reported that she had been traveling between 66-80 kmph (41-50 mph) prior to the crash.  She had no health-related problems, and had taken no medication prior to the crash.  She was rested and on her way home.  She does not wear corrective lenses.  She sustained minor injuries and was transported to a local trauma facility.\r \r The Critical Precrash Event for the driver of V2 was when the other vehicle encroached into her lane, from an adjacent lane (same direction) over the left lane line.  The Critical Reason for the Critical Precrash Event was not coded to the driver of V2 and no associated factors were coded to her."
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can use the same English prompt and apply it to the German version of the accident report. The response is in English:"
+      ],
+      "metadata": {
+        "id": "CnI4fYcIyDrD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text = df.iloc[0][\"SUMMARY_GE\"]\n",
+        "display(HTML(text))\n",
+        "response = call_openai(prompt + text)\n",
+        "print(response[\"choices\"][0][\"message\"][\"content\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 243
+        },
+        "id": "Fd64ys9AyAiy",
+        "outputId": "9bf74be7-142b-4d0e-db8d-abb039a93c4d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "V1, ein Minivan der Marke Pontiac Montana aus dem Jahr 2000, bog von einer privaten Einfahrt nach links auf eine zweispurige, trockene Asphaltstraße mit 5 Fahrspuren in nördlicher Richtung und einem Gefälle ab.  Die zulässige Höchstgeschwindigkeit auf dieser Fahrbahn betrug 80 km/h (50 MPH). V1 fuhr auf die Fahrbahn, indem er die beiden Fahrspuren in Richtung Süden überquerte und dann auf die dritte Fahrspur in Richtung Norden einfuhr, die an einer Kreuzung mit vier Fahrspuren nur für Linksabbieger vorgesehen war. Der Fahrer von V1 beabsichtigte, geradeaus über die Kreuzung zu fahren, und begann daher, die Spur nach rechts zu wechseln. Dabei übersah er V2, einen Pontiac Grand Am von 1994, der auf der zweiten Fahrspur in Richtung Norden unterwegs war. Die Fahrbahn in nördlicher Richtung war vor der privaten Einfahrt, aus der V1 herausgefahren war, nach rechts gebogen. Als V1 begann, die Spur nach rechts zu wechseln, berührte die Front von V1 das linke Heck von V2, bevor er auf der Fahrbahn zum Stehen kam. Der Fahrer von V1 war ein 60-jähriger Mann, der angab, vor dem Unfall mit einer Geschwindigkeit von 2 bis 17 km/h unterwegs gewesen zu sein.  Er hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Er war ausgeruht und auf dem Weg nach Hause.  Er trug die ihm verschriebenen Kontaktlinsen, die eine Kurzsichtigkeit korrigieren.  Er zog sich bei dem Unfall keine Verletzungen zu und lehnte eine Behandlung ab. Das kritische Ereignis vor dem Unfall war für den Fahrer von V1, als er begann, die Fahrspurlinie auf der rechten Seite der Fahrbahn zu überfahren. Der kritische Grund für das kritische Ereignis vor dem Unfall war unzureichende Überwachung (nicht hingesehen, hingesehen, aber nicht gesehen). Zu den assoziierten Faktoren, die dem Fahrer von V1 zugeschrieben werden, gehören das illegale Benutzen einer Linksabbiegerspur (von der Polizei verwarnt) und die Unkenntnis der Fahrbahn. Für den Fahrer von V1 war es das erste Mal, dass er diese Fahrbahn befuhr.  \r \r Bei der Fahrerin von V2 handelte es sich um eine 28-jährige Frau, die angab, vor dem Unfall mit einer Geschwindigkeit von 66-80 km/h unterwegs gewesen zu sein.  Sie hatte keine gesundheitlichen Probleme und hatte vor dem Unfall keine Medikamente eingenommen.  Sie war ausgeruht und befand sich auf dem Heimweg.  Sie trägt keine Korrekturgläser.  Sie erlitt leichte Verletzungen und wurde in eine örtliche Unfallklinik gebracht. Das kritische Ereignis vor dem Unfall war für die Fahrerin von V2, als das andere Fahrzeug von einer benachbarten Fahrspur (gleiche Richtung) über die linke Fahrspurlinie in ihre Spur eindrang.  Der kritische Grund für das kritische Vorunfallereignis wurde der Fahrerin von V2 nicht zugeordnet, und es wurden ihr keine zugehörigen Faktoren zugeordnet."
