Detailed-Analysis-of-US-traffic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:05:54.838255Z",
     "iopub.status.busy": "2021-09-20T23:05:54.837953Z",
     "iopub.status.idle": "2021-09-20T23:05:54.859223Z",
     "shell.execute_reply": "2021-09-20T23:05:54.858387Z",
     "shell.execute_reply.started": "2021-09-20T23:05:54.838218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\dot_traffic_stations_2015.txt\n",
      "data\\dot_traffic_US.txt\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"data\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('data'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting graphviz\n",
      "  Downloading graphviz-0.17-py3-none-any.whl (18 kB)\n",
      "Installing collected packages: graphviz\n",
      "Successfully installed graphviz-0.17\n"
     ]
    }
   ],
   "source": [
    "# !pip install pydot\n",
    "# !pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:05:54.862088Z",
     "iopub.status.busy": "2021-09-20T23:05:54.861087Z",
     "iopub.status.idle": "2021-09-20T23:05:56.083736Z",
     "shell.execute_reply": "2021-09-20T23:05:56.082776Z",
     "shell.execute_reply.started": "2021-09-20T23:05:54.862032Z"
    },
    "id": "NLVeqpOOJVWc"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pydot\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from graphviz import Source\n",
    "from sklearn import tree\n",
    "\n",
    "# display warnings only the first time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://host.docker.internal:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.1.2</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>Dataframe</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x1b701392d00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SPARK LIBRARIES\n",
    "from pyspark.sql import SparkSession\n",
    "spark=SparkSession.builder.appName('Dataframe').getOrCreate()\n",
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## read the dataset\n",
    "# df_pyspark=spark.read.option('header','true').csv('data/dot_traffic_stations_2015.txt',inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- algorithm_of_vehicle_classification: string (nullable = true)\n",
      " |-- algorithm_of_vehicle_classification_name: string (nullable = true)\n",
      " |-- calibration_of_weighing_system: string (nullable = true)\n",
      " |-- calibration_of_weighing_system_name: string (nullable = true)\n",
      " |-- classification_system_for_vehicle_classification: integer (nullable = true)\n",
      " |-- concurrent_route_signing: integer (nullable = true)\n",
      " |-- concurrent_signed_route_number: string (nullable = true)\n",
      " |-- direction_of_travel: integer (nullable = true)\n",
      " |-- direction_of_travel_name: string (nullable = true)\n",
      " |-- fips_county_code: integer (nullable = true)\n",
      " |-- fips_state_code: integer (nullable = true)\n",
      " |-- functional_classification: string (nullable = true)\n",
      " |-- functional_classification_name: string (nullable = true)\n",
      " |-- hpms_sample_identifier: string (nullable = true)\n",
      " |-- hpms_sample_type: string (nullable = true)\n",
      " |-- lane_of_travel: integer (nullable = true)\n",
      " |-- lane_of_travel_name: string (nullable = true)\n",
      " |-- latitude: double (nullable = true)\n",
      " |-- longitude: double (nullable = true)\n",
      " |-- lrs_identification: string (nullable = true)\n",
      " |-- lrs_location_point: integer (nullable = true)\n",
      " |-- method_of_data_retrieval: integer (nullable = true)\n",
      " |-- method_of_data_retrieval_name: string (nullable = true)\n",
      " |-- method_of_traffic_volume_counting: integer (nullable = true)\n",
      " |-- method_of_traffic_volume_counting_name: string (nullable = true)\n",
      " |-- method_of_truck_weighing: integer (nullable = true)\n",
      " |-- method_of_truck_weighing_name: string (nullable = true)\n",
      " |-- method_of_vehicle_classification: integer (nullable = true)\n",
      " |-- method_of_vehicle_classification_name: string (nullable = true)\n",
      " |-- national_highway_system: string (nullable = true)\n",
      " |-- number_of_lanes_in_direction_indicated: integer (nullable = true)\n",
      " |-- number_of_lanes_monitored_for_traffic_volume: integer (nullable = true)\n",
      " |-- number_of_lanes_monitored_for_truck_weight: integer (nullable = true)\n",
      " |-- number_of_lanes_monitored_for_vehicle_class: integer (nullable = true)\n",
      " |-- posted_route_signing: integer (nullable = true)\n",
      " |-- posted_signed_route_number: string (nullable = true)\n",
      " |-- previous_station_id: string (nullable = true)\n",
      " |-- primary_purpose: string (nullable = true)\n",
      " |-- primary_purpose_name: string (nullable = true)\n",
      " |-- record_type: string (nullable = true)\n",
      " |-- sample_type_for_traffic_volume: string (nullable = true)\n",
      " |-- sample_type_for_traffic_volume_name: string (nullable = true)\n",
      " |-- sample_type_for_truck_weight: string (nullable = true)\n",
      " |-- sample_type_for_truck_weight_name: string (nullable = true)\n",
      " |-- sample_type_for_vehicle_classification: string (nullable = true)\n",
      " |-- sample_type_for_vehicle_classification_name: string (nullable = true)\n",
      " |-- second_type_of_sensor: string (nullable = true)\n",
      " |-- shrp_site_identification: string (nullable = true)\n",
      " |-- station_id: string (nullable = true)\n",
      " |-- station_location: string (nullable = true)\n",
      " |-- type_of_sensor: string (nullable = true)\n",
      " |-- type_of_sensor_name: string (nullable = true)\n",
      " |-- year_of_data: integer (nullable = true)\n",
      " |-- year_station_discontinued: integer (nullable = true)\n",
      " |-- year_station_established: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Check the schema\n",
    "# df_pyspark.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# traffic_station_df = df_pyspark.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-j7dpgjcQNvN"
   },
   "source": [
    "Importing both the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:05:56.086208Z",
     "iopub.status.busy": "2021-09-20T23:05:56.085895Z",
     "iopub.status.idle": "2021-09-20T23:07:00.787168Z",
     "shell.execute_reply": "2021-09-20T23:07:00.786111Z",
     "shell.execute_reply.started": "2021-09-20T23:05:56.086166Z"
    },
    "id": "RdSxhGQfJVWd"
   },
   "outputs": [],
   "source": [
    "# traffic station characteristics\n",
    "\n",
    "# # change the path name of the dataset according to the location of data on your device.\n",
    "traffic_station_df = pd.read_csv(\"data/dot_traffic_stations_2015.txt\",\n",
    "                                 header=0, sep=',', quotechar='\"')\n",
    "\n",
    "# # traffic volume metrics associated to each traffic station\n",
    "\n",
    "# # change the path name of the dataset according to the location of data on your device.\n",
    "traffic_df = pd.read_csv(\"data/dot_traffic_US.txt\",\n",
    "                         header=0, sep=',', quotechar='\"')\n",
    "\n",
    "# rename long feature names\n",
    "traffic_station_df.rename(columns = {\"number_of_lanes_in_direction_indicated\": \"lane_count\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rD4Xh-hoJVWe"
   },
   "source": [
    "# **CLEANING DATA - TRAFFIC DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:00.789553Z",
     "iopub.status.busy": "2021-09-20T23:07:00.789184Z",
     "iopub.status.idle": "2021-09-20T23:07:00.825829Z",
     "shell.execute_reply": "2021-09-20T23:07:00.824503Z",
     "shell.execute_reply.started": "2021-09-20T23:07:00.789508Z"
    },
    "id": "sZ7uPesVJVWg",
    "outputId": "eef8b95c-e941-4204-f157-a281fbe38f79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traffic data:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .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>date</th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>functional_classification</th>\n",
       "      <th>functional_classification_name</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>...</th>\n",
       "      <th>traffic_volume_counted_after_1500_to_1600</th>\n",
       "      <th>traffic_volume_counted_after_1600_to_1700</th>\n",
       "      <th>traffic_volume_counted_after_1700_to_1800</th>\n",
       "      <th>traffic_volume_counted_after_1800_to_1900</th>\n",
       "      <th>traffic_volume_counted_after_1900_to_2000</th>\n",
       "      <th>traffic_volume_counted_after_2000_to_2100</th>\n",
       "      <th>traffic_volume_counted_after_2100_to_2200</th>\n",
       "      <th>traffic_volume_counted_after_2200_to_2300</th>\n",
       "      <th>traffic_volume_counted_after_2300_to_2400</th>\n",
       "      <th>year_of_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>56</td>\n",
       "      <td>3R</td>\n",
       "      <td>Rural: Principal Arterial - Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>122</td>\n",
       "      <td>124</td>\n",
       "      <td>110</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>21</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1617</td>\n",
       "      <td>1669</td>\n",
       "      <td>1308</td>\n",
       "      <td>1068</td>\n",
       "      <td>928</td>\n",
       "      <td>885</td>\n",
       "      <td>798</td>\n",
       "      <td>650</td>\n",
       "      <td>613</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>6</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>4244</td>\n",
       "      <td>4405</td>\n",
       "      <td>4609</td>\n",
       "      <td>4361</td>\n",
       "      <td>3272</td>\n",
       "      <td>2243</td>\n",
       "      <td>2050</td>\n",
       "      <td>1453</td>\n",
       "      <td>892</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>55</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1011</td>\n",
       "      <td>959</td>\n",
       "      <td>851</td>\n",
       "      <td>708</td>\n",
       "      <td>559</td>\n",
       "      <td>457</td>\n",
       "      <td>297</td>\n",
       "      <td>207</td>\n",
       "      <td>110</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>4</td>\n",
       "      <td>4R</td>\n",
       "      <td>Rural: Minor Arterial</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>61</td>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day_of_data  day_of_week  direction_of_travel  \\\n",
       "0  2015-04-07            7            3                    1   \n",
       "1  2015-09-26           26            7                    7   \n",
       "2  2015-06-16           16            3                    3   \n",
       "3  2015-04-26           26            1                    5   \n",
       "4  2015-05-23           23            7                    3   \n",
       "\n",
       "  direction_of_travel_name  fips_state_code functional_classification  \\\n",
       "0                    North               56                        3R   \n",
       "1                     West               21                        1U   \n",
       "2                     East                6                        1U   \n",
       "3                    South               55                        1U   \n",
       "4                     East                4                        4R   \n",
       "\n",
       "           functional_classification_name  lane_of_travel  month_of_data  ...  \\\n",
       "0       Rural: Principal Arterial - Other               1              4  ...   \n",
       "1  Urban: Principal Arterial - Interstate               2              9  ...   \n",
       "2  Urban: Principal Arterial - Interstate               0              6  ...   \n",
       "3  Urban: Principal Arterial - Interstate               1              4  ...   \n",
       "4                   Rural: Minor Arterial               0              5  ...   \n",
       "\n",
       "   traffic_volume_counted_after_1500_to_1600  \\\n",
       "0                                         89   \n",
       "1                                       1617   \n",
       "2                                       4244   \n",
       "3                                       1011   \n",
       "4                                         83   \n",
       "\n",
       "   traffic_volume_counted_after_1600_to_1700  \\\n",
       "0                                        122   \n",
       "1                                       1669   \n",
       "2                                       4405   \n",
       "3                                        959   \n",
       "4                                         61   \n",
       "\n",
       "  traffic_volume_counted_after_1700_to_1800  \\\n",
       "0                                       124   \n",
       "1                                      1308   \n",
       "2                                      4609   \n",
       "3                                       851   \n",
       "4                                        55   \n",
       "\n",
       "   traffic_volume_counted_after_1800_to_1900  \\\n",
       "0                                        110   \n",
       "1                                       1068   \n",
       "2                                       4361   \n",
       "3                                        708   \n",
       "4                                         35   \n",
       "\n",
       "   traffic_volume_counted_after_1900_to_2000  \\\n",
       "0                                         69   \n",
       "1                                        928   \n",
       "2                                       3272   \n",
       "3                                        559   \n",
       "4                                         29   \n",
       "\n",
       "   traffic_volume_counted_after_2000_to_2100  \\\n",
       "0                                         73   \n",
       "1                                        885   \n",
       "2                                       2243   \n",
       "3                                        457   \n",
       "4                                         21   \n",
       "\n",
       "   traffic_volume_counted_after_2100_to_2200  \\\n",
       "0                                         28   \n",
       "1                                        798   \n",
       "2                                       2050   \n",
       "3                                        297   \n",
       "4                                         23   \n",
       "\n",
       "   traffic_volume_counted_after_2200_to_2300  \\\n",
       "0                                         12   \n",
       "1                                        650   \n",
       "2                                       1453   \n",
       "3                                        207   \n",
       "4                                          9   \n",
       "\n",
       "   traffic_volume_counted_after_2300_to_2400  year_of_data  \n",
       "0                                          6            15  \n",
       "1                                        613            15  \n",
       "2                                        892            15  \n",
       "3                                        110            15  \n",
       "4                                          7            15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Traffic data:')\n",
    "traffic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Rows & Columns :: \n",
      "(7140391, 38)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Number of Rows & Columns :: \")\n",
    "print(traffic_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oo_WD1aVP5cO"
   },
   "source": [
    "We can see here that '**restrictions**' column is completely **empty** and therefore will be of no use to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:00.829315Z",
     "iopub.status.busy": "2021-09-20T23:07:00.828800Z",
     "iopub.status.idle": "2021-09-20T23:07:04.904962Z",
     "shell.execute_reply": "2021-09-20T23:07:04.904125Z",
     "shell.execute_reply.started": "2021-09-20T23:07:00.829258Z"
    },
    "id": "SCjWEFz_JVWg",
    "outputId": "c899a0ca-e100-4c2e-aa51-0f1617480476"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                               0\n",
       "day_of_data                                        0\n",
       "day_of_week                                        0\n",
       "direction_of_travel                                0\n",
       "direction_of_travel_name                           0\n",
       "fips_state_code                                    0\n",
       "functional_classification                          0\n",
       "functional_classification_name                     0\n",
       "lane_of_travel                                     0\n",
       "month_of_data                                      0\n",
       "record_type                                        0\n",
       "restrictions                                 7140391\n",
       "station_id                                         0\n",
       "traffic_volume_counted_after_0000_to_0100          0\n",
       "traffic_volume_counted_after_0100_to_0200          0\n",
       "traffic_volume_counted_after_0200_to_0300          0\n",
       "traffic_volume_counted_after_0300_to_0400          0\n",
       "traffic_volume_counted_after_0400_to_0500          0\n",
       "traffic_volume_counted_after_0500_to_0600          0\n",
       "traffic_volume_counted_after_0600_to_0700          0\n",
       "traffic_volume_counted_after_0700_to_0800          0\n",
       "traffic_volume_counted_after_0800_to_0900          0\n",
       "traffic_volume_counted_after_0900_to_1000          0\n",
       "traffic_volume_counted_after_1000_to_1100          0\n",
       "traffic_volume_counted_after_1100_to_1200          0\n",
       "traffic_volume_counted_after_1200_to_1300          0\n",
       "traffic_volume_counted_after_1300_to_1400          0\n",
       "traffic_volume_counted_after_1400_to_1500          0\n",
       "traffic_volume_counted_after_1500_to_1600          0\n",
       "traffic_volume_counted_after_1600_to_1700          0\n",
       "traffic_volume_counted_after_1700_to_1800          0\n",
       "traffic_volume_counted_after_1800_to_1900          0\n",
       "traffic_volume_counted_after_1900_to_2000          0\n",
       "traffic_volume_counted_after_2000_to_2100          0\n",
       "traffic_volume_counted_after_2100_to_2200          0\n",
       "traffic_volume_counted_after_2200_to_2300          0\n",
       "traffic_volume_counted_after_2300_to_2400          0\n",
       "year_of_data                                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zh58WUmyQJ-6"
   },
   "source": [
    "dropping **restrictions** column because it contains all values as null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:04.906691Z",
     "iopub.status.busy": "2021-09-20T23:07:04.906365Z",
     "iopub.status.idle": "2021-09-20T23:07:10.442902Z",
     "shell.execute_reply": "2021-09-20T23:07:10.442179Z",
     "shell.execute_reply.started": "2021-09-20T23:07:04.906648Z"
    },
    "id": "sdLZIgk-JVWh"
   },
   "outputs": [],
   "source": [
    "# dropping restrictions column because it contains all values as null\n",
    "traffic_df.dropna(how='all', axis=1, inplace=True)\n",
    "traffic_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:10.444886Z",
     "iopub.status.busy": "2021-09-20T23:07:10.444376Z",
     "iopub.status.idle": "2021-09-20T23:07:10.455029Z",
     "shell.execute_reply": "2021-09-20T23:07:10.454182Z",
     "shell.execute_reply.started": "2021-09-20T23:07:10.444853Z"
    },
    "id": "xPxxyp6jJVWi",
    "outputId": "69ffb803-61fe-4bcd-fa1f-75ffbd88ab41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                         object\n",
       "day_of_data                                   int64\n",
       "day_of_week                                   int64\n",
       "direction_of_travel                           int64\n",
       "direction_of_travel_name                     object\n",
       "fips_state_code                               int64\n",
       "functional_classification                    object\n",
       "functional_classification_name               object\n",
       "lane_of_travel                                int64\n",
       "month_of_data                                 int64\n",
       "record_type                                   int64\n",
       "station_id                                   object\n",
       "traffic_volume_counted_after_0000_to_0100     int64\n",
       "traffic_volume_counted_after_0100_to_0200     int64\n",
       "traffic_volume_counted_after_0200_to_0300     int64\n",
       "traffic_volume_counted_after_0300_to_0400     int64\n",
       "traffic_volume_counted_after_0400_to_0500     int64\n",
       "traffic_volume_counted_after_0500_to_0600     int64\n",
       "traffic_volume_counted_after_0600_to_0700     int64\n",
       "traffic_volume_counted_after_0700_to_0800     int64\n",
       "traffic_volume_counted_after_0800_to_0900     int64\n",
       "traffic_volume_counted_after_0900_to_1000     int64\n",
       "traffic_volume_counted_after_1000_to_1100     int64\n",
       "traffic_volume_counted_after_1100_to_1200     int64\n",
       "traffic_volume_counted_after_1200_to_1300     int64\n",
       "traffic_volume_counted_after_1300_to_1400     int64\n",
       "traffic_volume_counted_after_1400_to_1500     int64\n",
       "traffic_volume_counted_after_1500_to_1600     int64\n",
       "traffic_volume_counted_after_1600_to_1700     int64\n",
       "traffic_volume_counted_after_1700_to_1800     int64\n",
       "traffic_volume_counted_after_1800_to_1900     int64\n",
       "traffic_volume_counted_after_1900_to_2000     int64\n",
       "traffic_volume_counted_after_2000_to_2100     int64\n",
       "traffic_volume_counted_after_2100_to_2200     int64\n",
       "traffic_volume_counted_after_2200_to_2300     int64\n",
       "traffic_volume_counted_after_2300_to_2400     int64\n",
       "year_of_data                                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dHJ0LlF0QiyO"
   },
   "source": [
    "Changing the datatype of date column to datetime as the given datatype is not correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:10.456702Z",
     "iopub.status.busy": "2021-09-20T23:07:10.456458Z",
     "iopub.status.idle": "2021-09-20T23:07:12.328625Z",
     "shell.execute_reply": "2021-09-20T23:07:12.327055Z",
     "shell.execute_reply.started": "2021-09-20T23:07:10.456673Z"
    },
    "id": "Zdl1L-4yJVWj"
   },
   "outputs": [],
   "source": [
    "# Changing the datatype of date column to datetime as the given datatype is not correct.\n",
    "traffic_df['date']= pd.to_datetime(traffic_df['date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U76mxK1BJVWj"
   },
   "source": [
    "# **CLEANING DATA - STATION DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:12.332196Z",
     "iopub.status.busy": "2021-09-20T23:07:12.331857Z",
     "iopub.status.idle": "2021-09-20T23:07:12.366027Z",
     "shell.execute_reply": "2021-09-20T23:07:12.364200Z",
     "shell.execute_reply.started": "2021-09-20T23:07:12.332152Z"
    },
    "id": "iQ7YVHeSJVWk",
    "outputId": "07fb6670-55e8-4e5e-8e02-034ae4d3eb90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traffic Station data:\n"
     ]
    },
    {
     "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>algorithm_of_vehicle_classification</th>\n",
       "      <th>algorithm_of_vehicle_classification_name</th>\n",
       "      <th>calibration_of_weighing_system</th>\n",
       "      <th>calibration_of_weighing_system_name</th>\n",
       "      <th>classification_system_for_vehicle_classification</th>\n",
       "      <th>concurrent_route_signing</th>\n",
       "      <th>concurrent_signed_route_number</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_county_code</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_type_for_vehicle_classification_name</th>\n",
       "      <th>second_type_of_sensor</th>\n",
       "      <th>shrp_site_identification</th>\n",
       "      <th>station_id</th>\n",
       "      <th>station_location</th>\n",
       "      <th>type_of_sensor</th>\n",
       "      <th>type_of_sensor_name</th>\n",
       "      <th>year_of_data</th>\n",
       "      <th>year_station_discontinued</th>\n",
       "      <th>year_station_established</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>091</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129130</td>\n",
       "      <td>LAKEVIEW AVENUE ORA91R10.091</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>099</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100190</td>\n",
       "      <td>LITTLE JOHN CREEK SJ9912.526</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>005</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>Station used for Heavy Vehicle Travel Informat...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>022940</td>\n",
       "      <td>EDGEWOOD SIS5R22.999</td>\n",
       "      <td>P</td>\n",
       "      <td>Piezoelectric</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Vehicle length classification</td>\n",
       "      <td>M</td>\n",
       "      <td>Moving average of the steering axle of 3S2s</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>000302</td>\n",
       "      <td>I 15  12900 South  M.P.  290.6</td>\n",
       "      <td>X</td>\n",
       "      <td>Radio wave</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>000000</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W01136</td>\n",
       "      <td>E. of Franklin Rd Underpass</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  algorithm_of_vehicle_classification  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                   G   \n",
       "3                                   D   \n",
       "4                                   G   \n",
       "\n",
       "  algorithm_of_vehicle_classification_name calibration_of_weighing_system  \\\n",
       "0                                      NaN                            NaN   \n",
       "1                                      NaN                            NaN   \n",
       "2      Axle spacing with Scheme F modified                            NaN   \n",
       "3            Vehicle length classification                              M   \n",
       "4      Axle spacing with Scheme F modified                              0   \n",
       "\n",
       "           calibration_of_weighing_system_name  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3  Moving average of the steering axle of 3S2s   \n",
       "4                                          NaN   \n",
       "\n",
       "   classification_system_for_vehicle_classification  concurrent_route_signing  \\\n",
       "0                                                13                         3   \n",
       "1                                                13                         3   \n",
       "2                                                15                         1   \n",
       "3                                                13                         0   \n",
       "4                                                14                         1   \n",
       "\n",
       "  concurrent_signed_route_number  direction_of_travel  \\\n",
       "0                            091                    7   \n",
       "1                            099                    5   \n",
       "2                            005                    1   \n",
       "3                            NaN                    5   \n",
       "4                         000000                    7   \n",
       "\n",
       "  direction_of_travel_name  fips_county_code  ...  \\\n",
       "0                     West                59  ...   \n",
       "1                    South                77  ...   \n",
       "2                    North                93  ...   \n",
       "3                    South                35  ...   \n",
       "4                     West                27  ...   \n",
       "\n",
       "         sample_type_for_vehicle_classification_name second_type_of_sensor  \\\n",
       "0  Station not used for Heavy Vehicle Travel Info...                     N   \n",
       "1  Station not used for Heavy Vehicle Travel Info...                     N   \n",
       "2  Station used for Heavy Vehicle Travel Informat...                     N   \n",
       "3  Station not used for Heavy Vehicle Travel Info...                   NaN   \n",
       "4  Station not used for Heavy Vehicle Travel Info...                   NaN   \n",
       "\n",
       "  shrp_site_identification station_id                station_location  \\\n",
       "0                      NaN     129130    LAKEVIEW AVENUE ORA91R10.091   \n",
       "1                      NaN     100190    LITTLE JOHN CREEK SJ9912.526   \n",
       "2                      NaN     022940            EDGEWOOD SIS5R22.999   \n",
       "3                      NaN     000302  I 15  12900 South  M.P.  290.6   \n",
       "4                      NaN     W01136     E. of Franklin Rd Underpass   \n",
       "\n",
       "   type_of_sensor type_of_sensor_name  year_of_data  \\\n",
       "0               L     Inductance loop            15   \n",
       "1               L     Inductance loop            15   \n",
       "2               P       Piezoelectric            15   \n",
       "3               X          Radio wave            15   \n",
       "4               L     Inductance loop            15   \n",
       "\n",
       "   year_station_discontinued year_station_established  \n",
       "0                          0                       97  \n",
       "1                          0                       97  \n",
       "2                          0                       69  \n",
       "3                          0                       11  \n",
       "4                          0                       95  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view top of station dataframe\n",
    "print('Traffic Station data:')\n",
    "traffic_station_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bYeqwVjhQw-k"
   },
   "source": [
    "Here we are trying to observe the dataset so that we can clean it more efficiently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:12.370477Z",
     "iopub.status.busy": "2021-09-20T23:07:12.369963Z",
     "iopub.status.idle": "2021-09-20T23:07:12.411225Z",
     "shell.execute_reply": "2021-09-20T23:07:12.410300Z",
     "shell.execute_reply.started": "2021-09-20T23:07:12.370433Z"
    },
    "id": "J852eF4jJVWl"
   },
   "outputs": [],
   "source": [
    "# we are dropping the columns with more than 60% NULL values as they will not be able to contribute in the dataset and will reduce the model accuracy\n",
    "traffic_station_df.drop(['concurrent_signed_route_number', 'hpms_sample_identifier', \n",
    "                         'lrs_identification', 'lrs_location_point', \n",
    "                         'shrp_site_identification'], axis = 1, inplace=True)\n",
    "\n",
    "# here we are only dropping the rows which contains the null values in 'latitude', 'longitude', 'station_location' because they contain \n",
    "# significatly less null values and are useful columns.\n",
    "traffic_station_df.dropna(subset=['latitude', 'longitude', 'station_location'], inplace=True)\n",
    "traffic_station_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:12.414375Z",
     "iopub.status.busy": "2021-09-20T23:07:12.414097Z",
     "iopub.status.idle": "2021-09-20T23:07:12.548998Z",
     "shell.execute_reply": "2021-09-20T23:07:12.548341Z",
     "shell.execute_reply.started": "2021-09-20T23:07:12.414346Z"
    },
    "id": "sKTWgDISJVWm",
    "outputId": "d443700f-27ab-484c-ee4e-3f97823dbe98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traffic_Station_Null_Mean: \n",
      "\n",
      "algorithm_of_vehicle_classification                 0.347432\n",
      "algorithm_of_vehicle_classification_name            0.391028\n",
      "calibration_of_weighing_system                      0.713167\n",
      "calibration_of_weighing_system_name                 0.765299\n",
      "classification_system_for_vehicle_classification    0.000000\n",
      "concurrent_route_signing                            0.000000\n",
      "concurrent_signed_route_number                      0.522518\n",
      "direction_of_travel                                 0.000000\n",
      "direction_of_travel_name                            0.000000\n",
      "fips_county_code                                    0.000000\n",
      "fips_state_code                                     0.000000\n",
      "functional_classification                           0.000000\n",
      "functional_classification_name                      0.000000\n",
      "hpms_sample_identifier                              0.535657\n",
      "hpms_sample_type                                    0.000000\n",
      "lane_of_travel                                      0.000000\n",
      "lane_of_travel_name                                 0.000000\n",
      "latitude                                            0.000035\n",
      "longitude                                           0.000035\n",
      "lrs_identification                                  0.193775\n",
      "lrs_location_point                                  0.316132\n",
      "method_of_data_retrieval                            0.000000\n",
      "method_of_data_retrieval_name                       0.015457\n",
      "method_of_traffic_volume_counting                   0.000000\n",
      "method_of_traffic_volume_counting_name              0.030914\n",
      "method_of_truck_weighing                            0.000000\n",
      "method_of_truck_weighing_name                       0.793227\n",
      "method_of_vehicle_classification                    0.000000\n",
      "method_of_vehicle_classification_name               0.392749\n",
      "national_highway_system                             0.000000\n",
      "lane_count                                          0.000000\n",
      "number_of_lanes_monitored_for_traffic_volume        0.000000\n",
      "number_of_lanes_monitored_for_truck_weight          0.000000\n",
      "number_of_lanes_monitored_for_vehicle_class         0.000000\n",
      "posted_route_signing                                0.000000\n",
      "posted_signed_route_number                          0.027893\n",
      "previous_station_id                                 0.790803\n",
      "primary_purpose                                     0.007377\n",
      "primary_purpose_name                                0.022764\n",
      "record_type                                         0.000000\n",
      "sample_type_for_traffic_volume                      0.028525\n",
      "sample_type_for_traffic_volume_name                 0.036886\n",
      "sample_type_for_truck_weight                        0.423734\n",
      "sample_type_for_truck_weight_name                   0.450081\n",
      "sample_type_for_vehicle_classification              0.122497\n",
      "sample_type_for_vehicle_classification_name         0.155062\n",
      "second_type_of_sensor                               0.201504\n",