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1. Yes, the driver of V2 (Pontiac Grand Am) suffered minor injuries.\n",
+            "2. No, no one was killed.\n",
+            "3. Two vehicles were involved (V1 and V2).\n",
+            "4. 2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, we want to examine more examples. We store the results in a list."
+      ],
+      "metadata": {
+        "id": "HOccrBgK3oeh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# reset results\n",
+        "results = []"
+      ],
+      "metadata": {
+        "id": "RWSxiwgD05bq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "It might happen that the following code stops, for instance due to temporary unavailability of the API. In this case, you can simply resume execution after a while.\n",
+        "\n",
+        "Feel free to change the upper bound of the loop. In order to run a large number of samples, you may need to switch to a paid scheme."
+      ],
+      "metadata": {
+        "id": "8M8dmb6X311g"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i in tqdm(range(len(results), 10)):\n",
+        "    text = df.iloc[i][\"SUMMARY_EN\"]\n",
+        "    response = call_openai(prompt + text)\n",
+        "    results.append(response[\"choices\"][0][\"message\"][\"content\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rlRJSJK40YWa",
+        "outputId": "c42ea9eb-a1a7-4602-9ae8-ddde9156355c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 10/10 [00:42<00:00,  4.24s/it]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# store the results in a DataFrame and export to a csv file\n",
+        "if not os.path.exists(\"./results\"): os.makedirs(\"./results\")\n",
+        "pd.DataFrame(results, columns=[\"response\"]).to_csv(f\"./results/NHTSA_responses_{i:04d}.csv\", index=False)"
+      ],
+      "metadata": {
+        "id": "k2ohc8bm4juc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8dNQ3zns4UXY",
+        "outputId": "6162da8f-d48b-4701-b423-c2df7810b611"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['1. Yes, the driver of V2 sustained minor injuries.\\n2. No, no one was killed.\\n3. Two vehicles were involved.\\n4. 2',\n",
+              " '1. Yes, the driver of V2 was injured and bleeding.\\n2. No, no one was killed.\\n3. Two vehicles were involved.\\n4. 2',\n",
+              " '1. No one was injured.\\n2. No one was killed.\\n3. Two vehicles were involved.\\n4. The number of vehicles involved is 2.',\n",
+              " '1. Yes, the driver of vehicle one (V1) was injured.\\n2. No, no one was killed.\\n3. Only one vehicle (V1) was involved in the crash.\\n4. 1',\n",
+              " '1. Yes, the 17-year-old male driver of Vehicle #1 was transported to a hospital and treated for a complaint of pain.\\n2. No, no one was killed in the crash.\\n3. Two vehicles were involved in the crash.\\n4. 2',\n",
+              " '1. Yes, the driver of Vehicle #2 and the passenger in Vehicle #2 had minor injuries to the head/neck areas.\\n2. No, no one was killed in the crash.\\n3. Two vehicles were involved in the crash.\\n4. 2',\n",
+              " '1. Yes, someone was injured. \\n2. No, no one was killed. \\n3. Two vehicles were involved. \\n4. The number of vehicles involved was two.',\n",
+              " '1. Yes, someone was injured. The driver of Vehicle #1, an 82-year-old male, was transported to a local hospital for a head injury.\\n2. No, no one was killed.\\n3. Only one vehicle, Vehicle #1 (2004 Subaru Forester), was involved in the crash.\\n4. 1',\n",
+              " '1. Yes, the driver of Vehicle #1 was injured.\\n2. No, no one was killed.\\n3. Two vehicles were involved.\\n4. 2',\n",
+              " '1. Yes, the 19-year-old female driver of Vehicle #1 was injured and transported, treated, and released for minor bleeding to the head.\\n2. No, there is no mention of anyone being killed in the text.\\n3. Two vehicles were involved in the crash - Vehicle #1 (1987 Honda Accord) and Vehicle #2 (1994 Honda Civic).\\n4. The number of vehicles involved is 2.']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a id='extraction'></a>\n",
+        "<a name='extraction'></a>\n",
+        "###  2.2 Extracting the Features from the Reponses\n",
+        "\n",
+        "Next, we need to extract the desired features from the responses. We write a few of functions to achieve this.\n",
+        "\n",
+        "The first function, `first_matching_expression`, accepts a string, a dictionary and a default value as inputs. The dictionary is supposed to hold a mapping from expressions to values. The function searches the expression which appears first in the string and returns its corresponding value. If no expression is found, the default value is returned."