      "shrp_site_identification                            0.857620\n",
      "station_id                                          0.000000\n",
      "station_location                                    0.001300\n",
      "type_of_sensor                                      0.012366\n",
      "type_of_sensor_name                                 0.012366\n",
      "year_of_data                                        0.000000\n",
      "year_station_discontinued                           0.000000\n",
      "year_station_established                            0.000000\n",
      "dtype: float64\n",
      "\n",
      "Traffic_Station_Shape: \n",
      "\n",
      "(28466, 55)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Traffic_Station_Null_Mean: \\n')\n",
    "print(traffic_station_df.isnull().mean(), end = '\\n\\n')\n",
    "\n",
    "print('Traffic_Station_Shape: \\n')\n",
    "print(traffic_station_df.shape, end = '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W2TQcUqlJVWm"
   },
   "source": [
    "# **EDA - TRAFFIC DATA**    (Feature Engineering)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:12.551392Z",
     "iopub.status.busy": "2021-09-20T23:07:12.551020Z",
     "iopub.status.idle": "2021-09-20T23:07:15.005737Z",
     "shell.execute_reply": "2021-09-20T23:07:15.004589Z",
     "shell.execute_reply.started": "2021-09-20T23:07:12.551350Z"
    },
    "id": "0r9_cPgnJVWn"
   },
   "outputs": [],
   "source": [
    "variables = ['traffic_volume_counted_after_0000_to_0100', 'traffic_volume_counted_after_0100_to_0200', 'traffic_volume_counted_after_0200_to_0300',\n",
    "            'traffic_volume_counted_after_0300_to_0400', 'traffic_volume_counted_after_0400_to_0500', 'traffic_volume_counted_after_0500_to_0600',\n",
    "             'traffic_volume_counted_after_0600_to_0700', 'traffic_volume_counted_after_0700_to_0800', 'traffic_volume_counted_after_0800_to_0900',\n",
    "             'traffic_volume_counted_after_0900_to_1000', 'traffic_volume_counted_after_1000_to_1100', 'traffic_volume_counted_after_1100_to_1200',\n",
    "             'traffic_volume_counted_after_1200_to_1300', 'traffic_volume_counted_after_1300_to_1400', 'traffic_volume_counted_after_1400_to_1500',\n",
    "             'traffic_volume_counted_after_1500_to_1600', 'traffic_volume_counted_after_1600_to_1700', 'traffic_volume_counted_after_1700_to_1800',\n",
    "             'traffic_volume_counted_after_1800_to_1900', 'traffic_volume_counted_after_1900_to_2000', 'traffic_volume_counted_after_2000_to_2100',\n",
    "             'traffic_volume_counted_after_2100_to_2200', 'traffic_volume_counted_after_2200_to_2300', 'traffic_volume_counted_after_2300_to_2400']\n",
    "\n",
    "# we will be starting by correcting the datatype of all the time-slots that are given to us, so that we will be able to sum them up very easily.\n",
    "traffic_df[variables] = traffic_df[variables].astype(float).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MeLphat2SYG9"
   },
   "source": [
    "Here we are merging the time-slots in the **bucket of 4 hours** so that instead of dealing with 24 hours we will only have to deal with more accurate and less data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:15.007154Z",
     "iopub.status.busy": "2021-09-20T23:07:15.006948Z",
     "iopub.status.idle": "2021-09-20T23:07:26.384874Z",
     "shell.execute_reply": "2021-09-20T23:07:26.383863Z",
     "shell.execute_reply.started": "2021-09-20T23:07:15.007131Z"
    },
    "id": "TUdnRK0wJVWn",
    "outputId": "95c9f01c-1257-47ea-8f3a-9dbdf20c8efe"
   },
   "outputs": [
    {
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       "    }\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>date</th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>functional_classification</th>\n",
       "      <th>functional_classification_name</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>station_id</th>\n",
       "      <th>1_to_4</th>\n",
       "      <th>5_to_8</th>\n",
       "      <th>9_to_12</th>\n",
       "      <th>13_to_16</th>\n",
       "      <th>17_to_20</th>\n",
       "      <th>21_to_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>56</td>\n",
       "      <td>3R</td>\n",
       "      <td>Rural: Principal Arterial - Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>000084</td>\n",
       "      <td>13</td>\n",
       "      <td>381</td>\n",
       "      <td>581</td>\n",
       "      <td>523</td>\n",
       "      <td>425</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>21</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>056P94</td>\n",
       "      <td>1045</td>\n",
       "      <td>1700</td>\n",
       "      <td>4325</td>\n",
       "      <td>5829</td>\n",
       "      <td>4973</td>\n",
       "      <td>2946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>6</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>077590</td>\n",
       "      <td>1685</td>\n",
       "      <td>12487</td>\n",
       "      <td>18061</td>\n",
       "      <td>16324</td>\n",
       "      <td>16647</td>\n",
       "      <td>6638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>55</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>450001</td>\n",
       "      <td>312</td>\n",
       "      <td>811</td>\n",
       "      <td>3021</td>\n",
       "      <td>4057</td>\n",
       "      <td>3077</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>4</td>\n",
       "      <td>4R</td>\n",
       "      <td>Rural: Minor Arterial</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>102210</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>310</td>\n",
       "      <td>356</td>\n",
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      "text/plain": [
       "        date  day_of_data  day_of_week  direction_of_travel  \\\n",
       "0 2015-04-07            7            3                    1   \n",
       "1 2015-09-26           26            7                    7   \n",
       "2 2015-06-16           16            3                    3   \n",
       "3 2015-04-26           26            1                    5   \n",
       "4 2015-05-23           23            7                    3   \n",
       "\n",
       "  direction_of_travel_name  fips_state_code functional_classification  \\\n",
       "0                    North               56                        3R   \n",
       "1                     West               21                        1U   \n",
       "2                     East                6                        1U   \n",
       "3                    South               55                        1U   \n",
       "4                     East                4                        4R   \n",
       "\n",
       "           functional_classification_name  lane_of_travel  month_of_data  \\\n",
       "0       Rural: Principal Arterial - Other               1              4   \n",
       "1  Urban: Principal Arterial - Interstate               2              9   \n",
       "2  Urban: Principal Arterial - Interstate               0              6   \n",
       "3  Urban: Principal Arterial - Interstate               1              4   \n",
       "4                   Rural: Minor Arterial               0              5   \n",
       "\n",
       "  station_id  1_to_4  5_to_8  9_to_12  13_to_16  17_to_20  21_to_24  \n",
       "0     000084      13     381      581       523       425       119  \n",
       "1     056P94    1045    1700     4325      5829      4973      2946  \n",
       "2     077590    1685   12487    18061     16324     16647      6638  \n",
       "3     450001     312     811     3021      4057      3077      1071  \n",
       "4     102210      15      80      310       356       180        60  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "till_4 = ['traffic_volume_counted_after_0000_to_0100', 'traffic_volume_counted_after_0100_to_0200', 'traffic_volume_counted_after_0200_to_0300',\n",
    "         'traffic_volume_counted_after_0300_to_0400']\n",
    "till_8 = ['traffic_volume_counted_after_0400_to_0500', 'traffic_volume_counted_after_0500_to_0600','traffic_volume_counted_after_0600_to_0700',\n",
    "         'traffic_volume_counted_after_0700_to_0800']\n",
    "till_12 = ['traffic_volume_counted_after_0800_to_0900','traffic_volume_counted_after_0900_to_1000', 'traffic_volume_counted_after_1000_to_1100',\n",
    "          'traffic_volume_counted_after_1100_to_1200']\n",
    "till_16 = ['traffic_volume_counted_after_1200_to_1300', 'traffic_volume_counted_after_1300_to_1400', 'traffic_volume_counted_after_1400_to_1500',\n",
    "            'traffic_volume_counted_after_1500_to_1600']\n",
    "till_20 = ['traffic_volume_counted_after_1600_to_1700', 'traffic_volume_counted_after_1700_to_1800','traffic_volume_counted_after_1800_to_1900',\n",
    "           'traffic_volume_counted_after_1900_to_2000']\n",
    "till_24 = ['traffic_volume_counted_after_2000_to_2100','traffic_volume_counted_after_2100_to_2200', 'traffic_volume_counted_after_2200_to_2300',\n",
    "           'traffic_volume_counted_after_2300_to_2400']\n",
    "\n",
    "# Here we are summing up the data of 4 hours and creating the new column with the new data that we have got after summing up.\n",
    "traffic_df['1_to_4'] = traffic_df[till_4].sum(axis=1, numeric_only= True)\n",
    "traffic_df['5_to_8'] = traffic_df[till_8].sum(axis=1, numeric_only= True)\n",
    "traffic_df['9_to_12'] = traffic_df[till_12].sum(axis=1, numeric_only= True)\n",
    "traffic_df['13_to_16'] = traffic_df[till_16].sum(axis=1, numeric_only= True)\n",
    "traffic_df['17_to_20'] = traffic_df[till_20].sum(axis=1, numeric_only= True)\n",
    "traffic_df['21_to_24'] = traffic_df[till_24].sum(axis=1, numeric_only= True)\n",
    "\n",
    "# here we are dropping the columns named 'year_of_data', 'record_type' because they have only one value throughout the data which will affect our model accuracy.\n",
    "traffic_df.drop(variables, axis = 1, inplace=True)\n",
    "traffic_df.drop(['year_of_data', 'record_type'], axis = 1, inplace=True)\n",
    "traffic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KYKufVhqTVTl"
   },
   "source": [
    "After creating bucket of 4 hours, here i am dividing the value of every **bucket by 4** so that out values does not explode and we only get the data of each hour relatively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:26.386803Z",
     "iopub.status.busy": "2021-09-20T23:07:26.386240Z",
     "iopub.status.idle": "2021-09-20T23:07:27.645601Z",
     "shell.execute_reply": "2021-09-20T23:07:27.644958Z",
     "shell.execute_reply.started": "2021-09-20T23:07:26.386767Z"
    },
    "id": "nzWGuUW7JVWo",
    "outputId": "4971070c-9dd1-4907-848e-a8a4502f3335"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>functional_classification</th>\n",
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       "      <th>station_id</th>\n",
       "      <th>1_to_4</th>\n",
       "      <th>5_to_8</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
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       "      <td>3R</td>\n",
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       "      <td>000084</td>\n",
       "      <td>3.25</td>\n",
       "      <td>95.25</td>\n",
       "      <td>145.25</td>\n",
       "      <td>130.75</td>\n",
       "      <td>106.25</td>\n",
       "      <td>29.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>21</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>056P94</td>\n",
       "      <td>261.25</td>\n",
       "      <td>425.00</td>\n",
       "      <td>1081.25</td>\n",
       "      <td>1457.25</td>\n",
       "      <td>1243.25</td>\n",
       "      <td>736.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>6</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>077590</td>\n",
       "      <td>421.25</td>\n",
       "      <td>3121.75</td>\n",
       "      <td>4515.25</td>\n",
       "      <td>4081.00</td>\n",
       "      <td>4161.75</td>\n",
       "      <td>1659.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>55</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>450001</td>\n",
       "      <td>78.00</td>\n",
       "      <td>202.75</td>\n",
       "      <td>755.25</td>\n",
       "      <td>1014.25</td>\n",
       "      <td>769.25</td>\n",
       "      <td>267.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>4</td>\n",
       "      <td>4R</td>\n",
       "      <td>Rural: Minor Arterial</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>102210</td>\n",
       "      <td>3.75</td>\n",
       "      <td>20.00</td>\n",
       "      <td>77.50</td>\n",
       "      <td>89.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  day_of_data  day_of_week  direction_of_travel  \\\n",
       "0 2015-04-07            7            3                    1   \n",
       "1 2015-09-26           26            7                    7   \n",
       "2 2015-06-16           16            3                    3   \n",
       "3 2015-04-26           26            1                    5   \n",
       "4 2015-05-23           23            7                    3   \n",
       "\n",
       "  direction_of_travel_name  fips_state_code functional_classification  \\\n",
       "0                    North               56                        3R   \n",
       "1                     West               21                        1U   \n",
       "2                     East                6                        1U   \n",
       "3                    South               55                        1U   \n",
       "4                     East                4                        4R   \n",
       "\n",
       "           functional_classification_name  lane_of_travel  month_of_data  \\\n",
       "0       Rural: Principal Arterial - Other               1              4   \n",
       "1  Urban: Principal Arterial - Interstate               2              9   \n",
       "2  Urban: Principal Arterial - Interstate               0              6   \n",
       "3  Urban: Principal Arterial - Interstate               1              4   \n",
       "4                   Rural: Minor Arterial               0              5   \n",
       "\n",
       "  station_id  1_to_4   5_to_8  9_to_12  13_to_16  17_to_20  21_to_24  \n",
       "0     000084    3.25    95.25   145.25    130.75    106.25     29.75  \n",
       "1     056P94  261.25   425.00  1081.25   1457.25   1243.25    736.50  \n",
       "2     077590  421.25  3121.75  4515.25   4081.00   4161.75   1659.50  \n",
       "3     450001   78.00   202.75   755.25   1014.25    769.25    267.75  \n",
       "4     102210    3.75    20.00    77.50     89.00     45.00     15.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df['1_to_4'] = traffic_df['1_to_4'].div(4)\n",
    "traffic_df['5_to_8'] = traffic_df['5_to_8'].div(4)\n",
    "traffic_df['9_to_12'] = traffic_df['9_to_12'].div(4)\n",
    "traffic_df['13_to_16'] = traffic_df['13_to_16'].div(4)\n",
    "traffic_df['17_to_20'] = traffic_df['17_to_20'].div(4)\n",
    "traffic_df['21_to_24'] = traffic_df['21_to_24'].div(4)\n",
    "traffic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k1paExmOULZ_"
   },
   "source": [
    "From the below **boxplot**'s we can clearly see that there exists some **outliers in the dataset**. Therefore now we will be working on removing the outliers present in the data. \n",
    "\n",
    "> 1.   In statistics, an outlier is an observation point that is distant from other observations.\n",
    "> 2.   In descriptive statistics, a box plot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:27.646973Z",
     "iopub.status.busy": "2021-09-20T23:07:27.646582Z",
     "iopub.status.idle": "2021-09-20T23:07:42.078654Z",
     "shell.execute_reply": "2021-09-20T23:07:42.077622Z",
     "shell.execute_reply.started": "2021-09-20T23:07:27.646943Z"
    },
    "id": "DBkEuUKwJVWo",
    "outputId": "eb1e3e7c-9302-4048-919e-ed6809c052a8"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings=['1_to_4','5_to_8', '9_to_12', '13_to_16', '17_to_20', '21_to_24']\n",
    "plt.figure(figsize = (20,8))\n",
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.boxplot(x=traffic_df[timings[i-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ktIwfKsjfSvu"
   },
   "source": [
    "For more detailed look we can see that **between hour 1 and 4** there exists many **outliers** and most of them are **above 3000 range**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:42.080420Z",
     "iopub.status.busy": "2021-09-20T23:07:42.080183Z",
     "iopub.status.idle": "2021-09-20T23:07:54.517954Z",
     "shell.execute_reply": "2021-09-20T23:07:54.517022Z",
     "shell.execute_reply.started": "2021-09-20T23:07:42.080393Z"
    },
    "id": "D8_cWIK4JVWo",
    "outputId": "944c75a7-a8e6-4b67-f7e1-861339189c6b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For more detailed look we can see that between hour 1 and 4 there exists many outliers and most of them are above 3000 range.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.scatter(traffic_df['1_to_4'], traffic_df['day_of_week'])\n",
    "ax.set_xlabel('1 to 4')\n",
    "ax.set_ylabel('day of week')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nuJpngPrgOlp"
   },
   "source": [
    "Here we have used **Z-Score** to detect all the outliers present in the dataset.\n",
    "\n",
    "The intuition behind Z-score is to describe any data point by finding their relationship with the Standard Deviation and Mean of the group of data points. Z-score is finding the distribution of data where mean is 0 and standard deviation is 1 i.e. normal distribution.\n",
    "\n",
    "**how does this help in identifying the outliers?**\n",
    "\n",
    "> While calculating the Z-score we re-scale and center the data and look for data points which are too far from zero. These data points which are way too far from zero will be treated as the outliers. In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.\n",
    "\n",
    "We will use Z-score function defined in scipy library to detect the outliers.\n",
    "\n",
    "1.   The **Z-score** is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:54.519783Z",
     "iopub.status.busy": "2021-09-20T23:07:54.519481Z",
     "iopub.status.idle": "2021-09-20T23:07:56.312763Z",
     "shell.execute_reply": "2021-09-20T23:07:56.311615Z",
     "shell.execute_reply.started": "2021-09-20T23:07:54.519751Z"
    },
    "id": "LFIUyY3kJVWp",
    "outputId": "dda113ea-795e-4033-e779-521ba2e6cde2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           1_to_4    5_to_8   9_to_12  13_to_16  17_to_20  21_to_24\n",
      "0        0.358161  0.383019  0.490278  0.539823  0.529487  0.369380\n",
      "1        0.808870  0.122992  0.555000  0.738117  0.562474  0.642094\n",
      "2        1.532610  4.261234  4.389921  3.265818  3.365364  1.963056\n",
      "3        0.020038  0.218057  0.190940  0.311335  0.107250  0.028763\n",
      "4        0.355899  0.498493  0.565938  0.580044  0.588310  0.390490\n",
      "...           ...       ...       ...       ...       ...       ...\n",
      "7140386  0.354768  0.514221  0.598324  0.600275  0.584469  0.396930\n",
      "7140387  0.319712  0.088390  0.166421  0.015496  0.049627  0.237713\n",
      "7140388  0.093046  0.259565  0.080940  0.021036  0.149894  0.111055\n",
      "7140389  2.074285  7.203694  6.020935  5.271119  4.729114  2.944833\n",
      "7140390  0.334413  0.414477  0.514009  0.539823  0.460339  0.340041\n",
      "\n",
      "[7140391 rows x 6 columns]\n",
      "(array([      2,       2,       2, ..., 7140389, 7140389, 7140389]), array([1, 2, 3, ..., 2, 3, 4]))\n"
     ]
    }
   ],
   "source": [
    "timings=['1_to_4','5_to_8', '9_to_12', '13_to_16', '17_to_20', '21_to_24']\n",
    "z = np.abs(stats.zscore(traffic_df[timings]))\n",
    "print(z)\n",
    "threshold = 3\n",
    "print(np.where(z > 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BoMOsB7hihSH"
   },
   "source": [
    "In the below cell we are taking only the values which are between the range of **-3 to 3 in Z-Score**.\n",
    "\n",
    "All other values are outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:07:56.315948Z",
     "iopub.status.busy": "2021-09-20T23:07:56.315496Z",
     "iopub.status.idle": "2021-09-20T23:08:04.898919Z",
     "shell.execute_reply": "2021-09-20T23:08:04.897763Z",
     "shell.execute_reply.started": "2021-09-20T23:07:56.315877Z"
    },
    "id": "JTaSvaAHJVWp"
   },
   "outputs": [],
   "source": [
    "traffic_df[timings] = traffic_df[timings][(z < 3).all(axis=1)]\n",
    "traffic_df[timings] = traffic_df[timings][(-3 < z).all(axis=1)]\n",
    "traffic_df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i41_dzcjjEok"
   },
   "source": [
    "Now when we are running the boxplot again we can clearly see that all the outliers that were present in the dataset have been removed.\n",
    "\n",
    "Hence we have now obtained a consistent and good performing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:04.901249Z",
     "iopub.status.busy": "2021-09-20T23:08:04.900878Z",
     "iopub.status.idle": "2021-09-20T23:08:16.394483Z",
     "shell.execute_reply": "2021-09-20T23:08:16.393555Z",
     "shell.execute_reply.started": "2021-09-20T23:08:04.901205Z"
    },
    "id": "sUB2zVu0JVWp",
    "outputId": "5a07a254-960c-459e-ae5c-597141442169"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings=['1_to_4','5_to_8', '9_to_12', '13_to_16', '17_to_20', '21_to_24']\n",
    "plt.figure(figsize = (20,8))\n",
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.boxplot(x=traffic_df[timings[i-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fNmmT-2Rj2NU"
   },
   "source": [
    "We can also see from the below cell output that the** data consistency have been improved in all the time bucket sections** where we have appied the **Z-Score method** and there is nearly no visible difference between the **mean, 75%, and maximum** value of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:16.396019Z",
     "iopub.status.busy": "2021-09-20T23:08:16.395766Z",
     "iopub.status.idle": "2021-09-20T23:08:19.773008Z",
     "shell.execute_reply": "2021-09-20T23:08:19.771845Z",
     "shell.execute_reply.started": "2021-09-20T23:08:16.395989Z"
    },
    "id": "H1PyDWiyjixg",
    "outputId": "bea4a764-6d0b-4ff6-f2d0-df8207a8b56d"
   },
   "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>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>1_to_4</th>\n",
       "      <th>5_to_8</th>\n",
       "      <th>9_to_12</th>\n",
       "      <th>13_to_16</th>\n",
       "      <th>17_to_20</th>\n",
       "      <th>21_to_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.572624e+01</td>\n",
       "      <td>4.012449e+00</td>\n",
       "      <td>3.885063e+00</td>\n",
       "      <td>2.985262e+01</td>\n",
       "      <td>1.335427e+00</td>\n",
       "      <td>6.518998e+00</td>\n",
       "      <td>6.073193e+01</td>\n",
       "      <td>2.552298e+02</td>\n",
       "      <td>4.552350e+02</td>\n",
       "      <td>5.455916e+02</td>\n",
       "      <td>5.171385e+02</td>\n",
       "      <td>2.138846e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.770013e+00</td>\n",
       "      <td>2.000232e+00</td>\n",
       "      <td>2.281844e+00</td>\n",
       "      <td>1.657622e+01</td>\n",
       "      <td>1.071629e+00</td>\n",
       "      <td>3.456660e+00</td>\n",
       "      <td>8.323963e+01</td>\n",
       "      <td>3.154451e+02</td>\n",
       "      <td>4.602179e+02</td>\n",
       "      <td>5.339154e+02</td>\n",
       "      <td>5.242792e+02</td>\n",
       "      <td>2.468112e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>1.282500e+02</td>\n",
       "      <td>1.640000e+02</td>\n",
       "      <td>1.435000e+02</td>\n",
       "      <td>4.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>1.355000e+02</td>\n",
       "      <td>3.105000e+02</td>\n",
       "      <td>3.905000e+02</td>\n",
       "      <td>3.545000e+02</td>\n",
       "      <td>1.267500e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>7.975000e+01</td>\n",
       "      <td>3.357500e+02</td>\n",
       "      <td>6.277500e+02</td>\n",
       "      <td>7.545000e+02</td>\n",
       "      <td>7.207500e+02</td>\n",
       "      <td>2.940000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>7.455000e+02</td>\n",
       "      <td>2.299750e+03</td>\n",
       "      <td>3.270500e+03</td>\n",
       "      <td>3.805000e+03</td>\n",
       "      <td>3.781250e+03</td>\n",
       "      <td>2.383750e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day_of_data   day_of_week  direction_of_travel  fips_state_code  \\\n",
       "count  6.901954e+06  6.901954e+06         6.901954e+06     6.901954e+06   \n",
       "mean   1.572624e+01  4.012449e+00         3.885063e+00     2.985262e+01   \n",
       "std    8.770013e+00  2.000232e+00         2.281844e+00     1.657622e+01   \n",
       "min    1.000000e+00  1.000000e+00         0.000000e+00     1.000000e+00   \n",
       "25%    8.000000e+00  2.000000e+00         1.000000e+00     1.300000e+01   \n",
       "50%    1.600000e+01  4.000000e+00         5.000000e+00     3.000000e+01   \n",
       "75%    2.300000e+01  6.000000e+00         5.000000e+00     4.400000e+01   \n",
       "max    3.100000e+01  7.000000e+00         9.000000e+00     5.600000e+01   \n",
       "\n",
       "       lane_of_travel  month_of_data        1_to_4        5_to_8  \\\n",
       "count    6.901954e+06   6.901954e+06  6.901954e+06  6.901954e+06   \n",
       "mean     1.335427e+00   6.518998e+00  6.073193e+01  2.552298e+02   \n",
       "std      1.071629e+00   3.456660e+00  8.323963e+01  3.154451e+02   \n",
       "min      0.000000e+00   1.000000e+00 -1.000000e+00 -1.000000e+00   \n",
       "25%      1.000000e+00   4.000000e+00  9.000000e+00  4.700000e+01   \n",
       "50%      1.000000e+00   7.000000e+00  2.700000e+01  1.355000e+02   \n",
       "75%      2.000000e+00   1.000000e+01  7.975000e+01  3.357500e+02   \n",
       "max      9.000000e+00   1.200000e+01  7.455000e+02  2.299750e+03   \n",
       "\n",
       "            9_to_12      13_to_16      17_to_20      21_to_24  \n",
       "count  6.901954e+06  6.901954e+06  6.901954e+06  6.901954e+06  \n",
       "mean   4.552350e+02  5.455916e+02  5.171385e+02  2.138846e+02  \n",
       "std    4.602179e+02  5.339154e+02  5.242792e+02  2.468112e+02  \n",
       "min   -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00  \n",
       "25%    1.282500e+02  1.640000e+02  1.435000e+02  4.500000e+01  \n",
       "50%    3.105000e+02  3.905000e+02  3.545000e+02  1.267500e+02  \n",
       "75%    6.277500e+02  7.545000e+02  7.207500e+02  2.940000e+02  \n",
       "max    3.270500e+03  3.805000e+03  3.781250e+03  2.383750e+03  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Geh7XDADgmhK"
   },
   "source": [
    "## **Pattern 1**\n",
    "\n",
    "On the basis of below bar plot between **Traffic count and Days of weeks** across all time frames we can conclude that:\n",
    "\n",
    "- The **maximum amout of Traffic occurs on Day 6 i.e saturday around the hours of 12 to 16 i.e. Afternoon Hours**.\n",
    "- Also the traffic on other days is almost similar and the **minimum amount of traffic occurs during eary morning hours between 1 to 4**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:19.774821Z",
     "iopub.status.busy": "2021-09-20T23:08:19.774471Z",
     "iopub.status.idle": "2021-09-20T23:08:20.638518Z",
     "shell.execute_reply": "2021-09-20T23:08:20.637385Z",
     "shell.execute_reply.started": "2021-09-20T23:08:19.774781Z"
    },
    "id": "GPdlai-FJVWq",
    "outputId": "c160f205-d599-4c03-9d52-c7e1a49cc93c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c142ee410>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings=['1_to_4','5_to_8', '9_to_12', '13_to_16', '17_to_20', '21_to_24']\n",
    "Week_Traffic = pd.DataFrame(traffic_df.groupby(['day_of_week'], axis=0, as_index=False)[timings].mean())\n",
    "\n",
    "legends=['1 - 4','4 - 8', '8 - 12', '12 - 16', '16 - 20', '20 - 24']\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "Week_Traffic.plot(x='day_of_week', y=timings, kind=\"bar\", figsize=(25,10), xlabel = 'Day of week', ylabel = 'Traffic count')\n",
    "plt.legend(title='Timings', labels=legends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:20.640451Z",
     "iopub.status.busy": "2021-09-20T23:08:20.640193Z",
     "iopub.status.idle": "2021-09-20T23:08:22.159382Z",
     "shell.execute_reply": "2021-09-20T23:08:22.158646Z",
     "shell.execute_reply.started": "2021-09-20T23:08:20.640423Z"
    },
    "id": "s3vlmFdBJVWq"
   },
   "outputs": [],
   "source": [
    "# We are renaming the very long direction name in this code cell\n",
    "\n",
    "traffic_df[\"direction_of_travel_name\"].replace({\"North-South or Northeast-Southwest combined (ATR stations only)\": \"Northeast-Southwest\", \n",
    "                                                \"East-West or Southeast-Northwest combined (ATR stations only)\": \"Southeast-Northwest\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EkQvIAN0hQj1"
   },
   "source": [
    "The below code cell helps us to observe the data more vividly and understand it better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:22.161657Z",
     "iopub.status.busy": "2021-09-20T23:08:22.161078Z",
     "iopub.status.idle": "2021-09-20T23:08:25.484324Z",
     "shell.execute_reply": "2021-09-20T23:08:25.483434Z",
     "shell.execute_reply.started": "2021-09-20T23:08:22.161611Z"
    },
    "id": "Igtf80OGJVWq",
    "outputId": "35003b23-86d0-476c-bc2f-e2823ca644cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUNCTIONAL CLASSIFICATION:\n",
      "{'1U': 1548157, '3R': 1392830, '3U': 1108945, '1R': 992054, '2U': 604921, '4R': 485936, '5R': 319145, '4U': 314540, '5U': 79926, '6R': 37096, '7U': 10688, '7R': 7716}\n",
      "\n",
      "FUNCTIONAL CLASSIFICATION NAME:\n",
      "{'Urban: Principal Arterial - Interstate': 1548157, 'Rural: Principal Arterial - Other': 1392830, 'Urban: Principal Arterial - Other': 1108945, 'Rural: Principal Arterial - Interstate': 992054, 'Urban: Principal Arterial - Other Freeways or Expressways': 604921, 'Rural: Minor Arterial': 485936, 'Rural: Major Collector': 319145, 'Urban: Minor Arterial': 314540, 'Urban: Collector': 79926, 'Rural: Minor Collector': 37096, 'Urban: Local System': 10688, 'Rural: Local System': 7716}\n",
      "\n",
      "FIPS STATE CODE:\n",
      "{12: 606214, 39: 455679, 51: 451780, 13: 355635, 55: 298451, 53: 266437, 16: 252638, 36: 245816, 40: 202741, 28: 194818, 29: 191551, 1: 183643, 49: 168084, 32: 158457, 15: 151351, 56: 147386, 26: 145656, 19: 144872, 6: 143013, 48: 142145, 30: 126627, 35: 119045, 10: 111765, 41: 106885, 8: 102146, 20: 98256, 5: 93827, 44: 91380, 2: 88231, 21: 85705, 42: 81326, 4: 79403, 27: 68663, 33: 62484, 17: 61227, 46: 58852, 18: 54364, 54: 51232, 24: 48707, 47: 47841, 34: 47731, 23: 44769, 37: 43709, 22: 42147, 31: 34635, 45: 32997, 38: 30383, 25: 30351, 50: 27861, 9: 19771, 11: 3267}\n",
      "\n",
      "DIRECTION OF TRAVEL:\n",
      "{1: 1944709, 5: 1932219, 3: 1414000, 7: 1402267, 9: 53078, 4: 40219, 8: 38485, 2: 25774, 0: 25728, 6: 25475}\n",
      "\n",
      "DIRECTION OF TRAVEL NAME:\n",
      "{'North': 1944709, 'South': 1932219, 'East': 1414000, 'West': 1402267, 'Northeast-Southwest': 53078, 'Southeast': 40219, 'Northwest': 38485, 'Northeast': 25774, 'Southeast-Northwest': 25728, 'Southwest': 25475}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('functional classification:'.upper())\n",
    "print(dict(traffic_df['functional_classification'].value_counts()), end = '\\n\\n')\n",
    "\n",
    "print('functional classification name:'.upper())\n",
    "print(dict(traffic_df['functional_classification_name'].value_counts()), end = '\\n\\n')\n",
    "\n",
    "\n",
    "print('fips state code:'.upper())\n",
    "print(dict(traffic_df['fips_state_code'].value_counts()), end = '\\n\\n')\n",
    "\n",
    "\n",
    "print('direction of travel:'.upper())\n",
    "print(dict(traffic_df['direction_of_travel'].value_counts()), end = '\\n\\n')\n",
    "\n",
    "\n",
    "print('direction of travel name:'.upper())\n",
    "print(dict(traffic_df['direction_of_travel_name'].value_counts()), end = '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7xRCkuvhd1k"
   },
   "source": [
    "In The below code cell i have created a function so that i can create a dictionary for the appropriate Feature to match with its Feature name.\n",
    "\n",
    "Example: Direction and Direction name can be matched, Functional Classification and Functional Classification name can be matched together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:25.486692Z",
     "iopub.status.busy": "2021-09-20T23:08:25.486402Z",
     "iopub.status.idle": "2021-09-20T23:08:25.494994Z",
     "shell.execute_reply": "2021-09-20T23:08:25.493758Z",
     "shell.execute_reply.started": "2021-09-20T23:08:25.486663Z"
    },
    "id": "avwatjtRJVWq"
   },
   "outputs": [],
   "source": [
    "def columnWithName(dict1, dict2):\n",
    "    ans = {}\n",
    "    for k, v in dict1.items():\n",
    "        for k1, v1 in dict2.items():\n",
    "            if v == v1:\n",
    "                ans[k] = k1\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IAekFhi-iHPM"
   },
   "source": [
    "In the below code cell i am making use of the above created function to match the Features with its namings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:25.497068Z",
     "iopub.status.busy": "2021-09-20T23:08:25.496672Z",
     "iopub.status.idle": "2021-09-20T23:08:28.788054Z",
     "shell.execute_reply": "2021-09-20T23:08:28.786967Z",
     "shell.execute_reply.started": "2021-09-20T23:08:25.496998Z"
    },
    "id": "Vmq-_15jJVWr"
   },
   "outputs": [],
   "source": [
    "functional_classification1 = dict(traffic_df['functional_classification'].value_counts())\n",
    "functional_classification_name1 = dict(traffic_df['functional_classification_name'].value_counts())\n",
    "functional_classification_dict = columnWithName(functional_classification1, functional_classification_name1)\n",
    "\n",
    "\n",
    "direction_of_travel1 = dict(traffic_df['direction_of_travel'].value_counts())\n",
    "direction_of_travel_name1 = dict(traffic_df['direction_of_travel_name'].value_counts())\n",
    "direction_of_travel_dict = columnWithName(direction_of_travel1, direction_of_travel_name1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iEWcS-E6iY2w"
   },
   "source": [
    "## **Pattern 2**\n",
    "\n",
    "In the below Bar plot we have created the Graph between **Direction of travel and The traffic count** on Diferrent days across all time Frames.\n",
    "\n",
    "1. With this we can Easily conclude that **Northeast-Southwest Direction have the maximum amount of traffic specially on Hours 1 to 4, 4 to 8 i.e. early mornings and at Late nights between hours 20 to 24**.\n",
    "\n",
    "2. We can also conclude that **The traffic on time range 8 to 12 is extremely less irrespective of Direction of travel**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:28.790672Z",
     "iopub.status.busy": "2021-09-20T23:08:28.790213Z",
     "iopub.status.idle": "2021-09-20T23:08:30.552828Z",
     "shell.execute_reply": "2021-09-20T23:08:30.551841Z",
     "shell.execute_reply.started": "2021-09-20T23:08:28.790626Z"
    },
    "id": "gI6KdQFGJVWr",