+      ],
+      "metadata": {
+        "id": "zTSnboVA7omm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def first_matching_expression(string, dictionary, default):\n",
+        "    \"\"\" Given a string and a dict of {expression: value}, returns value corresponding to first occurring expression. \"\"\"\n",
+        "    # put default at end of the string\n",
+        "    positions = [(len(string), default)]\n",
+        "    # append with tuple (position, value) for each (item: value) in the dictionary\n",
+        "    for item, value in dictionary.items():\n",
+        "        position = string.find(item)\n",
+        "        # suppress items which were not found\n",
+        "        if position >= 0:\n",
+        "            positions.append((position, value))\n",
+        "    # return value corresponding to first position\n",
+        "    return sorted(positions)[0][1]"
+      ],
+      "metadata": {
+        "id": "qXu1DY-X8Deh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The next function splits a response into separate substrings representing the answers to each of the four questions. Then it extracts return values by searching defined expressions, by means of the function `first_matching_expression`. It returns both the substrings and the extracted information.\n",
+        "\n",
+        "This function is highly task-specific."
+      ],
+      "metadata": {
+        "id": "hGcyZO519Bf3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def extract_responses(string):\n",
+        "    string = string.lower() + \" \"\n",
+        "    i1 = string.find(\"1. \")\n",
+        "    i2 = string.find(\"2. \")\n",
+        "    i3 = string.find(\"3. \")\n",
+        "    i4 = string.find(\"4. \")\n",
+        "    s1 = string[i1:i2][3:]\n",
+        "    s2 = string[i2:i3][3:]\n",
+        "    s3 = string[i3:i4][3:]\n",
+        "    s4 = string[i4:][3:]\n",
+        "    d1 = {\"yes\": 1, \"minor\": 1}\n",
+        "    r1 = first_matching_expression(s1, d1, 0)\n",
+        "    d2 = {\"yes\": 1}\n",
+        "    r2 = first_matching_expression(s2, d2, 0)\n",
+        "    d3 = {\"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 5, \"6\": 6, \"7\": 7, \"8\": 8,\n",
+        "          \"9\": 9, \"only\": 1, \"one\": 1, \"two\": 2, \"three\": 3, \"four\": 4,\n",
+        "          \"five\": 5, \"six\": 6, \"seven\": 7, \"eight\": 8, \"nine\": 9}\n",
+        "    r3 = first_matching_expression(s3, d3, 1)\n",
+        "    r4 = first_matching_expression(s4, d3, 1)\n",
+        "    return [s1, s2, s3, s4, r1, r2, r3, r4]"
+      ],
+      "metadata": {
+        "id": "dyOpRR6C9755"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we define a function `add_responses_to_df` that takes a list of responses, applies `extract_responses`, concatenates the information to the original DataFrame and stores the resulting DataFrame.\n"
+      ],
+      "metadata": {
+        "id": "__CykhPc-ek9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def add_responses_to_df(responses, df, path_file_result):\n",
+        "    df_results = pd.concat([\n",
+        "        df.iloc[:len(responses)],\n",
+        "        pd.DataFrame(\n",
+        "            [extract_responses(r) for r in responses[\"response\"]],\n",
+        "            columns=[\"s1\", \"s2\", \"s3\", \"s4\", \"r1\", \"r2\", \"r3\", \"r4\"])],\n",
+        "        axis=1)\n",
+        "    df_results.to_excel(path_file_result)\n",
+        "    return df_results"
+      ],
+      "metadata": {
+        "id": "qmxcc4i6-i1G"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We load the responses for the first 1000 samples from a previous run of this notebook."