    "outputId": "32843a6d-d95c-494f-8046-acf877fb7295"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c1e5f6950>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1584x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "legends=['1 - 4','4 - 8', '8 - 12', '12 - 16', '16 - 20', '20 - 24']\n",
    "direction_EDA = pd.pivot_table(data=traffic_df, index=['direction_of_travel_name'], values=timings)\n",
    "direction_EDA.plot(kind='bar', figsize=(22,8), xlabel = 'Direction of Travel', ylabel = 'Traffic count')\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.legend(title='Timings', labels=legends)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M2enINQYkCq6"
   },
   "source": [
    "We can also conclude on the basis of the below density graph that **highest density of traffic is in Direction number 1 and 5 i.e. North and South across all the times included.**\n",
    "\n",
    "**More Proof for Pattern 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:08:30.554896Z",
     "iopub.status.busy": "2021-09-20T23:08:30.554578Z",
     "iopub.status.idle": "2021-09-20T23:09:05.017379Z",
     "shell.execute_reply": "2021-09-20T23:09:05.016071Z",
     "shell.execute_reply.started": "2021-09-20T23:08:30.554856Z"
    },
    "id": "gTQRjyjZJVWr",
    "outputId": "bf0905f9-1cc1-4657-b1d3-61b505b53ec2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='direction_of_travel', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.figure(1)\n",
    "\n",
    "sns.distplot(traffic_df['direction_of_travel'],color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IMa3hZkOkrqh"
   },
   "source": [
    "## **Pattern 3**\n",
    "\n",
    "In the below Graph we have created the Graph between **Lane of travel and The traffic count** on Diferrent days **across all time frames**.\n",
    "\n",
    "1. With this we can Easily conclude that **Lane number 8 have the maximum amount of traffic specially on Hours 1 to 4 and 4 to 8 i.e Early Mornings**, and the **Least amount of traffic on Lane number 1**.\n",
    "\n",
    "2. We can also observe that on the **time range of 8 to 12 the traffic is very least irrespective of Lane number**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:05.019114Z",
     "iopub.status.busy": "2021-09-20T23:09:05.018865Z",
     "iopub.status.idle": "2021-09-20T23:09:05.942737Z",
     "shell.execute_reply": "2021-09-20T23:09:05.942007Z",
     "shell.execute_reply.started": "2021-09-20T23:09:05.019085Z"
    },
    "id": "WE_Md7NXJVWs",
    "outputId": "61316907-493d-4745-e128-68c57fc91d7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c10701a90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "legends=['1 - 4','4 - 8', '8 - 12', '12 - 16', '16 - 20', '20 - 24']\n",
    "direction_EDA = pd.pivot_table(data=traffic_df, index=['lane_of_travel'], values=timings)\n",
    "direction_EDA.plot(kind='line', figsize=(20,8), xlabel = 'Lane of Travel', ylabel = 'Traffic count')\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.legend(title='Timings', labels=legends)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MYSIg_Q5nNq3"
   },
   "source": [
    "## **Pattern 4**\n",
    "\n",
    "In the below Graph we have created the Graph between **Functional Classification and The traffic count** on Diferrent days **across all time frames**.\n",
    "\n",
    "1. With this we can Easily conclude that **Urban Principal Arterial: Interstate and Other Expressways have the maximum amount of traffic specially on Hours 1 to 4 and 4 to 8 i.e Early Mornings and Late night between hours 20 to 24**, also the **Least amount of traffic on Lane number 1**.\n",
    "\n",
    "2. We can also observe that on the **time range of 8 to 12 the traffic is very least irrespective of Functional Classification** and on the **Rural Local system we have the least Traffic**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:05.947463Z",
     "iopub.status.busy": "2021-09-20T23:09:05.946599Z",
     "iopub.status.idle": "2021-09-20T23:09:07.879230Z",
     "shell.execute_reply": "2021-09-20T23:09:07.878552Z",
     "shell.execute_reply.started": "2021-09-20T23:09:05.947419Z"
    },
    "id": "M5gtnv3HJVWs",
    "outputId": "6f4c14cb-95da-4e20-818b-366cf1959fc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c106aa190>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t7XrkkUcKGg4AAAAAACCsBjr79do3wn8pyM2/alDdhVN05e+/x3YUAAAAAADKSpALVP/4OtsxCs692JczxX37/4MgKLliYkq96nL22Y6BUeh0dsoLYooHk21HscKZ5Mr/YEzpFwZsRwml/a/v066FO/TOD860HaXkrPz2Cu1fyftjGJjxRtGHx8mZQGEaGEtDfsVNnjz5uP+mTJmiCy+8UH/4h3+o8ePHa+HCcF6OFgAAAAAAoNBWfnuFBjpL40uL5V9dol2v7rQdAwAAAACAsrJ3xR517uywHaOwHMm/MZq3qNQK04ECtbvbJRMcvRClxkht7hal1GM7iTX+VVE5M4Y8D7PiLP/aUuWyOdsxSsquV3dq1XdW2o4BSao2ij5cLaeGwjQw1gr2qrvkkkv05ptvFurmAAAAAAAAQqtta6vW/2St7RhDFuQCzf+T59TR2G47CgAAAAAAZaMcp0x7l0Xk1OZPmS41PU6TUk53/sLS6n3jfwQmpxavQRklbUexJnJ3lRSznSKc2ra0avMvG2zHKBndB7u14M/m2Y4BSYoZxR6qljPZHXxbAAVXsNJ0S0uL0ul0oW4OAAAAAAAglIIg0OIvLlKQLa0vDVNdSc395NNKdlXul0wAAAAAABRKb3OPdry43XaMwnIl77rSnjKdUVKdzi7bMVBAWZNSq7tJOWVtR7HCmeAocnuV7Rih9do3lyszQF9tMNl0VvP/+DkNdPTbjoKIFH0wLmcahWnAliFfw2Hr1q0nXN7X16dt27bplVde0WWXXVaoXAAAAAAAAKG046VG7V2223aMEelobNcLf/qc7vnBR+S4XPYPAAAAAICR2vizeuUyOdsxCsq7MiJnwpHjBUEQlFxpusNtVGCOKtcGYsp0GUg5PWrTVtVlL5CpwD+oNyui7Ja0spsytqOETs/Bbq370Rpd+QdX244Saq99fZkOvrnfdgx4UvSBarlnDrmyiRHIaED9TrvG586wHQUhNeRX4De+8Y2TrnMcR1deeaUefPDBgoQCAAAAAAAIo0wyo6V/v8h2jFHZtWinVnx9ma77qxtsRwEAAAAAoCTlsjnVP7HedozC8iX/2tKeMt1nWtXvtOcvLK1fAafQ77SpM9il2tw7bUexInJHlfr39kg9pXX1u7Gw6ruv6+IH3q1YDRO5T2THK41663urbMeAK0Xvj8udTmG6mJKmSy3uJuVMWiZgmjdObMivws997nPHLTPGKB6Pa/LkyaqqYscDAAAAAADK29ofvqXEnoTtGKP21r+9oboLpuiCD19oOwoAAAAAACVn16Kd6jnQbTtGQXlXR2WqS3fKdE4Zdbg7bMdAkXW7++UHVRoXnGY7ypgzcUfRu6qU/Fmf7Sihk+xK6s1/fUPX/fWNtqOETte+hF58dL7tGHCkyH1xuTN920nKWq9pUZu7VTKHTy5pd7ersX2tLtEllpMhbIZcmj7//POLmQMAAAAAACDUept79Ma/rLQdo2Be/ssFqp1Rq2mXVN6XTAAAAAAAjEb9Y2ttRyismORfU9pTpjudXcqa1JEFgZgyXaba3UZ52ZhiQY3tKGPOPdeXe5mv7Nq07Sihs/ZHq3XJ71yuCWdOsB0lNLKprOb90VwlEwO2o1Q2I0VmV8k7n8J0sQQKlHD2qMvdm7/CBDrUs8tKJoSbM/gm+Xp7e/Xmm29qwYIFWrBggd5880319vYWIxsAAAAAAEBoLP+HpUr3ls8XEtlkRs998mn1HuK4DgAAAAAAQ5XY06ldr+60HaOg/PdFZWJHGsZBEFhMM3wDJqEetyl/IYXp8mUCtbqblVZlTlyO3FolU8MT/FjZZFYrv73cdoxQWfbVJWpe2zT4hiiqyJ1V8mZFbMcoWzll1eZuOb4w/T8uPueqMU6EUjDkSdOS9MILL2ju3LlKp/O/IPR9X7Nnz9aHPvShgoYDAAAAAAAIg6a1B7Xp5xttxyi4nqYePf/7z+i+J39TXnRYh4kAAAAAAKhIG56sPzzFuFzEjbyrS3fKdKCc2t3ttmNgjOVMRi1eg6ZlLpWrypreaqJGkdlxJX/SW17vRQWw6ecbdcUnrlLdBVNsR7Fu+wvbtPYHb9mOUfH822LyLqcwXSxZpdTiNijl9OSvOOpqE6X0mQZjZ8jfhi1dulS//OUvdcEFF+iWW27R6aefLkk6ePCgXnnlFf3iF79QdXW1rrvuuqKFBQAAAAAAGGtBLtDiv11oO0bRHFx9QK/+zSv64Ndu4wAiAAAAAACnkE1ltfFn9bZjFJR/bVQmUrrHAxLOXmVMv+0YsCBjBtTqbtLU7CwZObbjjCl3uifv6ogyr6dsRwmXQFr+taW697/us53EqsSeTr30Fy/YjlHx/Jui8o85KSkIAo7BF0hKPWrxNilrkvkrjipMAycz5E8Nr7zyii644AI9+uijuuSSSzRlyhRNmTJFl1xyif7sz/5MF1xwgV5++eViZgUAAAAAABhzm59uUNOag7ZjFNXGn9Vr3Y/W2I4BAAAAAECoNS7Ypv7WPtsxCsZMMPKuLN0JmCn1qsvZZzsGLEo6XWp3tyuowJHL/s0xmbrKKosPxa6FO7Rv5V7bMazJJDOa9+m5SnUlB98YReNdG5V/XSxvGYXpwukzbWr21h9fmJYoTGNIhrz3PHTokC6//PITvniNMbr88st16NChgoYDAAAAAACwKdWb0vKvLLEdY0ws+ftF2rNst+0YAAAAAACEVv1j62xHKCj/upiMV5rtokCB2t3tkgmOXogK1OscqsjyvPGMovfGh9H8qhzLvrJYQVCZbwhLv/SqDtU3245R0bz3RBS5mcJ0MQQK1OXsU6u7SYHJ2Y6DEjbkXWc8HldLS8tJ17e0tCgejxckFAAAAAAAQBi8+d3X1Xuo13aMMRFkA83/o7lK7Om0HQUAAAAAgNBp39ZWVtNLTa0j9zLfdowR63GalHK68xfSR6tYCXe3+kyr7RhjzjndlX991HaM0Gle26Tt87bajjHmts7drPU/Xms7RkVzL/MV+VBV3jIK04URKKd2d7s63V3s7zFqQy5NX3LJJVq0aJFWrlyZdzZOEARauXKlFi1apEsvvbQoIQEAAAAAAMZaYk+nVv/nm7ZjjKmBzgHN/fivlOpJ2Y4CAAAAAECo1P+0zKZM3xCVcUqzdZRRUp3OLtsxEDJt7lYlTffgG5YZ79qonDNc2zFCZ8XXlymbztqOMWY6dnbolb960XaMiuZe7CtyF4XpYsgqrUPuRvU6x0xRr8yB8igAb6gb3nfffdqxY4d++MMf6qmnntLUqVMlSYcOHVJ3d7fOOOMM3XfffUULCgAAAAAAMJaWfmmxssnKObD+a21b27Tg0Xm6+3v3luyXpwAAAAAAFFK6P61NP99oO0bBmCmO3FmlO2W6w21UYI46ZhOIqZNQYHJqcRt0WuZSeYrZjjNmjGMUmV2lgf/skTK204RH584ObXyyXpf81mW2oxRdZiCteZ9+lkEYFrnneYrcW5VXkKYwXRhp9avF26iMGchfwb4fozDkSdPjxo3TF77wBX3sYx/T2WefrZ6eHvX09Ojss8/WAw88oC984Quqrq4uZlYAAAAAAIAxsWfZbjUu2GY7hjU7Xtyuld9ebjsGAAAAAAChsHXuZiW7krZjFIx/Y6xki1x9plX9Tnv+wtL8VU5pnJvVVeN79c5YUozSHLqcSavFa1CuwtrDTp0r/wOVUxQfqtf/aYVSveVfJF78xUVqbWixHaNiOTM8Re6L5w0goTBdGAOmU03e2uML01JZ7vsxdoY8aVqSfN/XBz/4QX3wgx8sVh4AAAAAAACrcpmclvzdItsxrHvjn1eq7oIpOveu821HAQAAAADAqvrH19mOUDDO6a68C0pzynROGXW4O2zHKLqJbka/c1q74u7hsvSb3VV6uWO8aIgNTdr0qdXdoinZi2Qq6DHzr44quzWt3K7Ku3LeyfS19Gn1f7yp9/3Z+21HKZrNv2rQhp+utx2jYjnTXUXvj8t4FKYLrcc0qd1tlAwnDqHwhjxpurOzU1u3bj3p+q1btyqRSBQkFAAAAAAAgC31P12nti2ttmOEwoufm6+WhkO2YwAAAAAAYM2h+mY1r22yHaNg/JuitiOMWKezS1lz1NTYMuxReSbQfVMSbxemJemq8f26fVK3TDn+wkUy4HSowyn/gv2xIvfEpdJ9iRfF6u+vUm9Lr+0YRdG+vU0L//ol2zEqlnOGq+jHqmX8/II0henRCRSow9mhdm87hWkUzZAnTT/11FNqb2/X5z//+ROuf+aZZzRp0iR9/OMfH9LtzZs3T6tXr1Zzc7M8z9OMGTN033336cwzz3x7myAINHfuXC1ZskR9fX165zvfqYceeihvm97eXj355JNat+7wmZ2XXnqpHnzwQcXj8be32bdvn5544gnt3LlT1dXVuuGGG3T33XfzJgUAAAAAAPIMdPZr5TeX244RGpn+jOZ+8mk98OzDik+OD/4DAAAAAACUmbKaMj3dlTuzNKdMD5iEetxjyutlV/kIdFttl6ZFMsetuWxcvzwT6Pm2CQrK7xcvih73oHzFNT53uu0oY8aZ6ChyW5VSc/ttRwmNdG9ab/zza7r572+xHaWg0v1pzfvDZ5XuS9uOUpHMVEfRB6tlorwfF1JOGbW6WzTgdOSvCFSG+3zYNORJ01u3btW73/3uk66fNWvWKSdRH2vLli26+eab9Vd/9Vf63Oc+J8dx9K1vfUu9vUfO7nnhhRf04osv6sEHH9QXvvAFjR8/Xt/+9rc1MDDw9jb/+Z//qd27d+szn/mMPvOZz2j37t36wQ9+8Pb6/v5+ffvb39b48eP1hS98QQ888IAWLFigl17iTBsAAAAAAJBv5bdWaKBzYPANK0j3vi7N+/Szyqa5tCcAAAAAoLIku5La8swm2zEKxr8pZjvCiATKqd3dbjtG0V1S3a9Lxp38uNSs6gHdW5eQw8TpIetwGtVvOgbfsIx4l0bknjfkGZoVYcNP16tzV3k9Dxb9zctq29pmO0ZFMpMdxeZUy1TR4i2kjAbU7K2nMI0xMeTSdE9Pj8aNG3fS9fF4XF1dXUO+40cffVTXXnutzjzzTJ111ln6+Mc/ru7ubm3ffviDbhAEeuWVV3THHXfoyiuv1JlnnqlHHnlEAwMDev311yVJBw8e1IYNG/Tbv/3bmjlzpmbOnKnf+q3f0vr169XUdPgMw9dff12pVEqPPPKIzjzzTF155ZW6/fbb9dJLLykI+CAJAAAAAAAOa93SovWPrbUdI5T2r9ynxX+70HYMAAAAAADG1OZfNZTNFE9nhid3emkWKRPOXmVMeU/Oneandduk7kG3uyCe1H1TOuVSnB4aI7W6m5VS7+DblpHIXVVSnJbhr+UyOa34+jLbMQqm4akN2vTURtsxKpKpMYrOqZapHnLlEkOQNF1q8tYpbfryV1CYRpEM+RVcW1ur3bt3n3T97t27NWHChBEHGRgYUBAEiscPX+q1tbVViURCF1100dvbRCIRnXfeeWpsbJQkNTY2KhqNaubMmW9v8653vUvRaDRvm3PPPVeRSOTtbS6++GJ1dnaqtbV1xHkBAAAAAED5CIJAS764SEGWL5xOpv6xdWV1SWIAAAAAAE4lCIKy+newf1PUdoQRSalXXc4+2zGKKubk9JEpnfKOKoadagbgu6pSun9qp3zDcayhCExWLV6DskrZjjJmTLVzuDiNt217boua1h20HWPUWre0aNH/ftl2jIpkxhtFHx4nZwKF6ULqNS1qduuVMyc4SY3CNIpkyKcRXn755XrllVd08cUX64orrshb99Zbb2nFihW6+eabRxzkySef1Nlnn/12ATqRSEjScUXsCRMmqKOj4+1txo8fL2OOvEKMMRo/fvzbP9/V1aXa2trjbuPX66ZMmXLCPNu2bRvx74LSxt8eAICRYz8KAChVTcsPau/yPbZjhN6iv3lZfbE+TbqkznYUAADy8O9RAABGjv3oibXXt6ltS3kMYnPP9+SeUXpTpgMFane3S0eXg8tu6mSgeyYnVOPl8paaQX7Hd8RS+s0pHXqqpUapgALfYLImqRZ3k6ZmZ8mRazvOmPDO95W9xFd2fXlMyy+El/+/Bbr66+/P65kVwljtRzP9GS3/o8XKDGTG5P5wlGqj6MPVcmp4vy2UQIESzh51uXuLej8trS181q1Q55577knXDflT8d13362Ghgb9+7//u8444wydeeaZkqT9+/frwIEDOuOMM3TPPfeMKODPfvYzbd++XZ///OflOOF4cznVg4bytW3bNv72AACMEPtRAECpygxktPwHr9qOURKCbKB1X1qtB+Y+rAlnTbQdBwAASfx7FACA0WA/enIvfGer7QiFYST/ppjtFCPS4zQp5XTnLyyrwrT0/gm9mlk1sgnIZ8fSemBqh/67pVYDuXB0bcIs5XSrXds0OXu+TLk9kU4icluVBnZlFHQxlVyS2ta2KrLf0ztunlGw2xyr/WgQBFrwZ/PUu6en6PeFY8SMYg9Vy5lcGSdcjIWcsmp3t6nPKf7JaVPqpvBZF8cZ8qemqqoq/dVf/ZXuvvtuSdLatWu1du1aSYcL1f/rf/0vxePxYQf42c9+plWrVulzn/tc3tTniRMPf/HW1dWVt31XV9fb6yZOnKju7m4FR12XJAgCdXd3v73NhAkTTngbv14HAAAAAAAq25ofvqXEnoTtGCWjv71fz33yGaX7KueSpgAAAACAytLf3qft88qjNO1e7MuZcqTodXS/IswySqrT2WU7RlG9I5bU9RN7R3UbZ0Qzemhqh+JObvCNoT6nVQmncq42Z2JGkXuG3+UqZ8u+ukS5bOm9XjY+Wa8tT2+yHaPyRKTog3E50yhMF0pWKR1y648vTJfGxxOUiWFdfyUajWr27NmaPXt2Qe78ySef1KpVq/Tnf/7nOv300/PW1dXVaeLEiWpoaNA73/lOSVI6nda2bdv0G7/xG5KkmTNnKplMqrGxUe9617skSY2NjUomk5o5c+bb2/ziF79QOp2W7/uSpIaGBtXU1KiujkvJAgAAAABQyXqae7TqX1bajlFyWhoO6aU/f0F3fPeegl/OEgAAAAAA2xr+e4OyqaztGKPnSP6N0bxFpfDv+ECBOtxGBSZ79MKymjI9wc1q9uSEjv5zBIE0kj/P1EhGD01r15OHatWTpdg3mC53r/ygStXBVNtRxoT7Tk/eeyLKrGIAgiS1bW7Vlqc36cKPXmw7ypC1bDykV//PK7ZjVB5Pij5QLffMYdUrcQop9ajF26SsSeavKLN9PMLP2vU5Hn/8cS1fvlyf+MQnFI/HlUgklEgkNDAwIOnwB/UPfvCDWrBggVavXq39+/frv/7rvxSNRvXe975XknT66adr1qxZeuyxx9TY2KjGxkY99thjuuSSS3TaaadJkq6++mpFIhH913/9l/bv36/Vq1frhRde0K233loS/xgAAAAAAADFs+JrS5TuS9uOUZK2Pb9Vq77zuu0YAAAAAAAUVJALVP/T9bZjFIR7qS+ntvSmTPebNvU77fkLy6je4SrQh+s6FXePvqr6yArTv1bnZzVnaocmuGVQ9h8Dbe42DZjKufKc/4GYzGRrFbHQee2by5QZyNiOMSTJ7qSe//SzyiZ5bY8pV4reH5c7ncJ0ofSZNjV7648vTEtltY9HabD2yn711VclSd/61rfylt9zzz1vT7K+/fbblU6n9dOf/lS9vb2aMWOGHn30UcVisbe3/8QnPqEnnnhC//iP/yhJuvTSS/XQQw+9vT4ej+vRRx/VT3/6U33pS19SdXW1br31Vt16663F/QUBAAAAAECoNa05qE2/aLAdo6S99o1lqrugTjNufZftKAAAAAAAFMSeZbuV2N1pO8bouZJ/fSxvUSkMlsspow53h+0YRfXB2m6dEc0vbBbiT1PrZzVnWrueOFSrzgxFv1MygVrdTTotc5k8xQbfvsQZ3ygyu0rJH/Uenuha4br3d2v9j9foik+9x3aUUwqCQK98foESuzptR6ksjhS5Ly53pm87SVkIFKjb2a9OZxflaISGtU9J//Ef/zHoNsYYzZ49++0S9YlUV1frE5/4xClv56yzztJf/uVfDjsjAAAAAAAoT0Eu0OIvLrQdoyy88Jnn9bGn52jyeXW2owAAAAAAMGr1j62zHaEgvCsjciYcmSwbBEFJlKY7nV3KmtSRBYHKqmR1cbxfV4zvL9rtT/RymjO1Q08eqlUbxelTypmMDnkbdVrmUjn26lNjxj3Tk3dtVJllJ5jyWoHe+M7ruuhj71ZsYnhL8+t/vFbbnt9qO0ZlMVJkdpW88ylMF0KgnNrdRvU6zbajAHm49gIAAAAAAKg4m3/VoKY1B23HKAvp3rTmfuJpDXQW7ws/AAAAAADGQk9Tt3a8vN12jNHzJf/aaN6iUihMD5iEetym/IXhjz1kU/y0bp/UVfT7Ge/l9NC0dk3100W/r1KXMf1qcTcpUM52lDHhXx+VOY2qmCQlEwN689/esB3jpJrrm7T0S6/ajlFxIndWyZsVsR2jLGSV1iF34/GFaabdIwTYEwIAAAAAgIqS6klp+VeX2I5RVhK7OzXvj55TLlMZXzABAAAAAMrThifrFWRLv83jXR2Vqc6fMh12h6dRlkFh/SSiJqeP1CXkH9XSKeafpdoN9NC0Dp0eoTg9mKSTUIezQ0EFNPmMaxS9Ny65tpOEw9ofrlb3geKfyDBcycSA5v3hXGVTWdtRKop/W0ze5RSmCyGtfjV765R0EvkryuzqEShdlKYBAAAAAEBFWfWvr6v3UK/tGGVn77LdWvrlV23HAAAAAABgRHKZnDY+sd52jNGLSf41pTdlOuHsVcaU61WsAt01uUuT/PwCZLH/LDEn0ANTO3RWNFXcOyoDPW6Tup0DtmOMCWeKK/+mmO0YoZBNZrTy2ytsx8gTBIFe+osF6tqbGHxjFIx/U1T+1fn7zlI44SiMBkynmr11ypiB41eG/+MIKsSQS9NLly7Vv/3bv510/b/9279pxYpw7UgAAAAAAACOltjTqTX/8abtGGVr7Q9Xa+N/19uOAQAAAADAsO18pVE9TT22Y4ya/76oTOxIK6kUSl8p9arL2Wc7RtFcPb5P58WTVu476gT6zSkdOidq5/5LSaezU32mzXaMMeG9LyJnOuOmJWnTzzeqbWur7RhvW/vD1WpcsM12jIriXRuVf13+iQRBEJTECUdh02OadMjdqJzJ2I4CnNKQS9OLFy/WhAkTTrq+pqZGixYtKkgoAAAAAACAYlj6pVe5rGGRLfrCyzr4VmVM5gEAAAAAlI/6x9fZjjB6cSPv6tKaMh0oULu7XTLB0QvLxvRoSjfV2C3jRxzp/qmdmhmjOH1KRmpztyil0j95YjDGGEVmx6WI7ST2BblAy7+21HYMSdLB1Qe07P8uth2jonjviShyM4Xp0QoUqMPZoXbvmP05EFJDLk03NzfrrLPOOun6M844Q83NzQUJBQAAAAAAUGh7lu1W44LttmOUvWwqq+d+/xn1NHXbjgIAAAAAwJAk9nRq9+JdtmOMmn9tVCZSWkWvHqdJKeeYYwil9Suc1Dg3q3vrEnKO+n1sDf72jHTflE6dXzVgJ0CJCExOLV6DMir/grlT4yhya5XtGKGw8+VG7X/D7rT7gc5+zf/jucplclZzVBL3Ml+RD+W/BihMD19OGbW4Dep2GaSC0jHk0rQxRj09Jz+bqqenR7kcb9wAAAAAACB8cpmclnxxoe0YFaOvpVdzP/m0MgNp21EAAAAAABhUOUyZNuONvCtLa2xsRkl1OrtsxygKR4HunZxQtXukRxMEks0unmuke+sSuijeby9ECcialFq8BuVU/ler8y6PyHmXZztGKCz7v4sVWDqrIcgFevGz89W9nyEUY8W92FfkLgrTo5XRgJq99RpwOvJXMGwaITfk0vT06dO1atUqpdPHf9mVTqf1xhtv6JxzziloOAAAAAAAgEKof3yd2ra22Y5RUQ6tb9bLn3/R2pcNAAAAAAAMRSaZUcN/b7AdY9S866MyXumUvQIF6nAbFZjs0QvLxk01PTo7lt+vCUMXzzHSPZO7dEl1n+0ooZY2vWpztygopyflSUTvrpKqQvDktKxpzUE1vrDNyn2v/v4q7Xxlh5X7rkTueZ4i91blFaQpTA9f0nSpyVuntDlmfxKobK4YgfI15NL0nXfeqQMHDujrX/+6Vq9eraamJjU1NWn16tX6h3/4BzU1NemOO+4oZlYAAAAAAIBh6+/o12vfXG47RkXa8vQmrf7eKtsxAAAAAAA4qe3zt6m/vbQn75paR95lpTVlut+0qd9pz19YJiWrC+IDunpCeEvJxkh3Tu7WlePCmzEM+p32sp2EfjQzzlHkjqrBN6wAy/9hqbLpsZ0wfmDVfi3/h6Vjep+VzJnhKXJfXMahMD0avaZFzW69cuYEV5rkoUQJGPI1Fi666CL93u/9np544gn9+7//e966WCym3/md39GsWbMKHhAAAAAAAGA0Vn5ruZKJAdsxKtayry7R5PPr9I6bZ9iOAgAAAADAceofX2c7wqj5N0TzCmBhl1NG7W6j7RhFMdnL6I5JXXnLgiAcU6aPdeukbnkm0Ovd1bajhFa3u19+UKVxwWm2oxSVd5Gv7BZf2Y0nKEBWkM4dHWr47w1695xLx+T++tr6NP+P5yrIlv9E8zBwpruK3h/PuyoDhenhCRQo4exRl7vXdhRgVIZcmpaka665Rpdffrk2btyolpYWSdKUKVN08cUXKxaLFSUgAAAAAADASLVublH9Y6X/5WdJC6T5f/q8Hnh6jmpnTrKdBgAAAACAt7VtbdWBN/bZjjEqZoojd5ZvO8awdDq78qdTBiqLyZS+yekjUzoVdY4UIMNamP61m2t75DuBliWqVRZ/hCJodxvlZWOKBTW2oxRV5PYqDezJKOiu7ALvym+v0AUfuVB+vLjT+4NcoBcfnaeepp6i3g8Oc85wFf1YtYyf/z5HYXrocsqq3d2mPqfVdhRg1Jzh/kAsFtOVV16p22+/XbfffruuvPJKCtMAAAAAACB0giDQ4i8uUpCr7AP9YZDqSmruJ59WsitpOwoAAAAAAG8rhxOt/RtjJVX6GjAJ9bhN+QtLJ/4pBLpjUpfq/Gze0lL401w3sVc31fTocHsdxzGBWtxNSqvPdpKiMlVGkburbMewrq+lV2v+862i38+qf31duxfvKvr9QDJTHUUfrJaJlsAbckhlldIhd8PxhWl2GyhRwy5NAwAAAAAAlILGBdu1b8Ue2zHwPzoa2zX/T55TLpuzHQUAAAAAAKX7Utr0y422Y4yKc7or74LSmTIdKKd2d7vtGEVx5bh+XVRduieLv29Cn26t7RYNuBMLTFYtXoOySg++cQlzZ/ryrijuhOVS8Nb3VqmvrXgl+X2v7dHKby4v2u3jCDPZUWxOtUwVhemRSqlXTd46pZzu/BVlcpUIVCbvZCs+9alPyRij7373u/I8T5/61KcGvTFjjL73ve8VNCAAAAAAAMBwZQYyWvqlV23HwDF2v7pTK/5hqa77XzfajgIAAAAAqHBbntmsVHfKdoxR8W+K2o4wLAlnrzKm33aMgjszktIHarsH33AYInuzim/JKlNj1HuJpyBS/GbaleP75ZlAL7RPUEAT7jgZM6BWd5OmZmfJlPGMSv+WmLI7Mwo6KnfwQaonpTf++TXd9MUPFvy2e1t6Nf9PnufqiGPA1BhF51TLVJfv67XY+kyb2twtCswJ3g/YTaCEnbQ0fffdd0uSHMfJ+38AAAAAAICwW/ODt9S1N2E7Bk7grX9fpboLpuiCj1xkOwoAAAAAoILVP77OdoRRcc525c4snSnTKfWqy9lnO0bBxZ2cPlyXkHtUeSwIJDOKMll0d1Y1rx6eaBzdL0UO5NR5S0S5ePEbapeOG5BnpOfbJihHI+44SadL7dquSdlzZcr08TERo8jsKiV/3FvRg8frH1+nyx65UjXn1BTsNnPZnBZ85nn1tfQW7DZxYma8UXTOODkTKEyPRKBA3c4BdTo7KUejLJ20NH3FFVdo0qRJb5emZ8+ePWahAAAAAAAARqqnuUervrPSdgycwsuff1G1Mydp2iWn2Y4CAAAAAKhAzeubdKi+2XaMUfFvjtmOMGSBArW72yUTHL2w5ItYRoHurevUeC9/AudoCtNeW04Tl6XzlvkdgSbNS6rjloiyNcUvAF5cPSDPBHqmdSLF6RPodQ7JC6o0MXe27ShF457tybsmqsyKpO0o1uTSOb32jWW6418KN2T0jX9+TXuX7ynY7eEkqg9PmHZqKUyPRKCc2t1G9Tql/TkJOJWTvjv83d/9nerr69/+/2984xvatGnTmIQCAAAAAAAYqeVfXaJ0X3rwDWFNNpnRc598Wr3NPbajAAAAAAAq0PqfrLUdYVScGZ7c6SedkRc6PU6TUk53/sIy6OJeP7FH58QKdwzK6Q9UszAlkzl+ndsrTZqfkt+cO35lEZwfT+qjUzrlmQoeNXwKCXe3+kyr7RhF5d8YlZla2aXTrc9uVnN9U0Fua8+y3Xr9n14ryG3hFGJGsYeq5dS5tpOUpKzSOuRuPL4wza4AZeake7dIJKJk8sgZQ1u3blVXV9eYhAIAAAAAABiJg6sPaPMvG2zHwBD0NPXo+T94VpnkCb4JBAAAAACgSJKJAW19drPtGKPi3xS1HWHIMkqq09llO0bBnVs1oPdP7CvcDWYDTVyYknuKm3RSUu2LKUV3ZQt3v6cwsyql+6d0yDdjU9QuNW3uViVN9+AblijjGkXvjUsV3j1d/pUlCoLRNUZ7mnv0wmeep3habBEp+mBczrQKf9KOUFr9avbWKekk8leUwZUhgGOd9NTDs88+WwsWLFAmk1EsdviyLtu2bVM2e+oPX+9///sLmxAAAAAAAGAIglygxV9caDsGhuHg6gNa9IWXdcvXPyQzmmvXAgAAAAAwRJt+2aDMQOmewOue78k9ozSmTAcK1OE2KjDZoxeWfPmqxsvorsn5QweDQBrxoY0g0ITlaUVaB29Umpw0cXFaPX2B+i4q/vPgnFhavzmlUz9vqVEyqOypw8cKTE4tboNOy1wqTzHbcYrCmebKvyGq9KLk4BuXqb3L92jPkl0658Z3jujnc5mcXviT59TfWsCTLHA8T4o+UC33zNLYP4bNgOlUq7tZuRNd6qDE99nAiZz0neLBBx/U9773PT355JNvL1uyZImWLFlyyhukNA0AAAAAAGzY/KsGNa8tzOUSMXYantqgKRdP1WW/d4XtKAAAAACAMhcEgeofW2s7xsgZyb+pdMqZ/aZN/U57/sISL195JtB9dQnFnCMF51EVpiXF67Oq2jn0ac5G0vhVGTm9gXqu8kZ350NwdiytB6Z26GcttRrIUZw+Ws6k1eI1aFrmEjknr2CVNO+aqLLbMsrtG5sJ52G07KtLNP36d8g4w3+trfzWcu1/fV8RUuFtrhS9Py53enm+BoutxzSp3