+      ],
+      "metadata": {
+        "id": "VHwNErPL7OeN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = pd.read_csv(\"NHTSA_responses_0999.csv\")\n",
+        "results"
+      ],
+      "metadata": {
+        "id": "k5Uwn0Z3_rOU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "cc9d8395-d14a-4ee6-e8ca-c363b569edf4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                              response\n",
+              "0    1. Yes, the driver of V2 sustained minor injur...\n",
+              "1    1. No, the driver of V2 was not injured in the...\n",
+              "2    1. No one was injured.\\n2. No one was killed.\\...\n",
+              "3    1. Yes, the driver of vehicle one (V1) was inj...\n",
+              "4    1. Yes, the 17-year-old male driver of Vehicle...\n",
+              "..                                                 ...\n",
+              "995  1. Yes, someone was injured.\\n2. No, no one wa...\n",
+              "996  1. No, no one was injured.\\n2. No, no one was ...\n",
+              "997  1. No one was injured.\\n2. No one was killed.\\...\n",
+              "998  1. No one was injured.\\n2. No one was killed.\\...\n",
+              "999  1. It is not mentioned in the text whether som...\n",
+              "\n",
+              "[1000 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6466238c-f7b7-4654-8e92-1af81e2992e1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>response</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1. Yes, the driver of V2 sustained minor injur...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1. No, the driver of V2 was not injured in the...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1. No one was injured.\\n2. No one was killed.\\...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1. Yes, the driver of vehicle one (V1) was inj...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1. Yes, the 17-year-old male driver of Vehicle...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>995</th>\n",
+              "      <td>1. Yes, someone was injured.\\n2. No, no one wa...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>1. No, no one was injured.\\n2. No, no one was ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>1. No one was injured.\\n2. No one was killed.\\...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>998</th>\n",
+              "      <td>1. No one was injured.\\n2. No one was killed.\\...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>999</th>\n",
+              "      <td>1. It is not mentioned in the text whether som...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1000 rows × 1 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6466238c-f7b7-4654-8e92-1af81e2992e1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
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+              "\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_res = add_responses_to_df(results, df, \"./results/df_results.xlsx\")"
+      ],
+      "metadata": {
+        "id": "p2LA4Gd3_3hp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a id='evaluation'></a>\n",
+        "<a name='evaluation'></a>\n",
+        "###  2.3 Performance evaluation\n",
+        "\n",
+        "Next, we want to evaluate the performance of our model.\n",
+        "\n",
+        "Let's look at the confusion matrix of the predicted vs true number of involved vehicles:\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_g3KTpBW60o_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_true = df_res[\"NUMTOTV\"]\n",
+        "y_pred = df_res[\"r4\"]\n",
+        "labels = [str(i) for i in sorted(set(y_true).union(set(y_pred)))]\n",
+        "_ = evaluate_classifier(y_true, y_pred, None, labels,\n",
+        "                        \"ChatGPT #vehicles\", \"cm_nv_chat_gpt\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 820
+        },
+        "id": "mbPJA_04FQds",
+        "outputId": "cb54bf9a-9737-4f89-c6e8-2d36f4e20772"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ChatGPT #vehicles\n",
+            "accuracy score = 97.4%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.99      0.97      0.98       256\n",
+            "           2       0.98      1.00      0.99       625\n",
+            "           3       0.92      0.90      0.91        94\n",
+            "           4       1.00      0.75      0.86        20\n",
+            "           5       0.67      0.50      0.57         4\n",
+            "           8       1.00      1.00      1.00         1\n",
+            "\n",
+            "    accuracy                           0.97      1000\n",
+            "   macro avg       0.93      0.85      0.88      1000\n",
+            "weighted avg       0.97      0.97      0.97      1000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
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+              "    x.observe(notebookContainer, {childList: true});\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For the identification of cases with bodily injury, the performance looks as follows:"
+      ],
+      "metadata": {
+        "id": "xLq_B72X8nel"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_true = df_res[\"INJSEVB\"]\n",
+        "y_pred = 1- (1 - df_res[\"r1\"]) * (1 - df_res[\"r2\"])\n",
+        "labels = [str(i) for i in sorted(set(y_true).union(set(y_pred)))]\n",
+        "_ = evaluate_classifier(y_true, y_pred, None, labels,\n",
+        "                        \"ChatGPT injury\", \"cm_inj_chat_gpt\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 750
+        },
+        "id": "16A9KflsCj-3",
+        "outputId": "e3f5495e-9543-40c9-c76a-f3014688ccb8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ChatGPT injury\n",
+            "accuracy score = 88.1%,  log loss = nan,  Brier loss = nan\n",
+            "classification report\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.87      0.81      0.84       382\n",
+            "           1       0.89      0.92      0.91       618\n",
+            "\n",
+            "    accuracy                           0.88      1000\n",
+            "   macro avg       0.88      0.87      0.87      1000\n",
+            "weighted avg       0.88      0.88      0.88      1000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+            ]
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+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For both tasks, we can compare the accuracy score to the ones achieved by the supervised approaches examined in Part I of this tutorial.\n",
+        "We observe the following:\n",
+        "* The accuracy score is higher than with supervised training of a logistic regression classifier on the DistilBERT-encoded texts.\n",
+        "* The accuracy score is somewhat below the one obtained using task-specific fine-tuning of the DistilBERT model.\n",
+        "\n",
+        "Note, however, that here we have not employed any task-specific training!"
+      ],
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+        "id": "TqgnpwRx9mhE"
+      }
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+    {
+      "cell_type": "markdown",
+      "metadata": {
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+      "source": [
+        "<a id='conclusion'></a>\n",
+        "<a name='conclusion'></a>\n",
+        "## 3.&nbsp;Conclusion\n",
+        "\n",
+        "Congratulations!\n",
+        "\n",
+        "In this Part III of the tutorial, you have used ChatGPT to extract features from text in an unsupervised fashion.\n",
+        "\n",
+        "Advantages of this approach are certainly that no labels are required, and that it is very simple to implement.\n",
+        "\n",
+        "On the other hand, execution time is longer than for the supervised approaches examined in Part I.\n",
+        "\n",
+        "In terms of accuracy score, the approach used here performs better than  supervised training of a logistic regression classifier on the DistilBERT-encoded texts, but somewhat worse than task-specific supervised fine-tuning of the DistilBERT model.\n",
+        "Note however that we haven't performed any fine-tuning in this notebook.\n",
+        "\n",
+        "In practice, the unsupervised and supervised techniques could be combined, for instance by using ChatGPT to generate labels for a sufficintly large set of data, that is then used in a supervised setting.\n",
+        "\n",
+        "If you have enjoyed this tutorial, feel free to apply any of the approaches - or improved versions, of course - to your own text data, to enrich your structured features available for supervised learning tasks."
+      ]
+    }
+  ],
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