W2UDKPQUTlO+m4xffp0felLX1JnZ6e6urr05S9/Wffee69mzZo1lvkAAAAAAAAGlepJadlXTn2iN8Jryd8v0qRzJ2v6defYjgIAAAAAKGMH3tin9u3tg28YUu7Fvpwp7tv/HwRBaK/clFPmcAmrrAT6UG2XpkbyJ3GO5k8Q3Z3V+DUjm3xe3ZCV2xcocZ0vucV9HpwezeihqR168lCt+ihO50mbPrW6WzQle5FMqZ8VcALGMYrMrtLAf/RIadtp7GhtaNGWpzfpgvsuGtbP7Vq0Q6u++3qRUkGS5EiR++JyZ/q2k5ScQIE6nZ3qdg/YjgKMuZN+klm7dq0SiYRqa2t1zjnn6JprrtGFF16oc84555T/AQAAAAAAjLVV312pvpZe2zEwQkE20LxPz1Xn7k7bUQAAAAAAZWz9Y+tsRxg5R/JvjOYtCmthWpI6nV3KmaMalmUwwPLS6n69e9xAwW7Pa8tp4rL8FupwH6bYrpxqX0rJJIv/AE+NZPTQtHaNcyt34vDJDDgd6nB22I5RNM4kV/4HS2fKfTGs+OYyZZJDP8Gh+0CXFjw6v4iJICNFZlfJO5/C9HDllFGL20BhGhXrpKXpf/3Xf9WWLVve/v+tW7eqq6trTEIBAAAAAAAMVefuTq35z7dsx8AoJRMDeu4Tv1KqJ2U7CgAAAACgDPW19mr7/K22Y4yYe6kvpzZ/ynRYDZiEetym/IXh7XcPyWmRtG6d1F2w23P6AtUsTMkc1cEMNLKHKdIcaNILKTm9xX9O1PlZzZnaoQkUp4/T4x5Ut1O+BUT/qqicGZ7tGNZ07+vS+p+sHdK22XRW8//4OQ109Bc3VIWL3Fklb1bEdoySk9GAmr31GnA68leE92MFUHAnLU1XVVWpt/fIhKa2tjYlk8kxCQUAAAAAADBUS7/0qrIpvqgpB21b27Tg0XkKchyhBQAAAAAU1sb/3qBcOmc7xsi4kn99/pTXsE6ZDpRTu7vddoyCijk5faSuU95RD/moOuuZQBMXpeT25S8ezV/U6ww06fmkvPbiP8dr/azmTGtXrTf0qbuVosPZoX7TMfiGJSpyd5VUwQOnV/3LSiW7Bu/Ovfb1ZTr4VvkW6MPAvy0m73IK08OVNN1q8tYpbY7ZAY30rB2gRJ30FKAZM2bo+eefV1tbm2Kxw3u81atX69ChQye9MWOM7r777sKnBAAAAAAAOIHdS3dpx4vl9UVcpdvx4nat/NZyXfPn19mOAgAAAAAoE0Eu0IbH19mOMWLelRE5E47MxAuCILSl6YSzVxlTTtNVA90zOaGJXn4ZecQPfxBowoq0Iq2FP2Hc7ZdqX0ip82Zf6dPdwX9gFCZ6Oc2Z1qEnmmvVlqnc6cPHMVKru1nTMpcoomrbaQrOmeAocnuVUk+X02t86AY6B/Tmv7+ha//y+pNus+PlRr31vVVjmKry+DdF5V8dzVsW5v1iWPSaFrW5WyVzgv0PDx0qzEk/ucyZM0c//vGPtXDhQmWzh6c1rVmzRmvWrDnlDVKaBgAAAAAAYyGXyWnJFxfZjoEieONfVqruwik6967zbUcBAAAAAJSB3Ut2qWtfl+0YI+NL/rX55bCwFsNS6lWXs892jIK6dkKvZlalCnZ71fVZVe0s3jRoJy3VvpxW17XSwIziFqfHuTnNmdauJw/V6lDaL+p9lZLAZNXiNei0zKVyVX6TcL1ZEWW3pJXdVJmTxtf+4C1d+tuXadxp449b17UvoRc/O99CqsrhXRuVf13+uHMK06cWKFCXs1cJd4/tKEBonLQ0XVdXp89+9rMKgkB9fX169NFH9fDDD+uKK64Yy3wAAAAAAAAnVP/YWrVva7MdA0Xy4ufmq+YdtZpy8VTbUQAAAAAAJa7+sbW2I4yYd3VUpjr8U6YDBWp3t+dPsAxU0tMr3xlL6rqJvQW7vejurMatKX7R1OSkiUvTcnoD9c1yRzEWe3BxN9BD0zr0s0O1OpiiOP1rWZNUi7tJU7Oz5Ki45XUbIndUqX9vj9RT+InpYZcZyGjlt1folq99KG95NpXVvD+aq2RiwFKy8ue9J6LIzRSmhyOnrNrd7epzWmxHAULFGWwDY4yqq6t19913a+bMmRo3btwp/wMAAAAAACi2/o5+vfatFbZjoIgy/RnN/dTT6mvrsx0FAAAAAFDCug90aecrO2zHGJmY5F9TGlOme5wmpZzu/IXhjDokE9ysZk9O5PWNg1H0Q722nCYuS+ctK3bddPzqjMa/kZFyxb2nmBPogakdOjtauInc5SDldKvd3aag6H/psWfijqJ3VdmOYU3Df29Q29bWvGXLvrpEzWubLCUqf+5lviIfyn/OUZg+taxSOuRuOL4wXX5vScCwDVqa/rXZs2frzDPPLGYWAAAAAACAIVn5zeVM7agA3fu6NO8Pn1U2lbUdBQAAAABQojY8sV5BkUujxeK/LyoTO1IIC0bT2i2ijJLqdHbZjlEwrgJ9pK5TVe6RxzsIRj6w2ekLVLMwJXPUkOmxGsId35zVxMVpKVPc507UCfSbUzp0TjRZ1PspNX1OqxLOHtsxisI915d7WWVOFw9ygVZ8fdnb/799/lat/cFbFhOVN/diX5G7KEwPR0q9avLWHX8yU4lfAQIoFG84G6fTaa1evVq7d+9Wf3//CT+Q/+7v/m6hsgEAAAAAAByndXOL6h9fZzsGxsj+1/dp8RcX6gNfvtV2FAAAAABAicmms9r4ZL3tGCMTN/KuDv+U6UCBOtxGBSZ79MKSLmXdUtut06OZvGUjfugzgWoWpeQecyGtsXx4Yntycl5MqfMDEQWx4t2z70j3T+3Ur1pq1DgQHfwHKkSXu1d+UKXqYKrtKAUXubVKA7syCjrDeUJHMe14cbsOrNqvvr5erfjLpbbjlC33PE+Re6vy9n8Upk+t37Sr1d2Sv1/+NR42QNIwStPt7e36xje+odbWVlVVVam/v1/V1dXq6+tTEAQaN26colE+9AAAAAAAgOIJgkCL/3ZhyU6IwsjUP7ZOdRdO0SUPX2Y7CgAAAACghOx8uVG9h3ptxxgR/9qoTCT8JbF+06Z+pz1/YfhiDtms6n5dPr6/MDcWBJq4PC2/1f5xrEhLoEnzU+q4xVdu/JAvSj9snpHum9KpZ1snakt/rGj3U2ra3G1ys1HFgom2oxSUiRpFZseV/Env4ZMlKszSL7+qvt4+pbqYsF4MzgxPkfviMk7494VhEChQt3NAnc7Okt4PA2NhyJ+Efv7zn6u3t1ef//zn9aUvfUmS9KlPfUrf+c539OEPf1iRSESf/exnixYUAAAAAACg8YVt2vfaXtsxYMHi/7NQ+1bytwcAAAAADN36x0rzSlVmvJF3ZSR/WQhLYjll1O422o5RMFP9tD5U21Ww26ten1VsV65gtzdaXlegSfNS8tqKm8k10r11CV0cL1D5vByYQK3uJqVVfo+JO92Td3Vk8A3LUNOag+ramrAdoyw5011F74/LeBSmhyJQTu3udnW6FKaBoRhyaXrTpk268cYbNXPmTDnOkR/zfV933nmnzj33XP3sZz8rSkgAAAAAAIDMQEZLv/Sq7RiwJJfJad4fPquuvXwRAQAAAAAYXMfODu1dttt2jBHxro/mFcXCqtPZpZxJH1lQwpNmoyanj9Ql5B/VoglG8ftEd2c1bm1m9MEKzB2Qal9IKbI/W9T7cYx09+QuXVrdV9T7KSU5k1GL16Ccwve8GC3/5phMXfEmmKOyOGe4in6sWsbP3w9SmD6xrNI65G5Ur9Ocv6KE98lAsQ15j5VMJjVlyhRJkud5kqSBgYG315977rnaunVrgeMBAAAAAAActvo/31TXvsJN+0Hp6W/v19xPPa10X8p2FAAAAABAyG14vESnTNc68i4N/9TWAZNQj9uUv7Bk+2yB7prcpVo/v0g80n6e15bTxKXpvGVh6q45GanmlbRi24tb3jVGumNyt64aT3H61zKmXy3uJgUKzwTyQjCeUfTe+DBaaMCJmamOog9Wy0RLdocyptLqV7O3TknnmEEjgUp4nwwU35B3VzU1NUokDr/AotGoqqurtXfvkUuitrW1yXXdwicEAAAAAAAVr6epW29+93XbMRACrQ0teunPX1AwmnFPAAAAAICylhnIqOGpDbZjjIh/Q1TGDXfTKVBO7e522zEK5r3j+3RePFmQ23L6AtUsTMkc1b8OY3fNBNLE5RlVr8uMbqT2ENxS2633Tegt6n2UkqSTULvbqCBUVfrRc0535V8ftR0DJcxMdhSbUy1TFbZ3zHAaMJ1q9tYpYwaOX8lDCJySN9QNzzvvPG3cuFF33323JOmKK67Qiy++KMdxFASBXnnlFV1yySVFCwoAAAAAACrX8q8uVbovPfiGqAjbnt+qugtW6uo/vcZ2FAAAAABACG2ft1UDnScoEYWcqXPkzvJtxxhUwtmrjOm3HaMgpkdTurGmpzA3ljlcmHaPGawc5u7auLUZOb2But/nSU7xkt5U0yPfBFqaqFa4H5Gx0es0yw+qNCF3lu0oBeVdG1V2W0a5A9nBNwaOYmqMonOqZaoZVz4UPaZJ7W7j4TNgAAzbkEvTt9xyixoaGpROp+X7vj760Y+qpaVFzz77rKTDpeoHHnigaEEBAAAAAEBlOvjWAW3+VYPtGAiZ1765XJMvmKKZt73LdhQAAAAAQMisf2yt7Qgj4t8UkzHhLpSm1KsuZ5/tGAUxzs3q3rpEXlc4CKQR/QmCQBOXp+W3lV6BLb4tK7c/UOcNvuQX7/l37cReeSbQos5xojgtdTq75AVVigeTbUcpGOMYRWZXaeA/e6SM7TQoFWa8UXTOODkTKEwPJlCgTmeXut39tqMAJW3IpemzzjpLZ5115AyneDyuz372s+rr65PjOIrFYkUJCAAAAAAAKleQC7T4iwttx0BILfiz5/Wbv3pIdedPsR0FAAAAABASLZtadPCtA7ZjDJtzuivvgnBPmQ4UqN3dnj/ZMlBJ9l8dBfpwXULVbu7tZSMuTEuqXp9VbFdu8A1DKrovp0kLUur4YERBVfH+oO+d0CfPBHqpY7xK8olTSEZqc7fIy1yiiMbZTlMwTp0r/wMxpV8svWn/sKD68IRpp5bC9GByyqjV3aIBp8N2FKDkDekdJ5lM6hvf+IaWL19+3Lp4PE5hGgAAAAAAFMWmX25U87om2zEQUunetJ77xNPq7yiPSwIDAAAAAEZvw+PrbEcYEf+mqO0Ig+pxmpRyuvMXlmjv9eaaHp0VTectG2lhOrorq3FrS3+srt8WaNL8lNyu4pa/rxzfrzsndcmo9KZyF1pgcmrxGpRR0naUgvKvjsp5h2s7BsIuZhR7qFpOHc+VwWQ0oGZv/fGFad5GgREZUmk6Go1qz549ymazxc4DAAAAAAAgSUr1pLT8q0ttx0DIJfYkNP+P5iqXKd1pTgAAAACAwkj1prT5Vw22Ywybc7Yrd2a4p0xnlFSns8t2jIK4ID6g90zoK8hteW05TVyWX74u5Q6b1324OO21FPc4yyXjBnTP5ISckn60CiNrUmrxGpRTeXWyIvfEpfCfCwJbIlL0wbicaRSmB5M03Wry1iltjtlvleiVHoAwGPJs+/POO0/btm0rZhYAAAAAAIC3vfGdlepr6bUdAyVg7/I9WvqlV23HAAAAAABYtuWZTUr1pGzHGDb/5nBf3TtQoA63UYHJHr2wJE32MrpzUlfesmCEv4vTF6jmlZSOfVhKvcPmDEiTFqQU2VvcEu9F1Ul9uC4ht1SfTAWUNr1qc7coKKPHwpnoKHJble0YCCNPij5QLfdMz3aS0Os1LWp21ytn0sevLPWdDWDRkEvTDz74oHbu3KmnnnpKLS0tyuWY3gMAAAAAAIqjc1eH1v7gLdsxUELW/tdqbfxZve0YAAAAAABLgiBQ/U/W2Y4xbM4MT+70cBfH+k2b+p32/IUlWNaKmJw+MqVTEedIMTUIJDOS3yUTqGZhSm5//uISfFhOyGSlmkVpVW3NFPV+zosndd+UTnmmfMrCI9XvtJfNNPdf8y6NyD0v3O9vGGOuFL0/Hvr9nm2BAiWcPWrztki8PwIFd8p3oBUrVui8885TXV2d/uZv/kZBEOill17SSy+9JMdx5LrHj8j/7ne/W7SwAAAAAACgMiz98mJlU+V1SUoU36L//bImvWuyTr/yDNtRAAAAAABjrHltk1oaDtmOMWz+TVHbEU4pp4za3UbbMQog0B2TulTn5x9vGlFhOgg0cXlaflt5F9lMIE14LSOnN1DvZd4IH6zBzaxK6f4pHfp5S43SwZBnP5albne//KBK44LTbEcpmMhdVerf1yP1lffrBUPgSJH74nJn+raThFpOWbW729XntNiOApStU5amf/SjH+njH/+46urqdNVVV8kU6QMQAAAAAADAr+1euks7XtxuOwZKUDaV1XO//7QemPtbGn/6eNtxAAAAAABjaP3ja21HGDb3fE/uGeGettnp7FLOpI8sCFSS45SvGt+vC6uTBbmt6nVZxXZVztXZx63Pyu0N1PV+X3KK88c/J5bWx6Z26qlDNUpWeHG63W2Ul40pFtTYjlIQptpR5K4qpZ7qsx0FNhkpMrtK3vkUpk8lq5Ra3E1KOd35K0p03wuE1ZA/fT/yyCPFzAEAAAAAAKBsOqslX1xkOwZKWF9Ln5775NO6/+cPyItxEB4AAAAAKsFAZ7+2PrvFdozhMZJ/Y8x2ilMaMAn1uE35C0uwtHVmJKWba7oH33AIoruyGrcuU5DbKiVVjTk5/WklbvIV+MV5EpwVTeuBqR36WUutBnIVXJw2gVrcTTotc6l8xW2nKQjvfF/ZS3xl16cH3xhlKXJnlbxZEdsxQi2lXrV4DcqaY07woTANFFwFf8oAAAAAAABhU//YOrVva7MdAyXuUH2zXv7LFxUEXPYTAAAAACrBpl80KJssrSKre7EvZ6r79v+H7d+wgXJqd0v/SmBxJ6sP1yXkHlU4G+lD7bXmNHFZfukzXH+14ooeyKn2hZScvuL91qdHM3poaofiTrZo91EKApM9XJ5U+ZSMI7dVyUyg+VmJ/Nti8i6nMH0q/aZdzd764wvTEoVpoAgoTQMAAAAAgFDob+/Tym8ttx0DZWLLM5v01vdW2Y4BAAAAACiyIAhU//g62zGGx5H8G6N5i4wJVysq4exVxvTbjjEqRoHurUtovJfLXz6Ch9rpDVSzMCVzVJe3Eod/+u2BJs1Lyk3kBt94hKZGMpozrUPj3couTmfMgFrdTQpUvMd6LJmYUeSe8picjaHzb4rKvzp/fxe2k4RsChSoy9mvFrdBgans9zxgLHmDbfCjH/1IP/7xj4d8g9/97ndHFQgAAAAAAFSm1765XMmuE0xSAEZo+VeXaPJ5dXrnB2bYjgIAAAAAKJJ9r+1VR2O77RjD4l7qy6nNnzIdptJ0Sr3qcvbZjjFqN0zs0TmxAkzqzQSqWZSSe0yHPDx/sbHl9kqT5qfU+YGI0lOLM6txsp/VnGkdeqK5VomsO/gPlKmk06U2bdPk7HkyZfCMc9/pyXtPRJlVKdtRMAa8a6Pyr4vlLQvb/s6mQDl1ODvU4zbZjgJUnEFL0zNmzFBdXd1YZAEAAAAAABWqZVOLNvx0ve0YKDeB9MKfPqcHnnlYtTMn2U4DAAAAACiCkpsy7Ur+9fklsjAVyAIFane3SyY4emHJNYTPrRrQNRP7Rn9DQaCJy9Ly25iMejQnKdW+mFLiel/Jc4pTaq7xspozrV1PHKpVR2bQelPZ6nNa5AdxTcydbTtKQfgfiCm7I6OgrTwmaOPEvPdEFLmZwvTJ5JRRi7tJSSeRv6IE97dhZBTotEhGB1O+7SgIqUE/Vdxwww1673vfOxZZAAAAAABABQqCQEu+uFBBji+fUHip7pSe/cSv9MDTcxSdGBv8BwAAAAAAJaP3UK8aX9hmO8aweFdG5Ew4Mp03bCWyHuegUk53/sLwxBuSGi+juyZ35S0LAmkkD3P1uoxiuyl3nojJShNfTav76kD9Fxan1DzBy2nOtA49eahWrenKLU4n3N3ygpiqgym2o4ya8Y0is6uU/FHv4YIoyo57ma/Ih6ryloVtX2dTWv1q8RqUMcdcvoDCdEFMdDO6p65Lp0XS+n9NDFLBiRXnOhkAAAAAAABDtH3+Nu17ba/tGChjnTs6NP9PnlMuy5ecAAAAAFBONv53vXKZEvq3ni/510bzFoWpRJZRUp3ObtsxRsUzge6rSyjmHGljjrQwHd2Z1bh12QKmKz9G0oQ3Mhr3VvrwA10E49ycHprarml+uii3Xyra3W1Kmu7BNywB7pmevGPeC1Ee3It9Re6iMH0yA6ZTzd664wvTEoXpUQt0SXW/Hjm9XWdF0/KMNHtyl/x0m+1gCCFK0wAAAAAAwJrMQFrLvvyq7RioALsX79KKry21HQMAAAAAUCC5bE4bfrrOdoxh8a6OylTnT5kOi0CBOtxGBSZ79MISE+j2SV2aGsnkLR1JV89rzWni8vySbsk9HGOoekNWE5ampWxxHqW4G+jBaR06I5Iqyu2XgsDk1OI2KKMB21EKwr8+KnMatbVy4p7nKXJvVV5BmsL0ET2mSYfcjcqZzOAbY1jiTk731SV05+QuRY86aWpKJKNZwUaLyRBW7H0AAAAAAIA1q//jLXXt6xp8wzJlTnMU/VhckY9UydRymKbY3vreKm3+ZYPtGAAAAACAAtj96k517y+hqatRyX9feKdM95s29Tvt+QvDE29ILhvXr1nVoy+UOr2BahamdGx/vMQejjFXtTOn2pfTMqniFKdjTqCPTe3U2dHKLU7nTFotXoNyKv3SpXGNovfGJdd2EhSCM8NT5L64jENh+liBAnU4O9XubZcMp98U2ruqkvr46W06L5484Xp34kVjnAil4JTfxn3/+9/Xe9/73rHKAgAAAAAAKkhPU7dWfXel7RjWmFpHsd8aJ/dcX97FEcV+t1pmKsXpYnv5rxaoad1B2zEAAAAAAKNU/3hpTZn2r4nKVOWXycIip4za3UbbMUbltEhat9QWoESfCVSzKCW3P38xtb+hiTTlVPtCSk5vcZ7fUSfQb07p0DtiJy7HVYK06VOru0VBGcw+d6a48m+K2Y6BUXKmu4reH5fxKEwfK6esWt1N6nb3245SdnyT0+2TuvQbUzpV7eby1h39Ect48TFOhlLAN3EAAAAAAMCKZV9Zokx/6U9FGRFXit4Xl4keOXBsqg+XqJ0zGK9STNlkVs998hn1NvfYjgIAAAAAGKGufQntXLjDdoyhixt5V4d3ynSns0s5kz6yoMS6mFVOTh+p69RRfT2NqJMeBJq4LC2/rcQegJDxOwJNmpeU25EbfOOR3L4j/caUTr2rqnKL0wNOhzqcEnoPPAXvfRE50zkeWqqcM1xFP1Yt4+fv08K0j7MlowE1e+uPv4oDu5hROyOS0iOnteuycf0nXM/TD4OhNA0AAAAAAMbcgTf3a8vTm2zHsMb/YEzO6cd/GWCqjKJzqvmioMh6m3v03O8/o8xAhZb2AQAAAKDEbXiivqRKR/61UZlIOKdMD5iEetym/IUlVDYyCnTP5IQmevkF3ZEUpqrXZRTbXZyib6Vx+6RJ81Pym7JFuX3PSB+p69QF8YGi3H4p6HEPqts5YDvGqBljFJkdlyK2k2C4zFRH0Qer8waD4LCk6VaTt05p05u/IlBJ7WPDxlGg6yf26OFpHar18/cvIfpohRJAaRoAAAAAAIypIBdo8RcX2o5hjXu+J/+Y6VJHM1Gj6IPVcmZ4Y5iq8jStOaiF//ulUH1RDQAAAAAYXDaV1cafrbcdY8jMeCPvyvw2YFgmcAbKqd3dbjvGqFw7sVczqlKjvp3ozqzGrStOwbdSOWmp9qW0ojuL87i6Rpo9OaFZ1SeeNFoJOpwd6jftg28Yck6No8itVbZjYBjMZEexOdUyVeHYn4VJr2nRIbc+/woOv8bDNWKTvIx+a1q7rp3YK+cEj2NIPlqhRFCaBgAAAAAAY2rTLzbq0Ppm2zGsMBONInfHB9/ON4r+ZlzuuRSni2nTUxu19r9W244BAAAAABiGHS9tV19Ln+0YQ+ZdH5XxwtnkSTh7lTGlWzidEUvq2gm9g284CK8lp4nL88ttnGJdGCYn1SxJK74xU5QxoI6R7p7cpcvGlc57QkEZqdXdopRG/zqwzbs8IuddHAstBabm8NUSTTW1w6MFCpRw9qjN26LAcNWCwgl0xbg+/d5pbTo9ypUjURi8ewEAAAAAgDGT7E5q+deW2I5hhyNF7ovnTd841ZRj4xlF7o/Lvcgfi3QVa+mXXtWeZbttxwAAAAAADNH6x9bZjjBkptaRd2lk8A0tSKlXXc4+2zFGbKKb1T2TE3mTJUfSyXV6A9UsSskcNQw5EMNAC238mxmNW1Wc4rQk3T6pW1eNL/3i8EgEJqsWr0FZjX7ium3Ru6skJheHmhlvFJ0zTs4EKodHC5RTm7tVCXeP7ShlZZyb1W9O6dRtk7rlH/OU4wKSGA3ewQAAAAAAwJhZ9Z2VJTUNqpD8m2Nyz8yfljLY5XiNYxT5cJXcSyhOF0uQDTTv03PVuavDdhQAAAAAwCA6Gtu1b0XpFJL8G6IybvgKgIECtbvbJRMcvbBkuAr04bpOVblHQgeBNMhhluOlA9UsTMk9Zth2+P5i5aF6U1YTF6elbHGebLfU9uiaAkweL0VZk1SLu0k5ZQffOMTMOEeRO6psx8DJVB+eMO3UUjc8WlYpNbv16nNa8leU0H41jM6vGtDHT2vTjKoTnxAy7H0+cBTexQAAAAAAwJjo2NmhNT94y3YMK5x3efKviY7oZ41jFJ0dl3dlOCdTlYNkYkBzP/G0kt1J21EAAAAAAKdQ/3gJTZmuc+TOCudJ0D3OQaWc7vyFJVQ+uqW2W6dHM3nLhl2eCgJNXJ6W306rbSzFdudU+2JKJlmcx/3Gmh5dP7FHldhWTDndane3KSjx3927yJd7cTjfOytazCj2ULWcOtd2klBJqVdN3rrj96lcsmDEoianuycn9JEpibyToySmS6NwKE0DAAAAAIAxsezLryqXztmOMebMBKPo7PwJKcEIju5F7qiS9z6K08XSvq1NC/5snoIcR14BAAAAIIwyA2k1/Hyj7RhD5t8YG/QKUzZklFSns9t2jBF7d3W/Lh/fP/iGg6hem1Fsd+UdpwqDyKFAk+an5PQU5xjMtRN79YGayixO9zmtSjilM43/ZCK3V8mMD9/7Z8WKSNEH43KmUZg+Wr9pV7O3XllzgkEcPH1HZHo0pUdOb9Os6oETrg/hxyqUKErTAAAAAACg6HYv2aUdLzXajjH2HCnykbhM/MghmCAIRvylaeSWKvk3jGxiNQa38+VGvfbN5bZjAAAAAABOYOtzW5RMnLhEEzbmNEfeheGblBooUIfbqMBkj15YMqb6ad1W2zXq24ntyGrc+uzgG6JovESgSfOS8tqLU1y/ekKfbqvtVkk9wQuky92rHtNsO8aomCqjyN1Vg2+I4vOk6APVcs/0bCcJjUCBupz9anEb8venGDFXgW6u6daDUzs00cvfLzBdGsVAaRoAAAAAABRVNp3Vki8utB3DCv/GqNyz8w8oj3bKlH9DTP4HY6O6DZzcqu+s1NbnNtuOAQAAAAA4Rv1j62xHGLLITeH8d3u/aVO/056/sESmNkZNTh+pS8g/quUykiKV15LThOXpvGX0sexw+6XaF1KKHChO6fCK8f26a1KXTAX+hdvd7RowCdsxRsWd6cu7gqvuWeVK0fvjcqdTmP61QDl1OI3qdHeWzP4z7Kb4af3Oae1674S+E06SZro0ioHSNAAAAAAAKKr6x9apfXv74BuWGWeGJ//a4nxJ6l8TlX97OL+ALQcvfe4FtWw8ZDsGAAAAAOB/tGw8pKY1B23HGBLnbFfuu8I3ZTqnjNrdUr0KWKC7JydU6+eXa4dbpHJ6A9UsSskcNcQyEL03m5y0VPNyWrHG4hSn3z1uQPdMTsiptOK0CdTqblJa/baTjIp/S0ymlmqbFY4UuS8ud2b49me25JTRIXejetym/BUV9vZSKEaBrh7fq985rV1TI5m8dUyXRrGxZwEAAAAAAEXT396nld9abjvGmDPjjKL35l9CMijwkT7/qqgi91TxzV4RZAYymvvJX6mvtdd2FAAAAACApPWPl86Uaf/mcJ7k3OnsUs4cNWG5hApJ75vQp3PjqdHdSDpQzcKU3GM6pBxWsc8E0sRlacXrM0Vpyl1UndRH6hJyS+lJXwA5k1GL16CcMoNvHFImYhSZzfHPMWekyOwqeedTmP61tPrV5K1T0jlmgjtn3ozIRDerB6d26AO1PfKYLg0LKE0DAAAAAICiee2by5XsStqOMbaMFPlIXKb6yGGXIAhkinCkz7s0osiHqzjCUwTd+7v1/B/OVTZVnElHAAAAAIChSXYnteVXDbZjDIkzw5M73bMd4zgDJnH8ZMwSKSSdE03qhok9o7uRINDEZWn57ZVVmi0141dnNP71jJQr/N/p3HhSH53SKc9U1nMgY/rV4m5SoNzgG4eUe7Yn75qo7RgVJXJnlbxZEdsxQmPAdKrZW6eMOeasGwrTIxBoVnW/Hjm9TdNj6cE3B4qEr9QAAAAAAEBRtDQc0oafrrcdY8z5N0TlnpP/BWkxCtO/5l0cUeQ34pJbtLuoWAfe2KdX/3ah7RgAAAAAUNG2PL1J6b7SKNb4N4Wv2Bcop3Z3u+0YIzLezWp2XULOUYdVRjKIuHptRrE9pVsarSTxLVlNfDUtZQpfbp5RldJvTulQxFTWcyHpJNTuNioo4Unb/o1RmalU3MaCf1tM3uX5helCX0GxlPSYJh1yNypnTjCxncL0sFQ5OX24LqG7J3cp6uQ/pwr+FMsEiuxlGApOjj0KAAAAAAAouCAItOSLixQUYTJMmDnvdOVdN/ZfkHrn+Yp+LC6Fb5hVydvw+Dqt/8la2zEAAAAAoCIFQaD1j621HWNI3PM9uWeE7x/mCWfv8dMxS4CjQPfWJVTtHjm2FATScM9Lj+3Iatx6ilOlJLY3p9oXUzIDhT+uOD2W1semdipaYcXpXqdZ3c5+2zFGzLhG0XsZGlFs/k1R+VfnH9su1hUUwy5QoA5np9q97VKFTagvhnfGkvr46W26IH7iq5IW8inmH8pp8tyUaham5R+qrPd6DB2laQAAAAAAUHDb52/TvpV7bccYW9WHD94ffRB5LKdwuDN8RR+qlrhyYsEt/tuFlfd8BgAAAIAQOPjWAbVtbrUdY3BG8m+M2U5xnJR61eXssx1jRD5Q262zovkTxodbqvJacpqwPP82qL6VhkhLoEnzU3K7C194OzOa1oPTOlTlVFaZrtPZpT7TZjvGiDnTXPk3hG+af7nwro3Kvy5/P1aphemcsmp1N6nbLd0TDcLCM4Fure3Sx6Z2apyb/55b6K9OTDrQ+DfSqp2fktcVyEiasCIjp7l03/dQPJSmAQAAAABAQWUG0lr65VdtxxhbRop+OC4z7sihFhsHld3pnqJzqqVY5R3MLqZcJqd5f/isuvYmbEcBAAAAgIpS//g62xGGxL3YlzP1yAjUsTyJ+mQCBWp3j5mQaT/WkFwY79dV40c3HdvpDVSzMKWjBwoHkjhiUjq8rkC181LyWgtfbj4tktFDU9tV7VTQFHIjtblblFKP7SQj5l0TlXMW46YLzXtPRJGbKUxLUkZJNXvr1e+0568okf1nmJweSev3TmvTlSfZnxfy6RU5mNXkZ1OKb8rm7ee9RE7jlmwv3B2hbFCaBgAAAAAABbX6+2+qe1+X7Rhjyrs2Kved+ZfgtXVQ2T3TU+y3qqV45R3ULqb+9n7N/eTTSvelbEcBAAAAgIrQ39Gvbc9vsR1jcI6Om34ahqJZj3NQKac7f6H9WIOq8zO6Y1L+caVhd9DThwvT7kD+4hL49XEMd0CqXZBSZF/hy81TIlnNmdah8W7lFKcDk1OL16CMkrajjIhxjCKzqyTfdpLy4V7mK/KhqrxllVqYTppuNXlrlTa9+Ss442ZYjAJdO6FHvzWtXZP9/PfXgk+XTgUavyKt2hfTcntOfOPZc2cV9k5RFihNAwAAAACAguk+2K1V//q67RhjypkevktDOtNcxX67WmY8R3MLqXVTi1783AuhmBgGAAAAAOVu01MblE2Gv8zoXurLmRSuKdMZJdXp7LYdY9giJqeP1HUqclSTJQiGOY0yCDRxWVp+u/2/AwrDyUg1C9OKbcsU/LYn+VnNmdauiRVUnM6alFq8BuVUmr+zM8mV/8HY4BtiUO7FviJ3UZiWpF7TokNuvXImffzKyns4RqzWy+i3prXr+ppeOSd43Ao6XXpvVpOfSSq+bZD3ski4vrtBOFCaBgAAAAAABbP8K0uU6S/8FxihFTeKfDguc9QRwDB8OSpJTp2r6G9Xy0zkqG4hbZ+3VW/880rbMQAAAACgrAW5QPU/XW87xuBcyb8+v7xnu2wWKFCH26jAZI9eWAIC3TGp67iplMN9OKvXZBTbkytgLoSBCaSJKzKqXpsu+KjSGi+nOdPaVetVzjHNtOlVm7tFQWm8ORzHvyoqZ4Y3+IY4Kfc8T5F7q/L2WZVYmA4UKOHsUZu3RYFh3zFygS4b16ffO61NZ0SL+15qBgJNWJpS7cK03L5jUwBDQ2kaAAAAAAAUxIFV+7XlmU22Y4yp6L1VcibkH14J04Flp9ZV9HfGyUziEFAhrfzWcjUu2GY7BgAAAACUrb0r9qhzZ4ftGIPyrozkHRcIw4nU/aZN/U57/sLwHKo4qfeM79OF1clR3UZsR1bj6ktzei6GZty6rCa8lpFyhX2tTfBymjOtQ3V+5RSn+512dTq7bMcYscjdVRIDp0fEmeEpct/xg0DCdFx7LATKqc3dqoS7x3aUklbtZPUbUzp1+6TuvCtFSAU/x0XRXYenS1ftOHHBvbKewRgNvjEDAAAAAACjFuQCLf7iQtsxxpR3TUTuTN92jEE5ExzFfrtaZgqHgQppwaPz1LqlxXYMAAAAAChL9Y+ttR1hcL7kX5t/yXfbhbOcMmp3G61mGImzoindXNMzqtvwW3KasDydt8x+hR3FULUtq5qFaZl0Yf/C49yc5kxt1zQ/PfjGZaLb3a8e02Q7xog4ExxFbq+yHaPkONNdRe+Py3iVXZjOKqVmt159zjHHd9lxDMt5VQP6+OlteldV6oTrC/W0cvoDTVyUUs3itNyB/HX8yTASfFsGAAAAAABGreHnG3Sovtl2jDHjnOXKv7l0RpmYcY5iv1UtcxqHggol3ZvW3I8/rf6OfttRAAAAAKCs9DT3qPHF7bZjDMq7OipTHa4p053OLuXMUYVP+5EGVe1kde/khI4aeDrsyZROT6CJC1MyRw2eDMTEyXIW3Z9T7YKUnP7CPsmr3EAPTuvQGZETFwDLUbvbqAHTaTvGiHizInIvDP9Qi7BwznAV/Vi1jJ//7lhphemUetXkrVPK6c5fwY5jyCImpzsnJXTflITibv77cEE/DgWBYo1ZTX46qdgepkujcPimDAAAAAAAjEqyO6kV/7DUdoyxU2VOePnCsDNxR7GHx8k5y7UdpWx07U1o3qfnKpvm0r8AAAAAUCgbn6xXkA35v7Ojkv++cE2ZHjAJ9bjHTIwNeZPIKNC9dQmN9/KLUMN5KE06UM3C1HGTJ0P+q6MA/LZAtfNSchMnLtKNVMwJ9MDUTk2PVkhx2gRqcTcprT7bSUYkckdMGscrfjBmqqPog9Uy0cp+rPpNu5q99cqa5PErK/uhGbKzoik9cnqbLhk3cML1BZsu3ROo5pW0Ji5Lyznm7TjknxJRAihNAwAAAACAUVn1LyvV11KaB9VHInpPlZwJ+YdUbH8xOlQmZhR9qFrOOyhOF8q+FXu09Euv2o4BAAAAAGUhl8lpwxPrbccYlH9NVKYqPCdTB8qp3Q3/dO5j3VjTo+mx9OAbnkwQaMLStPwO6lOVyusJNGl+Sv6hwhanI06g+6d06J2xExQry1BgsmrxGpTVKF6Plpi4o+hdVbZjhJqZ7Cg2pzpvv1VpAgXqcvarxW1QYBiAMRKOAt04sVtzpnao5piTnQo9XbpqS0aTn00qup/p0igOStMAAAAAAGDEOnZ2aM0P37IdY8x4743IPa+0L/loIkbRB6rlvMuzHaVsrPvRGm14st52DAAAAAAoebsW7VDPwW7bMU4tbuRdHa4p0wlnrzKm32qG4TqvakDvmzC6k/Cr12QU21vYsixKj5OUal9MKbqnsEVI35E+OqVT51adeJpqucmYAbW6mxSo9F5T7rm+3MtK+5htsZgao+icapnqyq0IBsqpw2lUp7uTtu0I1fkZ/c5p7bpmYt8JJ0kX6mOQ25VT7YtpTViZkXPMORycHoVCqtx3RAAAAAAAMGpLv/SqcunSO5A+Es4ZrvwPxGzHKAjjGUXvj8u9gOJ0oSz63y/pwJv7bccAAAAAgJK2/rF1tiMMyn9/VCYSninTKfWqy9lnNcNw1XoZ3TW5K2/ZcB/GWGNW4+qZForDTFaa+GpaVZszBb1dz0gfqUvognhlFKeTTpfa3G0KSrCeGLm1SqaGRuzRzHij6Jxxx101sZLklNEhd6N63Kb8FaX3FLck0HvG9+p3T2vTtEhh31/z5ALFN2Y0+dmUIk1Ml0bxVe67IgAAAAAAGJXdi3dq58uNtmOMjZgUuS8u44bnS9HRMq5R5L643HczhaUQcumcnv+DZ9R9oGvwjQEAAAAAx0ns6dTuxTttxzglM97IuyqSv8zilOlAgdrd7ZIJjl4Yar4J9JG6hKLOkaBBMLwplf6hnCasyB9BGfJfG2PABNKE1zMatzo9/Bb+KThGmj05oXdXl9Y095Hqc1rU5ey1HWPYTNQoMjtOs/LX4ocnTDu1lVsNTKtfTd46JZ1E/opAPE+GYLyb1QNTO/XB2h55xzxehfxqxO3MqfaFlMa/mZE55lwo9u0olsp9ZwQAAAAAACOWTWe1+IuLbMcYM5G743Jq8g+j2L70biEYxygyu0ru5RSnC6GvpU/PfeoZZQbSg28MAAAAAMiz4Yn1oW/HeNdHZY5tDlnU4xxUyunOXxieeCcQ6EOTujT1mGmVwznE4vQEmrgoJXPUIEr6bzhadX1WE5alpWxhi9N3Te7S5eP6CnabYZZw96jXtNiOMWzudE/e1ZHBNyx3MaPYnGo5da7tJNYMmE41e+uUMcec7MAOY0guivfr46e36R2x1AnXF+SrkVyg+PqMJs9NKdJy4vdr/lQoFkrTAAAAAABg2Nb/ZK06GtttxxgT3lUReReUb6nYGKPoXXG+UCiQQ/XNeukvFpT8JHIAAAAAGEvZVFYbf1ZvO8YpmVpH3qXh+bdzRkl1OrttxxiWy8f1a1b1wIh/3qQD1SxMyT3mJihV4VhVO3KqeSUtkyrs8ZkPTerWe8b3FvQ2w6rN3aqkKb0rqvk3x2TqKrgOF5GiD8blTKvcwnSPadIhd6NyJnP8SnYYpxRzcpo9uVOz67oUc/LfPwt5uNtrz2nS8ymNX5PJOwlKCv35cygTFbyXAAAAAAAAI9HX1qeV315hO8aYMKc58m+J2Y4xJiK3Vcm7Nmo7RlnY+uxmvfXvb9iOAQAAAAAlY/sL29Tf1j/4hhb5N0Rl3HC0rQIF6nAbFRx9HfuQt4xOj6R1S2334BueTBBowtK0/I6Q/6IIjejBnGpfSMnpK+xz5oO1PXr/hJ6C3mYomUAt7iZlNPITHWwwnlH03nhlNuI8KfqxarlneraTWBEoUIezU+3edsmwrxiuc6JJPXJamy6qTp5wfUGmS2cDVa9Ja9JzKfntTJeGPZW4iwAAAAAAAKPw2jeXK9V14gNnZSUqRT8az7vsbrlPD47cHJN/M8XpQlj+taXa+Uqj7RgAAAAAUBLqH19rO8IpmTpH7qzwXIWq37Sp3znmCmAhbhlVOTl9uK5TR3fOh3uIZdyajGJ7c4NvCBzF7wg0aV5Sbmdhnzs31PTqhok9Cv3ZCqOUM2m1eA3K6QQTe0PMOd2Vf32FHeN0pej9cbnnVGZhOqesWt1N6nb3245ScjwT6JbaLj04rVMTvPz3yoJOl27JafLclMatzx7XaS/vd1KEEaVpAAAAAAAwZC0bD2nDT9fZjjEmIndWyanNv4yhKcg4hXDzr43Jv60ypmsXVSC98Jnn1b69zXYSAAAAAAi1tq2t2r9yn+0Yp+TfGAvNMYGcMmp3S+ckXaNA90xOaOIxRazhPJyxxqyq67ODbwicgNsrTZqfkt9c2OL0+yf26oM15V+cTps+tbpbFJTY7+ldG5Vzhjv4huXAkSL3xeXODM/JPWMpo6SavfXHn0xUWk9ZK6b5af3uaW26avyJr/ZRkI8+mUDjVqU1aX5KXoLp0ggHStMAAAAAAGBIgiDQ4r9bWBEHG70rIvIujtiOYY1/dVSRu6o4WjlKqe6U5n7yaSUTpXUZUwAAAAAYSxt+ut52hFMypznyLgxPEa3D3amcSR9ZEPLjNNdN7NWMqtSIf94/lNOEFem8ZSH/lRFCTkqqfTGl6K7Clu/fM6FPH6rtVrk/KwecDnU4O2zHGBbjGEVmV0nlPnjZSJHZVfLOD89+aiwlTbeavLVKm978FYE4tn0KRoGumdCr3z6tXXV+/vtiIadL+005TX42peoGpksjXChNAwAAAACAIdk+b2voJz8VgpnqMGlZknd5RJF7KU6PVueODs3/k+eUy3IJYQAAAAA4Vro/rYZfbLQd45QiN4XnGMGASajXac5fGOJ/t8+MJXXtxN7BNzwJpyfQxEUpmaP+SU0PDiNlctLExWnFGzIFvd3Lx/fr7sldMmVeAexxD6rbOWA7xrA4da78D4TnPbwYIndWyZtVmcM/ek2LDrn1+ScS/Ro7ipOa6GY0Z1qHbqzpkXuCx6kQ06VNOtD4lWlNWpCS1810aYQPpWkAAAAAADCozEBaS//vYtsxii8iRT8al/GOHLILCjlaocR4syKKfDQuVciVLItl9+JdWv7VJbZjAAAAAEDobH12s1JdSdsxTso525X7rnBM7wyUU7u73XaMIZvoZnX35ETesuEcYjHpQDULU3KPuXgTJSuMhpE0flVG41alCzpOdVb1gGZPTsgp8+J0h7ND/abddoxh8a+OynlHeR7c9G+Lybs8vzBdCceyAwVKOHvU5m1RYBhUMXSBLqnu1yOnt+us6AmK5gUS2Z/V5GeSim8p7GR/oJAoTQMAAAAAgEG99b031b2vy3aMoovcUSVncv5BdFOI0QolzLvAV/T+ePlfyrLIVn//TW36ZbinpwEAAADAWKt/fJ3tCKfkh2jKdMLZq4zptx1jSFwF+siUTlW5R8p7QTCM6ZVBoAlL0/I7yr/8BzuqG7KauCQtZQv3HLuwOqmP1HXKLefitJFa3S1KaeQT5G2I3BOXorZTFJZ/U1T+1fm/VBAEZX8sO1BObe5WJdw9tqOUlLiT00frErpzcpeiTv57VKF69iYZaMLytGpfTsstrbcIVCBK0wAAAAAA4JS6D3TpzX993XaMonMv9eW9uzIvZTgY912+og9USzw8o/LKX72oprUHbccAAAAAgFBorm9S87om2zFOypnhyT0nHGcQp9SrLmef7RhDduukbp0WyeQtG06Pb9zqjGJ7mR6K4ortyqn2pZRMqnAl53PjKf3GlE55pnyL04HJqsVrUFYp21GGzJnoKHJble0YBeNdG5V/Xf5JPZVQmM4qpWa3Xn1OS/6K8n25FcS7qpL6+OltOjd+4it7FOJpE91zeLp01XamS6M0UJoGAAAAAACntPyrS5UZyAy+YQkzdY4it5fPgfNicN/hKfpQddlNZRlL2WRWz33qGfU299iOAgAAAADW1T8W9inT4fgHcKBA7e526egSZogLYpdU9+uycSOfiB1rzKp6A6UrjI1Ic6BJ81Nyegv3onpnVUq/OaVDEVO+xf+sSarFbVBOpfNa9S6NyD0/HCfCjIb3nogiN1deYTqlXjV565RyuvNXBJLK+1cfMd/kdPukLv3GlE5Vu/nvRwWbLj0QaOLilGoWpeUes+sP8UcVgNI0AAAAAAA4uQOr9mvLM5tsxyguX4p+NC7jHzm6GhTqqGGZcc/yFHt4nFTFkeiR6m3u0XO//0zZn4gAAAAAAKeS7EqG+niDe74n94xwlOt6nIPHl8RC+s/yaX5at9Z2jfjn/UM5TViRzlvGERoUm9cZaNK8pLyOwpWcp8fS+tjUTkXLuDidcnrU7m5TUEKv0sidVVI8pG+gQ+Be5ivyofzBH5VQmO437Wr21itrTjApubx/9RE7I5LSI6e1n/QkplE/ZYJA0Z1Z1T2dVGzXid/n+NMgzChNAwAAAACAEwpygRb/7ULbMYoucnuVnClu3rJyP9A8Gs7prmK/XS2N4zEaqaY1B7XwCy9RzgcAAABQsTb/cqMy/SE9mdRI/o2xwbcbAxkl1ensth1jSKImpw/XJeQf1UIZzj97nZ5ANYtSOrpjygBRjBW3T6qdn5J/sHCTk8+MpvXQtA5VOeVbnO5zWpVw9tiOMWSm2lHkrtK82qB7sX9c9nIvTAcK1OXsV4vboMCUzlRzmxwFun5ijx6e1qFaP/8xK9ShaKcv0MRFadUsScs5psfO0W6UCkrTAAAAAADghBqe2qBDG5ptxygq992+vEsjtmOUHGfK4eK0mVC+B+WLbdPPN2rtD1fbjgEAAAAA/z97/x3m1nmeCdz3e95zUGcG0zlUo9W7ZEtW77YsS1anZRXKlh0pTrIl8a6/3XzZzWY3jr3xZu18adcmu9nYXjuSe2xJlEiJEjsliqocliFFctjLVHQMgNPe7w9a5GAKpwE4B5j7d12+LuvgAOcZDAYgnnOf5606pRS2PtftdRmTkpca0DpPXlzt1QWvCgoJ2VsaFPNtGknh/vbUuIDWdLN8wlJoXmVCK4zZXqbqiKZDs4CW1y2E9pYvnLkgYGNJZxxRrX4Dn2l5CFlROz1k/UID8grD6zJmRF6gI/BguCQgXf+BaRcJrRdJuY8fBtPUptv40oI4borloE3wnJVjunRot42254sIHeJ0aaptDE0TERERERER0TjFTBFv/M/1XpdRUaJNQ+Ce2pws4gdaq0Twyw0QLWwvzdb6b63BgfX7vS6DiIiIiIioqo6+cwTDu4a9LmNiGmDcGizZ5FUoLS+GkdfipRt9mka6oWkE54XN2d3ZVWhab8FI+DYRTvOIcIHYeguRbXbZxrJ2BBw8uSCBRlm/wem43IOCSHldxrQF7grXzDAI7RwdgcURCG3+BKZd2BiQ25GVfaU38GNiEgpXNYzgK13DWBiszCoeWlah+TULsTdtaNbYoxPVHp7VIiIiIiIiIqJx3v7bjcgPjXhdRuXoQHBxBCJQ2mymmdFiGoJPRSHa2WKaDeUqLP83LyG5P+F1KURERERERFWz9VkfT5m+0oDW6v2UaRc24rLXk2PP1KJgEbfEsrO+f8MH9qQTK4m80viejca3bcAtz3tAq+HgiwviiMnKBBo9JxSG5A5YyHtdybSIkEDg/ojXZUxJO0si+IUIhD5/AtMW8ujTu1HUxoTwFXx74ZCXGqSDRzuSuKs1A2NMi74s/4RRCuGdNtpeKCJ4jNOlqX7wjBYRERERERERlUjsjWPzD973uoyKMu4KQVsgS7bVc7O5krRGDaEvRSEWsM00G8VUAUuf+TWKmaLXpRAREREREVXcyPAI9izf5XUZE5OAcUuoZJNXvYKE3AdXjBrl6NPrvBulgwfbUxg1AHVGIa3QHgfRbfU7fZdqW2Sng9haC7DL8wcY0118cUECrXp9BqddYWNQ74GL2vj55Nk69GsCXpcxKe00ieBjUQij9HOonnvYBZFCv94NW4wJ3zMwPaELwwU80zWMcyZZ6WGuLxWZdtHyiommTTa0MX/WPv1nCdG08WwWEREREREREZVY/601cK36nfAjLzFgXBWcekeaNhHVEPpiA7TT5NQ70zjxPXG8+u+WQZVpehEREREREZFf9fxiGxzTnyFZ/aoAtKaTEQqvpkwXRAo5rb90ow/DYhoUHmpPISJPPk9KTT+kZQy4aNpolWzjt2Lym9BBFy0rTIhCeV6djbqLJxck0GFYU+9cg2yRx6DcAYXa6C0bnwpBtPkvOic6NQSfiEAEffjmXyFZ0YcBuQ2umCB0P3+ehmkJChf3taXwcEcKYVn63lSWf7q4CpFtNtpeNBEYmPgB+SuhWqd7efBdu3ZhxYoVOHDgAJLJJL7yla/gpptuOnH797//fWzcuLHkPmeffTb+83/+zyf+27Is/OIXv8A777wD0zRx8cUXY8mSJWhtbT2xz/DwMH784x9j586dCAQCuPbaa/GFL3wBuu7pj09ERERERETkO/vX7MO+lXu9LqNiRIuGwL1hr8uoSyIsEHwyiuLPcnAP+vMEuJ/te70XG7+7ATf+4S1el0JERERERFQRylXY9ly312VMzACMm0ovsPZimqeCi7jcU/XjzsanWzI4PVga/JzuU6ZlXTSvNiFG5So5SJT8KjCo0LrcROIzBtyGuQdso9LFks4EfjbYgj7TKEOF/lLUUoijF63OeRA+/6sWhkDggTCK/y/nm6s2RJuG0JNRiLD/wtyVoKCQ1PYjI494XUpNOCto4t62FGL6xBcmzHm6dMJF7E0LxtD4Pwh+TlM98TQ1XCwWcdppp+GGG27A97///Qn3ufjii/HMM8+c+O+xQeef/exn2Lx5M7761a8iGo3i5z//Of7u7/4Of/InfwJN0+C6Lv7u7/4O0WgUf/iHf4hcLofvf//7UEphyZIlFf35iIiIiIiIiGqJYzlY92ervS6jciQQ/HzphA6lVF0vaVhtIigQfCKK4i9G4O6tjaU4/eSd/7UJ7Rd34IL7L/K6FCIiIiIiorI7uOEAUgdTXpcxIf3aIERD6ZRpL/oFKe0QbJGv+nFn6pJIHlc3zq5OYSk0r7SgFcZsL0NdRJWipxVal5lIfjoAuwyTicNS4fHOBH4x0IwjZqAMFfpLTuuHocJocs/wupQpydN16DcFYW8oel0KRPPxoRQiOj8C0y4cDMsPkdfiXpfiexIKtzZncW3jyLhg9ExWeZiUqxDd6iC6xS65oGk0fk5TPfH0Xfbyyy/H4sWLcfXVV0/6hUPXdcRisRP/i0ajJ24bGRnBhg0b8Mgjj+CSSy7BokWL8Mwzz+DIkSPo6ekBAGzfvh1Hjx7FM888g0WLFuGSSy7BI488gvXr1yOf9/+XDSIiIiIiIqJq2fKjzUj01m+D0rgzBK1LlmxjYLr8hCEQfDQCeQFX+JqN1/7DKxjY1j/1jkRERERERDVm67ObvS5hYkHAuN77KdMmckhrh6t+3JlqN2zc3Zqe3Z1dhdg6C0bSJyNdiWZA5oGWV0wEjpRnhbWQpvBYZxKLgmZZHs9vktp+jIhhr8uYFuOWIESXt0Fl0SgQfLIBWtP8CEzbKKJf3zI+MM2Ph3E6DQtf7orjuqbxgWlg7oFpfdhF60smGjaPD0zz10H1yvfvtHv27MHXv/51/PEf/zF+9KMfIZ0++Y/vAwcOwHEcXHrppSe2tba2oqurC729vQCAvXv3oqurC62trSf2ufTSS2HbNg4cOFC9H4SIiIiIiIjIx0aGR/DWX7/pdRkVIy/SYVwTnHpHKguhCwQeiUBeWn9LjFaaXbCx9KvPY2Qo53UpREREREREZZM5lsHe13u9LmNCxg1BiHDpqlTVpqAQl3sAoUZv9J2AcPFwexKBUUmTmTxdDe/bCB6eZIQlUQ3QbKB5pYXQnvKssBbQFB7pSOCckPdTjstOAMPyQ5jIel3JlIQUCD4YAeTU+1ZE5PiEaa3F9zG+siiKDPr0zbDEmP6nAscZjyKgcG1jDk91xdEZKH3PKcs/VRyFhvcttL5swkhM/ID8dVC98vXIn8suuwxXXXUV2tvbMTw8jOeffx5/+Zd/if/yX/4LDMNAOp2GpmloaGgouV9TU9OJcHUqlUJTU1PJ7Q0NDdA0DanU5Ev/7N69u/w/ENUE/u6JiIhmj5+jRES1a+tfbYaZrsPmPI4vaxi4L+J1GfOO0AQCD4Vh6oDTbXldTk3JHs3gl1/+Ga77zk3QjPlxsoSIaK74fZSIiGj2qvE5uuuHO6EcH6aAIwL6td5Pmc5qx2BqmdKNvksqKXyuLY02o3TK7nSfrtAeG9Ht5ZnQS+QloYDYGzZkDshdIec85tXQgM93JPHCUAy78qEyVekPSrgY1HuwwL4SOvw90ELrkDBuD8FaWajugUMCoSej0Nq9SmxXV04MIi53Q40daQz48HPPOzHp4N62FM4KTdxXn+s/VYwBF01vWtBT4/9tVm/Z9cHBIQyyZzQvnX/++ZPe5uvQ9LXXXnvi/59xxhlYtGgR/uiP/ghbt27FVVddVdFjn+pJo/q1e/du/u6JiIhmiZ+jRES1a3D7AA4tq9PVmDQg8HAEIlQ6McqLE6DzkRACwfsjMI087Hfrc6nRSklsi+PwPx/Ap/78M3y9EhFNgd9HiYiIZq8an6Ou7WLtipUVPcZsGTcGIQLe9gxsFJHU/N+XuaZxBBdFZnfBvdHvomnjmCmZqK9QFs0/DZttaCMKmet0QJvbq1kK4KH2FJYOK+wYCZepQn9whPmb4PQV0Dwb5Tw9+vUBOLstuAerdIFHAAg+EYG2wN/PSzkoKKS1Q0jJg16X4nMKl0UL+ExLBkGtAhebWQoNH9iI7HAm/Qyut8/mjo52NLNnRGPU1Kia5uZmNDc3o7+/H8DxidKu6yKbLV3KIZ1On5guHYvFTkyd/kg2m4XruojFYtUpnIiIiIiIiMinlFJY+41VvlzytRyMT4cgTy+9ZtxPAdQRMYQj+js4pr+PvEh4XU7FBO4OQ78h4HUZNWfbj7dgyz9v9roMIiIiIiKiOdm3she5/uzUO1aZaBTQP1n6XbXaPQMFhYTshRLO6I2+c0bQxB3Ns/sdalkXzatNjB4qysA01YvILgfNqy3AmvsfriaAB9rSuCKaL0Nl/mKJHIblh1B+fIMbRQiBwAMRoBptTB0IPhYd17uuRwouhuUuBqanENZcPNSewn1t6XGBaVWGPx3jmIO2F01EJwhM+/svk6j8aio0nclkkEwmT4SdFy1aBCklenp6TuwTj8fR19eHc889FwBwzjnnoK+vD/F4/MQ+PT090HUdixYtqu4PQEREREREROQze5btwpFNh70uoyLkBTqM6/y77GNexDEkd8IRRVhiBINyOzLaUd+fPJitwKfDMG7z7+/Dr9Z9YzUOb+QJBSIiIiIiql1bnu32uoQJ6TcHIXRvo7t5MYy8Fi/d6LM0cVRz8FB7qmSQ7nTDW8JSaF5pQRszoNpnPyLRnAQPu2hdYULk597TEwL4XFsaVzWMlKEyf8lrcSS1/V6XMSWtWUPgMxWe9i2B4BcikIvqPzDtwES/3IoRbbD0hvpsgc/aOaEinlk4POmKDnO5pkuYCo0bLbSusKBnJ37i+blM842n776FQgEDAwMAjk+2isfjOHjwIKLRKKLRKJYuXYqrrroKsVgMw8PD+NWvfoXGxkZcddVVAIBIJIKbb74Zv/zlL9HY2IiGhgb8/Oc/x+mnn45LLrkEAHDppZfitNNOw/e//308+uijyGaz+OUvf4lbbrkF4XB9LWlBRERERERENBNW3sL6/77G6zIqQsQEAvdHSrZ5scTuZExkMSR3lnYjBZCQe2FhBC3uORC1da37tBi3hABDwHq94HUpNcO1Xbz8r5bi8RefROysZq/LISIiIiIimpHkgSQOrtvvdRnjiGYB/ePerojkwkZc9npaw1Q0KDzUnkKDdEu2T6u94irE1lkwkkzGUf0zhhRal5tI3mnAaZp7T++u1gx0ofB2JlqG6vwjI4/AUGE0qC6vSzkl/RMB2B9acPfY5X9wDQgsjkCea5T/sX3GRA6Deg8cMSYIzOUGTjCEwh3NGVzVOH7CvFJzC0sDQOCwg6aNFmT9XYdBNCeehqYPHDiA7373uyf++8UXX8SLL76IG264AV/84hdx+PBhbNy4ESMjI4jFYrjwwgvxu7/7uwiFQifu89hjj0HTNPzjP/4jLMvCRRddhKeffhqadvwfIZqm4fd///fx3HPP4S/+4i9gGAauu+46PPLII1X/eYmIiIiIiIj85P1/fAeZIxmvyyg/DQg8HIEIn+wo+ikwbaOIQb0HSrgT3p6VfbBEHu3ORZCov+a5cX0QMABrOYPT01VI5PHS77yAL/zLEwhEvT2pT0RERERENBPbnvPnlGnj1hCE9LZPkJD74Arr5AYfhshua87izJA19Y4TaHjfRvDwxL0PonqkZ44HpxOfCsDumHtw+lMtWehC4c10FL57c5iDuNwD3QkhpJq9LuWUgveFkf8/WaAME8RPEEDggTD0C+uv5zvW8VUWP4QSzvgb6+flPCcLAxbua0uhzZjgOcIcp0sXFRrfthDeO/5z2If/3CCqOpFM8rI+oo/s3r0b559/vtdlEBER1SR+jhIR1ZbM0TR+dMf3YRcqMC3DY8anQjBuDHpdxoRc2OjXt8ASU4920FUIHfYlMBCZct9aZG8xYS7NcynGGTjvnvPxub9/AEJjW5uIaDR+HyUiIpq9Sn6O2kUb37vu/6CQGD890UuiXUPodxs8vbi6IFIY0Ld6dvzpuCBcwOKO1KzuG9ptI/Zm/fWciKZD6UDyVgPmmbIsj7cxFcHaVAPqKeYolESXfaXv+552jwXzV+Ub0Ru4Nwz9E/U9EEFBIaMdRVLbV08v2bISULixKYebYjmMbfOWY7p08ICDxrcsSM4sAQAUH/9XsO55zOsyyGfqb51XIiIiIiIiIprShm+vq8vAtHau7tvAtILCkPxwWoFpALBFAX16N/IiUeHKvKFfEUDg4TC7UzOwZ/luvP13G70ug4iIiIiIaFr2LN/tu8A0ABi3hTwNTCu4iMs9nh1/Olp1G/e2pUu2qWle9Gz0u2h6q7TnxOulaT4RNtC82kJ4V3l6rzfERnBnSwb19JekhINBvQcOZjfJvlr0SwzIS8szFdq4KzQuMK2m+8ZaIxRcJLReJCUD05Np0W18aUEctzSPD0wDcwtMa3mF2BoTzWvGB6br65VGNHc8LUVEREREREQ0zxx55zB2vbjT6zLKTjQKBB8Ml2zzS+NZQSGh9aKgzSwArYSDQbkdGe0oVB22NvVLAgg+EgHKM3hnXnjr//cm9ryy2+syiIiIiIiIprT12c1elzCO6NKgX1yeANxspbRDsIX/wuQfMYTCw+1JBLWTfYjpTr7UMi6aV5sQ7sltCszO0fwjFNC00Ub0A2v6Vxycwicb87i7NQNRR/1BWxQwJHdAwZ16Zw8F7g5DNM7tXcy4PQjj2tJBH0opTy/gKTcXNgbkdmRlX+kN9fOSnSOFjzeM4Le6hnFasMzDbJRCqNdB2wtFhA5M/PdUP680ovJgaJqIiIiIiIhoHnEdF2v/2yqvyyg/AQQejkBETrY6/NR4zmhHxjeMp0sACbkXCa3X9ycRZkNeYCD4WATw9px1TVnx75dhaOeg12UQERERERFNaujDQRx954jXZYwTuD3k6fFN5JDWDntaw6kp3N2aRkfAKdk6nfaKMBVaVlnQimO2l7E6olrTsMVB0xs24M49OfrxhjzubUvXVXC6qKUxLHf7eliECAsE7gtPveMk9BuDMG4u/ezxU9+6HCzk0ad3o6ilSm/gVTMAgKjm4AsdSdzdmkFgTFJzrtdUaDmF5lUWYhvGf/7696+KyHsMTRMRERERERHNIz2/2I7B7QNel1F2xm1ByLP0km1+aTyPiCEktf2lG2fRsczKPgzI7b5ftnI25DkGgk9EgcDU+xJgjVhY+tvPIx8f8boUIiIiIiKiCW19ttvrEsbRzpSQ53l3xa6CQlzuOT6C9uRGX/lEQx6XRgszv6OrEFtvQU/67Aci8oFwr4PmlRaENfe/j8uiBTzYnoLmtzePORjRBpHWDnldxinJcw3oV828calfE0DgU/UdmC6IFPr17vErKDAwDQC4IFzAMwuHcW7YnPD2Wb8UlEJ4l422F4oIHuZ0aaKZYmiaiIiIiIiIaJ4opot48zvrvS6j7LSzdeg3Bafe0QNFkcGw3FXaoZxDw7ioHW9CW6i/sKw8S0fwi1EgxHbudKQPpbDsXy+FYzlT70xERERERFRFZs7Ezl/1eF3GOIbHU6az2jGYWqZ0o4++Ai8MWLizJTP1jhNoeM+eNLRFREDwqIuWV0xo+bmHnS+KFLG4IwlZR8HplDyInPD3qmrGnSGIlunH7OTHDQQ+Wzqhut4C01nRhwG5Da6wx99YPz/mrASEi8+1prC4I4WILP1bnfN06YyL5tcsNG20oY2Zr1I/7wpElcXQNBEREREREdE88fbfbkR+qM7Ctg0CwYfCJc1mNdeuY5nYKGBQ9kCJUScNyzBhwxYF9OndyIvE3B7Ih+RpOkJfigLRed5Vn6bDGw9h/TfXeF0GERERERFRiV0v7oSZnXiiole0c3TIRfrUO1aIjSKS2gHPjj+VsObi4fYk5Kiv49Ntr4R224j28IJeoqkYcYXWZSZkau4XGJwXNvFIRxKG8EcftByG5S4URdrrMiYlAgKBB8LT6u3KSw0E7q3fwLSCQkLbh7g+ZvUEAgCcETTx9MJhXNEw8coNc5ouvcNG+4smgsc4XZpoLhiaJiIiIiIiIpoHEr1xbP7B+16XUV4CCD4UgYiebG/4pfnswsaAvh2uGDPqoUylKeFgUG5HRjtangf0EW2BROhLUYhG73+PtaD7hx9g20+2eF0GERERERERgOPfy7c+2+11GeMYt3u3QpWCQkL2Qgln9EbfEFB4oC2FJr00gDWd9orR56LprdIJoz760Yh8R2YVWpebMAbmHpw+O2zi0Y4EAqJOprwLhUG5AzYmDpr6gTxTh37DqT9P5AU6Ag+OH/Lhh551ObhwMCR3ICOPeF2K70go3BbL4MnOBJrHfKbOdc6LTB2fVt/0to2xg735uUs0cwxNExEREREREc0D6761Bq5dJw3039BvDkJ+rHRKlB+azwru8Qa/yFf2QAJIyL2Ia3ugUF+/W61dIvhUA0Sz97/PWrD6T17H0Xd4ooKIiIiILmQcpAABAABJREFUiLzXv6UPA9v6vS6jhLxAhzzNuynTeTGMvBYv3eijr7s3x3I4OzzzyeAy46J5jYkyL7BFVPe0ItCywkTwwNwntJ8ZsvB4ZwIhrT56g66wMKj3wIU99c4eMW4LQnROHLfTztERWByB0OozMG2jiH59y/jPNKZ20W7YeKorjhtiIxNedDTrl4CrENlqo+1FE4GBiZ/o+nh1EVUXQ9NEREREREREdW7/6r3Yv2qv12WUlbZIwrjVuylRk1FQiMs9KGqpqh0zK/swILfDgTX1zjVEa9GOB6db2b6aimu5eOn3XkDmqH+XMCUiIiIiovlh6z/7bMq0AIzbQ54d3oWNuOz17PhTOTdUxE2x3IzvJ0yF5pUWtOKY7WWqi6jeCQeIrbEQ3jH3cPBpQRtPdCYQrpPgtCVGMCR3Qvk0iSukQPDBCCBLt2tnSQS/EIHQ6zMwXRQZ9OvdsMSYz4x5f7WMwjWNOXylaxgLAuUN++txF63LTDS+b2PsQHl//nUQ1Q6edSIiIiIiIiKqY47pYN0313hcRZlFBYIPRcYtcegHae0wctpA1Y9b1FLHm9YYqfqxK0lr0hB6Kjrp9BY6KT80gpe++gKsfH2F54mIiIiIqHYUUgXsWrrT6zJKyEsNaJ0nk23V7h8k5D64YtT3NH+0LwAAMWnj/vbSi76n9fS4CrF1FvSUj34YohokADS9baPhPWuaf3yTWxCw8eSCOKLa3KdX+0FBSyKh+XcIiLagdKCHdppE8LEohFGaHq6XwPSIGMKA3ApHTLAqQX38iLPSKB080ZnAp1uy0Mc8D3P6k3YUopsttL5kwhjmdGmiSuAZJyIiIiIiIqI61v2jD5DojU+9Yw0JPhiGaDzZ0vDLxI6cGERKHvDs+LYooE/vRl4kPKuhEkSDhtCXotAWyql3nucGtvXj9T981TcXERARERER0fyy81c9sAvlnbI4JxrGrVJVzf5BQaSQ0/pLN3rfvgAA6ELh4Y4UQtrJ749KAdN5ehresxE8Uh8TbYn8ILrNQdN6C3Dm1s9pNxw8uSCBJlkfwemsPIaMdtTrMial3xCEdoaE6NQQfCICEfTJG3wZKSiktIMY0ndCjR11PK8pXBLJ45mFw1gUmniAxWz/uaEPuWh9yURDtwMx5i2BHV+i8mFomoiIiIiIiKhOjQzlsOlvNnpdRlnpNwUhzzFKtvkhMF0QKQzLXaUbPehiKuFgUG739QmF2RBhDcEvRqGdyeD0VHa9uBPv/sPbXpdBRERERETzjFIKW5/d7HUZJeSVBrRWb6ZMK7iIyz1VO95MfaYlja5AacB9Ou2V0G4b0Z76CGQS+Ul4n4vm1y0Ic27vU62GgycXxNGs++gCljlIaHuRF/4cCCI0gcCDEYSejEKE6y9+p+BiWO5CSh70uhRfCWkuHmhL4YH2dMmFR8Acp0vbCg3vWmhdZsJIcro0UaXV37s2EREREREREQEANn73DZjpotdllI12poRxW3DqHavMQh5DcgdKRj8oeNfFFEBC7kVc64VC/UwAEUGB4BNRaGczOD2VN//neuxd2et1GURERERENI8c2XQY8T0+CrZJwLglVLKpmhddp7RDsEW+asebiSuieVzZUJjx/Yw+F01vlQYxOfWSqHyCfS5aXjGh5eb2lxXTXTzZmUBbPQSnBTAkP4SJnNeVTEhr0SCi9Re9c2CiX27FiDZYesM8f9P/WKiIZ7qGcUl04nMus/1nhtHvom2pieh2Tpcmqpb6e+cmIiIiIiIiIgxs68e2n27xuozyCQsEHo5AaCc7j9WcEDUZBxYG9e1wxZiTED4Y+5CVxzAgt8NFHZwg+Q0REAg+FoV2nu51Kf6mgFf/4GXEdw97XQkREREREc0TfpsyrV8VgNZ0Mg5RzR6CiRzS2uGqHW8mFhgW7mpNz/h+MuOieY0JMerabC+vFyeqV0ZCoXVZETIxt0EIjbqLJQvi6DSsMlXmHSUcDOo9cGB6Xcq8YCKHPr0bppYpvWEev+nrQuHOljQe70yiUS/925zLPy+EpdC4yULrKyb0NKdLE1UTQ9NEREREREREdUYphbXfWF1XYwiCD4RLTnYC1Z0QNREFF0NyB2wx8+lM1VLUUujTu2HBn9OtZkPoAsEvRCAvZnD6VMysiaVffR6FlH9fn0REREREVB9ygznseWW312WcZADGTaUrVVWrh6CgEJd7xq9G5QMhzcXDHUnoo56K6YS9hKnQvNKCNmawJoNcRJUhR4DW5SaMvrkFp6NS4YnOBLoCtR+cdkQRg7IHLhyvS6lreRFHv74FjphgkvI8fdNfYFj4StcwPtk4cX99tv+8CBx10PZCEZGdfE0TeYGhaSIiIiIiIqI6s/vlD3H0bX9ONJoN/foA5PmG12WUUFAYlrtR1GY+nanabJFHn74ZBZH0upSyEfL45HF5ub9eF36T3JfAK7//ElxnbifZiIiIiIiITqXnF9vgWv753qFfE4Ro8GbKdFY7Nn46py+CZgr3taXQPGZC5pRhL1chts6CnvJJ8ptontAsoOU1E8F9cwtUhn8TnD4jWPtTmk0ti2G5C8ovV6LUEQWFjHYUg7IHSjDECwACCjc0ZfFUVxztRulzMqfp0qZC0xsWWl6zIHNzLJKIZo2haSIiIiIiIqI6YuUtrP/va70uo2y00yWMO0JelzFOSjuIEW3Q6zKmTQkHA3IbMtoxr0spG6EJBB+MQL8q4HUpvnZg7X688e11XpdBRERERER1ynVcbHuu2+syTgoCxg3eTJm2UURSO1CVY83UDU05nBeeeWiy4T0bwSP+CcQTzSfCBZrXWYhst+f0OEFN4dGOBBbVQXA6rw0j5dP32Vql4CKh9SIh9/rkIh/vNes2nlyQwG3NOcgJnpPZ/rMieNBB2/NFhPcwmE7kNYamiYiIiIiIiOrIe//nHWSPZqbesRaEBAKLIxCjOpPVnA41mazoR1oe8rqMmRNAQvYirvVCoX5OeAY+F4Z+HYPTp/L+/30XO/5lu9dlEBERERFRHTq4bj/Sh/2zCpNxfRAiXP0+goJCQvaWTuj0voUBAPhYqIhbYzMfZxneZSPaw2AXkdca37XR8LY1p/G2AQ34QmcC54SKZazMG2l5GFnR73UZdcGFjUHZg6zsK73BJ59f1adwRXQET3fFcUbQKtujioJC0zoTzastyPzYIxKRFxiaJiIiIiIiIqoT6SNpvPcPb3tdRtkE7g9Di5W2Lqo1HWoyBZFEXO4p3Vhjnc2sPIYBuR0u5jalxk8CnwlDvzk49Y7z2Mr/tAJ9m+tn0jgREREREfnDlmd9NGU6IqBf582U6bwYRl6Ll270wcTOJunggbZUyVTM6eQujT4HjW+V9g1qrP1BVFeiOxzE1lqAM/u/RF0An+9I4oJwoYyVeSMu96AgUl6XUdMs5NGnd6OgJUtvUPDF51e1RTQXn29P4XNtGQS00r+zWV+voBSC+x20v1BEeN/EQ0zm4VNN5AsMTRMRERERERHViTf+xzrYhfoIwurXBKBfaHhdRgkLIxiUOwAxqktao03kopZCn94NC/mpd64RgdtDMO5gcHoyTtHBS199Htn+rNelEBERERFRnUgfSWP/qr1el3GCcWMQIlD9KdMubMRlb1WONRMSCg+1JxGRJ58HpYCpcuQy7aJ5jVUP7Q+iuhI64KJlhQlRnP17mxTAQ+0pXBKp8Z6gUBiSO+qqt1lNBZFCv94NW0ww9ngevtmfFy7gmYVDOD8y8ST22Vx/pY0oxFZbaF5rQRtznQIvQiLyHkPTRERERERERHXgyNuHsevFnV6XURbaQgnjzpDXZZRwYGJA3166zC5Q001kW+TRp29GQSS9LqVsjJtCMO7y12vHT3IDObz0O8/XzcUVRERERETkre0/3QLl+iP6IxoF9E8GSrdVacp0Qu6DK6yTG/zxlOBTLRmcFiz9/jfVUyJMheZVFrQxubEabn8Q1ZXAgELrchNadvZvNJoA7m9L44pobQeOXWFjUO+BA2vqnemErOjHgNwGV0zQH5xnb/aGcHF3axqPdKQQleWbLh3aY6PthSJChzhdmsivGJomIiIiIiIiqnGu42Ltn67yuozyCAKBxREIWf3JUJNx4WBQ9sARE0+aqGVKOBiQ25DRjnldStkY1wYRuC/M7vMk+jf3YdUfv+b53xUREREREdU2x3Kw/adbvS7jBP3mIIRe/S+CBZFCTusv3eiD76OXRPK4unGGgUhXIbbOgp7i90UiP9NTCq3LitDjEwcyp0MI4HNtaVzVMFLGyqrPFnkMyZ1QmP1zMV8oKCS0fYjru0tXUpynTg+YeLorjo83TPxZOavp0lmF5tctxN6woZmlt/EZJ/IXhqaJiIiIiIiIalzPz7dhcPuA12WUReC+CLSW0nZFtSZDTURBYVjugqllPauh4gSQkL2Ia71QddK+1T8eQODBMDtfk9jxy+344HvveV0GERERERHVsL2v9SI3kPO6DACAaBbQPx6YescyU3ARl3uqftypdBgW7mlNz/h+De/aCB5h8JCoFsg80PKKicBRZ+qdT+Gu1gyubfTHe/lsFbUU4rJ++pqV4MLBkNyBjDzidSme06BwSyyLJxck0GKU/v3MZbp0eOfx6dLBo5wuTVQLeOqIiIiIiIiIqIYV00W8+Z31XpdRFvrVAegXG16XUSKp7UdeG/a6jKrIymMYlNvhYoKlGWuQflkAgcURQHpdiT9t+O9rcWDdfq/LICIiIiKiGrX12c1el3CCcWuoZMWqaklph2CLGU5zrrCAcPFwewrGqCTIdEJg4V02ojvmFr4kourSLKD5dQuh3rn97X6qJYubmrKo5Vm4Oa0fGY2B4InYKKJf34K8Fi+9oXZ/3bPWptv40oI4borloE3wz4bZzG6RaRctr5po2mRDG9NWn4dPMVHNYGiaiIiIiIiIqIZt+ps3kR/21wm62RALNBifCXldRomMdmzeTd8oaEn06d2wUPuvKQDQLzIQfDQC6F5X4j/KVVj+b5YisS/hdSlERERERFRjEnvjOPTGQa/LAACIdg3y8upfgG0ih7R2uOrHPTWFe9vSaB0zOXOqEJjR56DxrdKkF4NeRLVBKCC2wUJkqz2HMbnALc053N5c28HppLYfI2LI6zJ8pSgy6Ne7YYkx08QV5tnoY4WrG0bwla5hLAyWaWCIqxDZbqPtRROB/on/bubVU0xUYxiaJiIiIiIiIqpRid44uv/fB16XMXcBIPj5CIR+so2o5tDkL4e8SCCh9ZZurN1zBjNiizz69M0oiKTXpZSFPNdA8IkoUP2Vmn2vmC7ipd/+NYqZotelEBERERFRDdn24y1el3CCcVsIYjajIedAQSEu9xxPK57c6LlrG0dwYWRm3+9k2kXzGmvcj8KgF1FtaXzfRuMmG3Bn/2Z0fdMIPtOSgS/e0GZDAMNyF0xkva7EF0bEEAbkVjjCHH/jPHqTb5AOHutI4jOtmZJVGIDZX2cgky5alptofNeGGDPovUb/eojmHYamiYiIiIiIiGrUum+uhmu7XpcxZ4F7w9BaZcm2ap/wHM1EFkNyZ2nzeJ6dMVTCwYDchox2zOtSykIu0hFcEgX8NczcF+J74nj1ay/DdWr/vYSIiIiIiCrPLljo+cU2r8sAAIguDfrF1Z8yndWOwdQyY4qpehklzgyav5kSO33CVGheZUEbk7OeR+0PoroS+dBBbI0F2LOPbV7dmMc9rWmIGo1+KuFiUO+Bjfk7IEBBIaUdxJC+E0rM737fRZECnukaxtnhCYLjmHolhnFchegWG21LTQSGOF2aqJYxNE1ERERERERUg/at2ov9q/d5XcacyU8Y0C/1zwhgG0UM6j1Qo0dEzLPA9AkCSMhexLVeqBo9UTKaPENH6IsNQGQ+/jJPbd/Kvdj4l294XQYREREREdWA3ct2oZAseF0GACBwe/WvjLVRRFI7UPXjnkpUc/BgewraqK+7U07PdBViay3oqdr/vk9EJ4UOuWhZYUIUZv+3fWVDAfe11W5w2hEmBvUeuHCm3rnOKLgYlruQkge9LsVTQeHivrYUHmpPISxLX8eznS6tD7tofdlEwwc2xmbRa/MvhWh+Y2iaiIiIiIiIqMY4poN131ztdRlzJjo1BO4Ke13GCS4cDOo945csnOcZ26w8hkG5HS5sr0uZM61LIvSlKETDPP+lTuDd/7UJu5bu9LoMIiIiIiLyua3/3O11CQAA7UwJeV51p0wrKCRk7/gLrT2kQeGh9hQaZGmCa6rpmQ3v2ggend8TSInqVWBQoXW5CZmZ/d/4pdECHmpPQfP6TW6WLJHDsPywLgZBTJcDE/1yK0a0wdIb5s9TAAA4K2ji6YXDuCw68QVeM54u7ShE37fQ+rIJI87p0kT1gqFpIiIiIiIiohrT/cMPkNyb8LqMuTGA4OIIhHGypahmO+ahDBQUhuROWCLnWQ1+VtCS6NO7YSHvdSlzpnVIBJ+KQsTYzh7rtf/wCga29ntdBhERERER+dRgzwCOvX/U6zIAAIYHU6bzYhh5LV660eOvlrc3Z3FmyJrRfcK7bER3zL8JrETziZ5WaFlmQh+afXD6wkgRizuS0EVtpm7zWhxJrfZXapwOEzn06d0wtUzpDfNoBUUJhU81Z7BkQQIxvfR1P9vTDsagi7alJhq2Ohj7Z1CbfxVE9BGGpomIiIiIiIhqyMhQDpv+5k2vy5izwD1haO2yZJuY8ZiH8kloe1HQajyIXmG2yKNf70ZBJL0uZc60VongUw0QLWyNjWYXbCz9neeRG+TFA0RERERENN7W53wyZfocHXKRXtVjurARl71VPeZULgwXcG3TyIzuYxxz0PhW6UpSDH4R1SdZAFpeNRE4PPuLJM4Lm3ikIwmjRoPTGXkUWdHndRkVlRdx9Otb4Iji+BvnSWC607Dwla7hST8TZ3zawVZoeMc6fuFBitOlieoRzwwRERERERER1ZCN330DZsb0uow5kVcY0K8IeF3GCWntCLLymNdl1ARX2BiQ25DRav/50mIaQk9FIdrZHhstezSDl3/vRTgmp44REREREdFJZtbEzl/3eF0GAMC4PVj1YybkPrhi1ERnj/ODrbqNz7WlS7ZNNUlTpl00r7FKpmXOoyGkRPOSZgPNqyyEdttT7zyJj4VMPNqRQEDMfmq1l+JyT10MgRhLQSGjHcWg7IES87OPJ6BwXWMOX+6KoyNQ+hzMerp0n4O2F01Ee5xxn4+1eekAEU2EZ4WIiIiIiIiIasTA1n5s++kWr8uYE9GuIXB32OsyThgRw+OXaWT389QEkJC9iGu9UDX+ZInG3wSnu9giG+3Yu0ew5r+uhJrt2QUiIiIiIqo7H76wA1bOmnrHCpMX6JCnVXfKdEGkkNP6Szd6mDQ2hIuH25MIaie/syl16kmawlRoXmVBG3MdPgPTRPVPKCD2po3oZnvWSdIzQxYe70wgpNVgcFoAg3IHLMxsMr+fKbhIaL1IyL3z9o08Jh0s6UzgjpYs5ATPwUynSwtToXGjhdZXLegZTpcmqnc8I0RERERERERUA5RSWPuNVbUd6NWB4OIIROBke9HLUKaJLIblh6XdTo5YmrasPIZBuR0uZj+pxg9EREPoiw3QTpdel+Ir236yBVt+tNnrMoiIiIiIyAeUUtjy7GavywAEYNwequohFVzE5Z6qHvPUFO5uzYybqHnKcJirEFtrQU/VclOJiOaqodtG40YbcGf3XnBa0MaSzgQiNRicVsLBgL4dDry/+GeuXNgYlD3Iyr7SG+bNW7zC5dE8nl44jDND5fl9Bo44aHuxiMiu+Tmxm2g+YmiaiIiIiIiIqAbsfulDHH3niNdlzEngs2FonaXBVDHTkQ9lYqOAAX071OhlJRmYnrGClkSf3g0Lea9LmRMREgg+GYW2iMHp0dZ+YxUOvXnQ6zKIiIiIiMhjfR8cw1DPoNdlQF5ilPQVqnEhdko7BFv45zvvVQ15XBotzOg+je/aCB6tvZAjEZVfZLeD5lUWhDW798/OgI0lC+JokLUXLnVEEUNyBxRq9/3QQh59ejcKWrL0hnnS1w5rLh5uT+HetnTJagvA7Iaoi6JC0wYTLa9bkLkyFUlENYGhaSIiIiIiIiKfs/IW1v/5Wq/LmBN5mQH9EwGvywDwm2kceg9cMWYSxTxoLFeCLfLo17tREEmvS5kTERAIPh6Fdk51l3n2M+UoLPvXS5E6mPS6FCIiIiIi8tDW57q9LgHQAOO2YMmmSl+IbSKHtHa4oseYidMCJj7dkpnRfcIf2ojsqL1wIxFVTvCIi5ZXTWj52QWn2w0HT3Ym0FSDwemilsaw3A1Vg2OZCyKFfr17/IU88yQwfU6oiGcWDuPCSHHC22f6T4LgAQdtLxQR7q3dED0RzR5D00REREREREQ+997/fhvZozM7KeYnolVD4HNhr8sAcHxZ3SG5E5YY8bqUuuIKGwNyOzLaMa9LmRNhCAQfi0BeyOD0RwqJPJZ+9XmYOdPrUoiIiIiIyAOFZB67ln7odRmQVxrQWqs3ZVpBIS73AEKN3uiZsObiofYU5KhQ2FRPgXHMQeMmu2Rb7cUEiagSjGGFlmUmZGp2gdEWw8GTC+Jo1u2pd/aZEW0Qae2Q12XMSFb0Y0BugysmeL7rPDBtCIW7WtJ4tDOJBln6ep3VdOm8QmytieY1FuQE+XMimh8YmiYiIiIiIiLysfSRNN79h3e8LmP2JBBcHIEInOzeVmP53IkcP+HZO375QioPoZCQvYhrvTU5reUjQgoEPh+BvNTwuhTfGN45hBVfXw7l1u7vlYiIiIiIZqfnl9vhFD0OxUnAuCVUsqnSU6az2jGY2pgL2D0KpgkoPNieQpNeGhY71VMg0y6a11jjMt91nq0johnQswqty00Yg7MLTsd0F092JtBWg8HplDyInBj0uowpKSgktf2I67tLL+KZJxYGLPxW1zCuasxPePuM/imgFEJ7HbS/UERo/8SveX5GEs0fDE0TERERERER+dgb317r/cnJOTDuCkHrkiXbKn1iczIZ7QhyWr8nx55PsvIYBuV2uKjd163QBAIPhSE/zuD0R3pf2Y1Nf7vR6zKIiIiIiKiKlFLY+ly312VAvyoArelktKHSF2PbKCKpHajoMWbillgOHwtNf/UfYSo0r7SgjbkLw2BENJZWBFpeNRE86Mzq/o26iycXxNFpWGWurPKG5S4URdrrMiblwsGQ3IG0POx1KVWnQeHmWBZfWhBHq1H62pzNPwG0nELzKgux9Ra0Yult8y+KTkQAQ9NEREREREREvnVk0yFfLIE7W/JiA8bVQa/LAACMiCEk5f7SjeyIVkxBS6JP74aFiaeA1AIhBIL3RaBfE/C6FN/Y9FdvYs/yXV6XQUREREREVXL4zUNI7k14W4QBGDeV9hYqeTG2wvFVlJRwRm/0zHnhIm6M5aZ/B1chttaCnmbTg4imRzhAbI2F8M7ZDUCISIUlCxJYGKix4LRQGJQ7YKPgdSXj2CiiX9+CvBYvvWEevLW36Da+uCCOm2M5aBN83M94uvRuG20vFBE8zOnSRHQSQ9NEREREREREPuQ6Ltb+6Wqvy5g10aIhcF/Y6zIAAEWRxpAcEz7nmrQVZ4s8+vVuFETS61LmJPDZMPQb/RH+94MVX1+OoZ3+X76UiIiIiIjmbutzm70uAfo1QYiG6k2Zzovh8SE1j/oHMWnjvrZUybapfvzGd2wEj04cDCMimoxQQNMmGw3vW7Ma5RvSFB7vTOCM4PSn4vuBKywM6j2+WjGvKDLo17thiTEXzNR9P1vhEw0jeLprGKcF5/770LIuml+zEHvThjYmzz8PsudENAWGpomIiIiIiIh8qOfn2zDYM+B1GbMjgeDiCETwZBe30ic1J2OjgEHZc7zzf6IY1HmD2T9cYWNAbkdGO+Z1KXMS+FQIxm0MTgOANWJh6TO/Rj4+4nUpRERERERUQbn+LHpf3eNtEUHAuKF6U6Zd2IjL3oo9/kzoQmFxRwoh7WQ/Q6lTT9gMf2gjstOZfAcioilEtzpoesMCnJn3coOawqMdCSwKFitQWeVYYgRDcieUD6K0I2IIA3IrHDFB+LyO+9lRzcEXOpL4bGsGxpgk44xPKyiF8A4bbS+YCB7jdGkimhhD00REREREREQ+U0wV8OZ31ntdxqwZnw5BWyhLtlXypOZkHFgY0LfDFWMmU7ArWl3i+NLGca3XFycfZsu4JQTjzpDXZfhC+nAay/7VUjgWT8YTEREREdWr7T/fBtf2dmKxcX0QIly9C7ITch9cMWocpWdfYRXuakljQaC0n3Gq1opxzEHjptL9a/cbOBF5KdzronmlBWHO/F0koAFf6Ezi3FBtBacLWhIJba9nvUsFhZR2CEP6Tigxv1YLuCBcwDMLh3FueOIp5TM5rSBTLlpeMdH0tg1tzCkBfiYS0WgMTRMRERERERH5zKa/2Yj8cN7rMmZFXqjDuNb7ibwKLobkDtiiNp/HepSVxzAot/tqucuZMq4PwriHwWkAOPzWIaz7s9Vel0FERERERBXgOi62/aTb2yIiAvq11ZsyXRAp5LT+0o0eXXR9RTSPKxoK095fpl00r7G4yBYRlU3wmIuWV01oIzOPmuoCWNyRxIXh6b+P+UFWHkPWg9XyFFwMy11IyQNVP7aXAsLFva0pLO5IISJLX2czvkbKVYhss9G21ERgYOI78zORiEZjaJqIiIiIiIjIR+J7htH9ww+8LmNWREwgcF/E6zKgoDAsd6Oopb0uhcYoaEn06d2wULthduPqIAIPhNlpB7DlR5ux7SdbvC6DiIiIiIjKbP+afcgcyXhag3FjECJYnSnTCi7ick/FHn8mFhgW7mqd/nMvigrNKy1oYwZ08isrEc2VEVdoXVaETM588rEUwIPtKVwaqa0eYELbi7yIV+14Dkz0y60Y0QZLb6jzschnBk08s3AYl09ygdBMrpHSEy5al5lofM+GGLMoXp0/jUQ0BwxNExEREREREfnIum+u8Xz521nRgMDiSFWXzZ1MWjs0vtFMvmGLPPr1bhRE0utSZk2/IoDA4gg7awBW/8nrOPLOYa/LICIiIiKiMtr6rLdTpkWjgP7JQOm2Ck6ZTmmHfLFSVUhz8XBHEvqoH/WUrRVXIbbWgp5mLIyIKkPmgNblJoz+mferNQHc15bGldGRClRWIQIYkh/CRK7ihzKRQ5/eDVMbc6FMHS8VIKFwe3MGSzoTiOmlr6kZn0pwFKLdNlpfMmEMc7o0Ec0MT+0QERERERER+cS+VXtxYM0+r8uYFeOOEOTpesm2Sp7QnExODCAlD1b9uDQzrrAxILcjK/q8LmXW9IsNBL8QAaTXlXjLtVy8/HsvIn2Ek92JiIiIiOpB+lAK+1fv9bQG/eYghF6dnoKJHNKaHy4EVbivLYXmMSGyU7VWGt+xETxWgxfeE1FN0UygZYWJ4AFn6p3HEAK4py2DqxtqJzithINBvQcOzKl3nqW8SKBf3wJHFMffWKdJ33bDxlNdcVzfNDLhZ9uMpksPu2h92UTDZhtizMcgLyMioulgaJqIiIiIiIjIBxzTwbpvrva6jFnRztNh3BD0ugwURArDcnfpRnZJ/UsoxPU9SGh7oWr0FyXPNxB8PAoYXlfirfzQCF766vOw8pbXpRARERER0Rxt+8kWT79Li2YB/eOBqXcsAwWFuNwDCDV6oydubMrhvPD0A3rhnTYiO2ceYCQimg3hArE1FsI99qzu/5nWDK5rrPz05nJxRBGDsgcuyvs+q6CQ0Y5iUG6HEvPlPVzhmsYcvtI1jAWB2b1+TnAUGt6z0PqyCSPB6dJENHsMTRMRERERERH5QPf/ex/JvQmvy5gx0SQQfCBcsk3NeC29ubMwgiG5Y/yJTp93SWPSwaeaM7ixKYsGOV8a5aUy8viJAhdzbJp7RJ6tI7gkCnh/3YCnBrcP4PX/+Ionf/9ERERERFQejulg+8+3elqDcWsIQlbny3xWOwZTy5Ru9KCP8LFQEbfEph8mDBx10Ph26XdofhMjokoTAJresdHwjgXMov9zR0sWN8eyqJV3LFPLYljuKtuwBwUXCa0XCbnX9z3rcmmSDp7oTODTLVmMXUBipi8hY8BF24smotucklMAQK28oojITxiaJiIiIiIiIvJYbjCHTX+70esyZk4DAg9HICIn2wtKKYiZrKVXBg4sDOo9cMWY0K3Pm89dAQu/tXAY1zaN4NbmHJ7pGsbFkYLXZXmioCXRp3fDQt7rUmZFnqkj+GQUCPv8RVdhu5Z+iHf//m2vyyAiIiIiolnqXbEbI4Mjnh1ftGuQl1dnKR8bRSS1A1U51qk0SQcPtKUwupVyqiCZTLmIrbVq7ZpxIqoj0R4HsXUW4Mw8qnpzLIc7mmsnOJ3XhpEqw2eFCxuDsgdZ2Vd6Q208DbOgcEkkj6cXDmNRaOKV6aZ9CsFSaHzbQstyE3qa06WJqDwYmiYiIiIiIiLy2MbvboCZmf4SrH5h3BaEPFMv2VbtwLSCi0HZA1vUVtj49ICJJzoTCGknG71hqfBgewoPtCUR0lwPq/OGLfLo17tREEmvS5kVeZqO0JeiQHR+t+nf/M567H291+syiIiIiIhoFrY+2+3p8Y3bQlXpKygoJGQvlHBGb6w6CYWH2pOIyJMHV2ryIJkoKjSvsqCNaiExME1EXgjtd9HymglhzvzN87qmEXymJYNaSQyn5WFkRf+s728hjz69GwUtWXpDnb6BhzQXD7al8EB7uqT3Dcx8unTgmIO2F01EdjjjnqraePUQkV8xNE1ERERERETkof6tfdj+M2+Xvp0N7Rwdxk0hT2tQUBiWu8YvpetzZwVNPNaZRFCbuLV7SbSIZ7qGcU6oWOXKvOcKGwNyO7Kib+qdfUjrlAg9FYVorMMzHtOlgFe/9jLiu4e9roSIiIiIiGYgvmcYhzce8uz4okuDfnF1pkznxTDyWnxMAVU5dIlPt2RwWrB01axJM+OuQmytNW7K5jz+9klEHgv0K7QuN6HlZh5fvboxj3ta0xA1En2Nyz0oiNSM71cQKfTr3bDFmNX16jQw/bHQ8b72xdGJ+9rTvS5KmAqNb1poWWFBz3K6NBGVH0PTRERERERERB5RSmHtn66uubEIokEg+GC4ZJua6ZiIMkhpBzCiDVX9uHNxdqiIL3QkEJgkMP2RRt3Fo51JfLYlDUPMs6nTQiGu70FC2wtVa38cALQ2ieCXGyCa52/r3syaePG3f41CqrYmwBMRERERzWdbf7zF0+MHbq/OhdkubMSl96vjXBrJ46rG/NQ7/kbj2zaCx+ZZf4CIfE9PKrQuK0JPzPz96cqGAu5rS0Orhf6fUBiSO2Bh+u/bWdGPAbkNrrDH31hnbUNdKNzZksbjnUk06qWvhRlPlz7soO2FIiK7nal3JiKaJYamiYiIiIiIiDyya+mHOPbuEa/LmBkBBB6OQERPthSUUlVZPne0rOhDWh6u6jHn6rxwEZ/vSMKYQTfmE415PN0VxxlBc+qd60xGHsWg3A4XE5xY8DmtWUPwqQaItvnbekvtT2L5v30Jrs2T+kREREREfmcXLOz4xTbPjq+dISHPq86U6YTcB1dYJzd4kNXrMCzc3Zqe9v7hnTYiHzI8RkT+JEeAluUmjGMzf5+6NFrAg+0pyBoITrvCxqC+HQ6sU+6noJDU9iOu7waE/3+uuVpgWPhK1zA+OcmFQNOeLl1QaFpvomWlBTlSelv9P4tEVG3z98wNERERERERkYesvIUNf77W6zJmzLg1CLlIL9lW7cB0XiQQl3tKN/q8c3phuICH25PQZ/FUtRgOnuxM4PbmTE2cQCingpZEn949oykufqE1aQg9FYXonL/tt4Pr9mPDt2vvfY6IiIiIaL7ZtfRDFNNFz45v3FGdKdMFkUJO6y/dWOVpn0Hh4uH2VMkF1aeawhk46qDx7dKLiedXZ4CIaoFmAS2vWwjtnXlw+sJIEYs7ktBrIGBsiwKG5E4oTDwkwIWDIbmz5oZ9zIaAwg1NWTzVFUe7Ufp7n+l06eD+49Olw3snfl7rbDA3EfnA/D1rQ0REREREROShd//hbWSPZbwuY0a0j0noNwc9rcFEDkNyZ2mnVMHXndNLIvnjE1PmUKMQwPVNI/hyVxydxqmnmdQbW+TRr3ejIJJelzJjIqoh9KUotNOk16V45oN/eg87/mW712UQEREREdEpbH2u27Nja+fo4y7OrgQFd/wF2FWncG9bGq1jwmWTXYsuUy5ia62SQaU+b4EQ0TwmXCC23kJkmz3j1Oy5YRNf6EjAEP5fsayopRCXvVBjLmGxUUS/vgV5bbj0Dv7Pgs9Ys27jyQUJ3Nacm7DnPd0ZK1peIbbGRPNaC7JQelsdPm1E5CMMTRMRERERERFVWfpwCu/973e8LmNmogLBhyIlU6XVTEdGzJEDE4N6D5QYdXLR52cLL4/mcX9bGtqoGufytHUGbHy5K44bmnIQ86h17AobA3I7sqLP61JmTIQ1BJ+MQjtz/ganV/6nFej74JjXZRARERER0QRSu5Oe/nvduL06F2entEOwhberGF3bOIILItOb6C2KCs2rLGjmyW0+b4EQEQEAGt+zj0/Id2fWu1wUsvBYZxLBGghO57R+ZLQjJ/67KDLo17thiVzpjnX3xq1wZXQET3fFcUZwDoM9lEKo10Hb80WEDnC6NBFVH0PTRERERERERFW24dvr4BTtqXf0C4HjgemGk20EpVRJgLrSXDgYlD1wxJiTiz7unn68YQT3tqVLJmsoNf1JG5ORAritOYsvLkigRa+h19FcCYW4vgcJbe+4SS5+J4ICwSVRaGdXfnqaHzlFBy/9zvPI9me9LoWIiIiIiMY4+NJ+z44tL9AhT6v89yQTOaS1wxU/zqmcFTRxe/M0vxO5CrG1FvR06XdfH7dAiIhKRHY6iK21AHtmPbwzghYe70wgpPk/OJ2U+zEihjAihjAgt8IR5vid6uiNO6I5+Hx7Eve0ZRDQSn+vMxkSouUUmldaiG0ovTAI4HRpIqoehqaJiIiIiIiIqujIpkPY/dKHXpcxI/pNQcgxYc9qBqYVFIblhzC12glcfrIxh7tbMyXbyhGYHu30oIWnu4ZxVcMI5lNLOSOPYlD2wEVtBcaFIRB8LAJ5/vwMTucGcnjpd56HXait3xsRERERUT0rZoo4utKjMLEAjNtDFT+MgkJc7gGEGr2xqhqkgwfaU9NeharxbRvBY/4PDBIRnUrooIuW10yIwszedBcGbSzpTCCiOVPv7LEh+SGG9J1QNTAdey7OCxfwzMJhnB+ZIBiOafa8lUL4QxttLxQRPMLp0kTkLYamiYiIiIiIiKrEdVys+dNVXpcxI9pZEsat1VkqdzJJbR/yWtzTGmbi+qYc7mwpDXiXOzD9EUMD7mrN4LGOJBql/08klEtBS6BP74YFb5dWnimhCwQeiUBebHhdiif6N/dh5X9aATWT8TNERERERFQxO3/dA6fgzXdJeYkBrVOe+O9KfU/IasdgaqUXNVczlaVB4cG2FBrkyYDYqXoE4Z02Ih/On+/3RFTfAgMKrctNaNmZhYo7AzaeXJBAg9/7naK+e1wB4eLu1jQe6UghKmc/XVqmXbSssND0lg3NKr2tvp9BIvIrhqaJiIiIiIiIqmT7z7ZiqGfQ6zKmLyIQeCgCMWoUUrXDjhntKDLyaFWPOXsKN8ey45bbrVRgerSzwyaeWTiMSyN5zJdWsy3y6Ne7URApr0uZESEFAg+HIa+Yn8Hpnb/qwQf/9J7XZRARERERzXtKKWx9rtubg2uAcVvpBdqVWNHKRhFJ7UDZH3cmbm/O4sxQaUJssh81cNRB49ulq/PMj2/4RFTP9LRC6zIT+vDMgtNthoMnOxOI+T04/ZE6e8M+PWDit7ri+HjDxEMrpvWx7SpEemy0vWgi0Mfp0kTkHwxNExEREREREVVBMVXAxu9s8LqMGQk+GIbWVNo6qMRJzMnkRRwJbW/pRt82nxVui2VxcyxXurUKgemPhDSF+9vTeKg9hbBW30tCfsQVNgbkNmRFn9elzIjQBIIPRKBfHfC6FE9s+PO1OLB2n9dlEBERERHNa8fePYrhnUOeHFteYUBrreyUaQWFhOyFEs7ojVV1YbiAa5tGprWvTLmIrbFKhpYqMExGRPVB5oGWV0wEjswsAN1iOFiyII4W3Z56Zy/V0Ru2BoVbY1k8uSCBFqP09zWj6dJJFy2vmGh8x4YY82v3bYufiOYNhqaJiIiIiIiIqmDT32xEPj7xVAY/0m8IQJ7r3SRcE1kMyZ2lzWbfNp8VPt2cxQ2x0hOh1QxMj3ZRpIjfXjiM88KF6h/cC0Ihru9BQtsLVWMt98A9YejXz7/gtHIVlv/bl5DYl/C6FCIiIiKiecuzKdMSMG4JlWyqxAXaeTGMvBYv3VjF7+htuo3PtaVLtk0WNhNFheaVFrRRA6l92wIhIpolzQaaV1oI7ZlZcDqmu3hyQQLtho+D03Xyht2m23iqK44bYzloE/xM054uvdVG21ITgcGJP/jq5OkiohrG0DQRERERERFRhcV3D6P7hx94Xca0aWdIGHeEpt6xQmwUMaj3QIlR05J9e7ZQ4bMtGVzT5I/A9Eei0sUjHSnc05pCQMyPqdMZeRSDsgcufHwCZQKBO8PQbwlOvWOdKaaLWPrMr1FMF70uhYiIiIhoXhkZymHlH63Azud7PDm+flUAWuxkTKESU6Zd2IjL3rI/7nQZwsXDHUkEtZM/26R9AlchtsaCnil9HnzZAiEimiOhgNgbFqJb7BmNLW6QLpZ0xrHAsKbemWZB4eqGEXylaxhdgdn3VvW4i9aXTTS+b2NsS7q2Rl0QUb1jaJqIiIiIiIiogpRSWPfN1XDtGgmuhgUCiyMQo0ZJVOIE5mRc2BjUe+AIs/QGH54tFFD4XGsan2gsnSDudWB6tCsbCnhm4TDODJpT71wHCloCfXo3LNTOVHcACNwWgvEp7y5U8EqiN45XvvYyXKdG3h+JiIiIiGqYYzp4//++ix/e/j1s+8kWb9JLBmDcVHrRaCWmTCfkPrhizNjmqlG4pzWNdqN0kuqEP6ZSaNxkI9jH70RENL80fGCj8S0bcKf/Bh2RCk8sSGBhgMHpcmqQDh7rSOIzrRkYY1KE0z4t4ChEP7DQ+pIJI87p0kTkfwxNExEREREREVXQ/lV7cWDtfq/LmLbg/WFoTaXtgkqcwJyIgsKQ/BCWyFXleHMhoHBfWxpXNBRKtvspMP2RmO5iSWcCn2rOQM6DmR62yKNf70ZBpLwuZUaMG4MwPjv/gtP7V+3Fxu9u8LoMIiIiIqK6tn/1Xjx39w+x/ltrYGa8u6hWvyYI0VDZKdMFkUJO6y/dWMXv6Vc35HFJdHor6oR3OojscqbekYioDkV2OWhebQHW9D8LQprC452JeTMgotIuihTwTNcwzg5P/HxOp8+tD7poW2qiYYsDMeZXWf+daCKqVQxNExEREREREVWIYzpY9801Hlcxffp1AcgLDE+OraCQ0HpR0BKeHH8mNCg81J7CpVH/B6Y/IgRwbdMIfmvh8LxYxtIVNgbkNmRFn9elzIhxTRCB+8LzbvTKu3//Nj58YYfXZRARERER1Z3E3jhe+Mq/4IWv/AqJ3ri3xQQB44bKTplWcBGXe8r6mDNxesDEp1oy09o3cMRB4zt2yTaGy4hovgkedtG6woQoTP8dMKgpPNqRwMdC07tAhcYLChf3t6XwUHsKYVn63E/7eiZboeFdC63LTegpTpcmotrC0DQRERERERFRhWz+wftI7vN/CBgAtNMkjE95N+U2ox1FVvo/4CqhsLgjiQsjpU35uQSmZcad0YmBuWg3HDzVFcdNTVlo9X46VijE9T1IaHuhauhn1T8eQOCh8Lzr2r3+h69iYGv/1DsSEREREdGUiuki1n9rDZ696/9h/+p9XpcDADCuD0KET35xrsSU6ZR2CLbIl/1xpyOiuXioPQU5qjcw2Y8oUy5ia62SiZwKDJcR0fxkDCm0LjMh0+7076MBj3QkcV6YwemZWhQ08czC4XEDQT4ynR630eei7UUT0e2cLk1EtWmenX4hIiIiIiIiqo7cYA5v/+1Gr8uYnhAQWByBkJU9eTmZETGEpDbmJK4Pu6u6UL9pxpcuVzjrwLSj0LTeRPuvTHT+rIimDTObqjJbUgC3NOfwpQVxtOr21HeocRl5FIOyBy5q52fVLw0g8PkIIL2upHrsgo2lX30eucGc16UQEREREdUs13Gx7adb8cM7vof3/++7cK3pB9AqKiKgX1vZKdMmckhrh8v6mNMloPBgexKNeunzPdGPKIoKzSstaKMWgWJgmojmOz2jjk8sHpz+55YugIfbk7gwPHH4l0pJKHyqOYMnFiTQNObzarqnAoSl0PiWhdZXTegZTpcmotrF0DQRERERERFRBWz8zgaYWXPqHX0gcF8EWnNpi6DcJy8nUxQZDMtdpd1UH54tNISLRzsSODs8/nc6m6dKFBVaVpgI7z3ZoA73umh/vojQHnsG6yDO3sKgjd/qGsYnG0fgy5R6GRW0BPr1LbBROydR9AsNBB+LALrXlVRP9lgGL//ei7CLtRNwJyIiIiLyiyPvHMbPHngOK/+/ryI/NOJ1OSWMG4MQwcpdqK2gEJd7MG50c5XcEstiUciaekdXoXmNNS5o5rMWCBGRJ7QC0LrCROCQM+37SAE82J7CpRFvVhmoFZ2Gha90DePapon/fTCd/nbgiIO2F4qIfDj93w8RkV8xNE1ERERERERUZv1b+7D951u9LmNa9E8GoF9keHJsGwUMyh4oMWqyhQ8D00Hh4rHOJM6azgnQadCyxyenBAbGn8HVikDsDRstr5qQqcpPBDM04M6WDJ7oTKBJ1nfD2xIj6NM3oyBSXpcybfIcA8EnokDA60qq59i7R7Dmv66s6rR7IiIiIqJaljmaxvLffwm/fOSnGNjW73U544hGAf2TpV9qyn2hdlY7BlPLjDlwWQ8xqfPDBdwYm0ZIXSk0brIR6PPJ9G8iIh8SNtC82kJ41/QvqNcEcF9bGldG/XXBkB8IKFzXmMOXu+LoCJT2fqc9Xbqo0PSGhZbXLUguEEdEdYKhaSIiIiIiIqIyUkph7X9bVRODe0WXBuPOkCfHdmFjQN8OV4wJIvssMB3SXDzemcAZwfIEpvWEi9blReipU79AAv0KbS+aiG62AKfyL6ZFIQtPLxzGZdE8auLFO0uusDEgtyEr+rwuZdrkIh3BJ6OAN3+qntj+063Y8sMPvC6DiIiIiMjX7IKFTX/zJn50x/ex68WdXpczKf3mIIReuS/7NopIagcq9vin0qzbuLctXbJtshBaeKeDyK76vliZiKgchAKaNtqIfmBNO9krBHBPW+Y3K+oRAMSkgyWdCdzRkoWc4GN4OtcvBQ8eny4d3sPPLyKqL/NogU8iIiIiIiKiytv14k4ce++o12VMLQAEF0dKTlwqpco+7WkiCi4G5Q7Ywt/LJoZ/E5heEJj+ZJNTMfocNK+yoE0zfy1coKHbQWifi/T1OqyFsix1TCakKdzXlsYF4SJeiTdhxK3Ta+2FQlzfA8sZQbN7NoTfkvoTkKfrCH2xAYUf54CR+g21j7b2z1aj9fx2nHnTWV6XQkRERETkK0op7Fm2C+v/fC0yh9NT38FDollA/3jlls5RUEjIXijhjN5YlQuydaGwuD2FkHbyO5pSE4fQAkccNL5T2lvw4UJbRES+0rDFgRwB0jfox8dJT8OdLRnoQuGtdLTC1fmZwuXRAu5sySCoza6PKAoKTZsshPaPXx2Bn19EVA/q9OwXERERERERUfVZIyY2fHud12VMS+DeMLTW0hBudQLTCnG5B0UtVfFjzUVUc7CkM162wHRwv4OW16YfmB5NTyu0rrDQtMGEKFQ+MHtBpIhnFg7hgnCh4sfyUkYexaDsgYvy/I4rTeuSCD0VhWicH6cllKOw7F+/iNTBpNelEBERERH5xmDPAP7l8Z9h2b9e6vvANAAYt4YgJhpvWSZ5MYy8Fi/dWJWvTAqfbUmjc0zPYKK2iky6iK21IEZ9nWfgjIhoesJ7jg+hENb0e6K3N2dxSyyLel5NbzJhzcXD7Snc25YeF5ie1tBupRDc56D9+eKEgWmAn19EVB8YmiYiIiIiIiIqk3f/4W1kj2W8LmNK+lUB6JdWbtLTqaS1w8hpA54ce7oapYMnFyTQESjPsoPhHfbxE6QT95mn/zi97vGG9W572ktTzlZUKizuSOHe1hSCcy3cxwpaAv36FtiojYC41i4RfCoKEZsfpycKyQKW/vbzMHOm16UQEREREXlqZHgEK//za/jJvf+MI28d9rqcaRHtGuTlRsUe34WNuOyt2OOfypXRPC5vmPp7pCiocStOMTBNRDQzwSMuWl4xoeWn3w+9KZbDp5rnV3D6nFARzywcxoWR4oS3TzUvRRtRiK220LzOgjbmIebPs0hE8wVD00RERERERERlkD6Uwnv/512vy5iS6NRg3BXy5Ng5MYiUPODJsacr9pvAdKtRhsC0Umh410LT23bZTohqRSD2po2WV03IVOXDzJc3FPD0wmEsCk7cbK8HlhhBn74ZBeHv6ecf0Vokgk81QLTMj7be8IdDWPHvl0O5PD1DRERERPOPYzn44Pvv4Ue3fw/bnuuuqX8XG7eFKrqiVULugyvGpJGroCtg4TOt07hg3lFoXmtBz5QWxsA0EdHMGXGF1mUz64de2zSCu1oyqPfIryEU7mpJ49HOJBpk6fMz3enSod022p4vInSI06WJaH6YH2dXiIiIiIiIiCpsw7fXwinaU+/opQAQ/HwEQj/Z5lQVnlj8kYJIYVjuKt3os351i25jyYI4mvUyBKYdhaYNFqLbSx+rXD9yoF+h7UUT0c0W4FT2iYzpLp5YkMSdLWnowme/tDJxhY0BuQ1Z0ed1KdOixTSEvhyF6Jgfrb3eV3dj09+86XUZRERERERVdWDdfvz47h9i3TdWo5iurQtZRZcG/eLKTZkuiBRyWv+Yg1bscCeENBcPtycxqq0ycSBNKTRushHoq9+Vm4iIqk1mFVqXmzAGpv/eelVjHp9rTUP4rRFdJgsDFn6raxhXNeYnvH3K6dJZhebXLcTetEtWRQB817onIior3esCiIiIiIiIiGrd4bcOYffLu6be0WOBe8LQ2mTJtkpOffqIhTyG5A5gdODWZ+vRtuk2Hu9MoFGf+wlNYSrE1lgIHhsz2QPl/ZGFCzR0Owjtc5G+Xoe1UE59pzn4ZGMeZ4dMvDQcwzGzcie/PSMU4voeWE4eze7HIPz0Ap2AaNAQ+lIUhR/noObBifhNf70RbRd24PzPXeB1KUREREREFZXYl8D6b63Bvtd7vS5l1gK3V26FKwUXcbmnYo9/qiPf35ZCbEzfYKK2SniHg8juMlyQTUREJbQi0LLCROoWA8VF0+uFXtFQgC4UXhqOwfV5v2+6NCjcGMvhxqYctDE/klJTh6WhFMIfOmh4z4Y2yRyY+nimiIgmNj/G0RARERERERFViOu4WPuNVV6XMSV5pQH98kDVj+vAwqC+Ha4Y0331Ude1w7CwZEG8LIFpbUSh5VWz4oHp0fS0QusKC00bTIhCZWeAtBkOvrQgjltiWWh1Om8kI49gUPbAhc8nxwMQEQ2hLzZAO6OygXm/WPH1ZRjcMeh1GUREREREFVHMFLHh22vx7Gd+UNOBae0MCXle5S60TWmHYIuJJ2pW0k1NOZwbNqfcL3DEQeO7/v8+SURUq4QDxNZYCO+c/nvtJdEiHmpPQdZBP7NVt/HFBXHcHBsfmAamDkzLtIuWV000bRofmK79Z4eIaHoYmiYiIiIiIiKag+0/3YqhHn+H+ES7hsDd4aofV8HFkNwBWxSqfuzpWmBYWNKZQFTOvSUsU+7xJSLj4x+rGhnxcK+L9ueLCO22J1kfuDw0AdwUy+Gprjjajfo8EVzQEujXt8CGf1+7HxEhgeCSKLSP1X9w2s7beOm3f418fMTrUoiIiIiIyka5Ctt/vhU/uuN7eO9/vwPXqu2VZIw7Kjdl2kQOae1wxR5/MmeHirg5lptyP5l0EVtrjVtoi4iIyksAaNpko+E9a9p90AsiRXy+Iwld1Oo7s8InGkbwW13DOC04i56sqxDZbqPtRROB/omfAx/NOSEiqiiGpomIiIiIiIhmqZAq4M3vbPC6jFMzgODnIxDGyZanqmCg9sQxoDAsd6OopSt+rNk6LWDiiQUJhMsQmDYGjwemZdbbprtWBGJv2mh51YRMVvZEe1fAxle6hnFtYw6iDk8DW2IEffpmFETK61KmJAICwcei0M7VvS6l4tKH01j2r5bCsbjUNRERERHVvmPvHcVPH3wWr//HVzEyWPsXB2pn65CLKvO9REEhLveg2onkJunggbZUyeTOidoqoqDQvNKCZpWWxwAaEVHlRLc5aNpgAc70PhDOCZv4QkcCAVFbFyhFNQdf6Ejis60ZGGOSftNp9cvE8d5147s2xJiWWv11dYmIpsbQNBEREREREdEsbfqbjSgkqr8k7EwEPhuG1lE6gVZMtUZfGaS0gxjR/DuB+8ygicc6kwhpc28LBw45aHnVhFYsQ2FlEuhXaFtqIvrB9E8azIYugE+1ZPFEZwIxWX8hVlfYGJDbkBV9XpcyJWEIBB+NQF5U/8Hpw28dwrpvrPa6DCIiIiKiWcv2ZfDqv1uGny/+MQa29HtdTtkYtwcr9thZ7RhMLVO6scLtDQmFh9uTJRdbKwWMa6s4Cs1rTOhjLqRmYJqIqPLCe100v25BmNPrgS4KWXi0M4lgjQSnLwgX8MzCYZwbNie8/ZStflch2m2j7SUTxhCnSxMRfYShaSIiIiIiIqJZiO8expYffuB1GackLzegfzxQ9eNmRT/S8lDVjztdi4JFPNqRQLAMgenwLhvNq61xEzr8QLhAwxbn+JKLxypb4FkhC08vHMYV0Tzqbj6JUIjre5DQ9kH5/GcTUiCwOAJ5meF1KRW35Z83Y+uPu70ug4iIiIhoRuyCjbf/7i388PbvYeeve7wup6zkBTrk6ZW5iNNGEUntQEUe+1Q+3ZLBwqBdsm1cOE0pNG6yEej39/dFIqJ6Fuxz0fKKCS03vffiM4IWnliQQEjzb3A6KFzc25rC4o4UImNWSpzOdGl92EXrSyYaNtsYmw/nJxYRzXcMTRMRERERERHNkFIKa/9sNVzbv01V0aYhcE+46sctiOTx5XJH81EX9pxQEV/oTI5bxnDGlEJ0s4WmjXbJysB+pKcVWlZYaFpvQhQqV2xQU/hcWxqPdCQR1XyYIp+jjDyCQdkDF/bUO3tIaAKBB8OQn6j/4PSa/7oSR9457HUZRERERERTUkphz/Jd+OdPfx8bv7sBdt7f3ytmTADG7aGKPLSCQlzugRp9tXIVvodfFs3jqsapVxeL7HAQ2V1/34GJiGqNkVBoXV6ETEyvZ98VsLGkM4GID/uYZwZNPL1wGJc3FCa8/ZTTpR2FhvcttL5swkhwujQR0UQYmiYiIiIiIiKaoX2r9uLguv1elzE5HQgujkAETrY/1XTGT8yRhREMyh0oSREr+KYLe364gM93JKHPtR5XoXGjjYbu0oa6z7PTCO910f58EaHd9vTGkczSeWETzywcxoXhiZv6taygJdCvb4ENf/9sQggE741Av6b6k+arybVcvPy7LyB9JO11KUREREREkxrcMYhfPfFzvPx7LyJ9uD7/7SovMaB1yhP/Xc4eRF4Mo6AlSjdWuM/QaVj4bMvUv6vAYQcN79ZZAJ6IqIbJHND6igmjb3rB6c6AjScXJNAo/RGcllC4vTmDJZ0JxPTSn2E6H63GgIu2pSaiW51xgz783rsmIqomhqaJiIiIiIiIZsAxHaz7s9Vel3FKxl0haAtkyTZxyvETc+fAxIC+vXTyE+CbwPRFkQIebk9BzrUeS6F5tTVuipSPsuGnpBWB2Js2Wl41IZOVm5QekQoPd6RwX1sKwbHrP9Y4S4ygT9+Mgkh5XcqUAp8NQ78p6HUZFZUfzuOlrz4PK295XQoRERERUYl8Io/V/+V1/ORzP8LhjYe8LqdyNMC4rfR7R7l6EC5sxGVvWR5ruoLCxcPtqZIVqiYKqsmki9g6a9x140RE5C3NBFpeMxHcP70gdJvh4MkFCcQ8Dk53GBa+3BXH9U0jE06SPuVHq6XQ8LaFluUm9BSnSxMRTYWhaSIiIiIiIqIZ2Pz995Dan/S6jEnJSwwYV1U3JOnCwaDcAUcUq3rc6bosmscDbSloc+wMi4JCywoTwcNjpnyg9prOgX51fOrIBxbgVO607mXRAp5ZOIyPhfz52pgtV9gYkNuQFf1elzKlwB0hGLfXd3B6cPsAXv+Pr1Rloj4RERER0VRc20X3/3sfP7zte9jyz5uh3Pr+d6q8woDWWpkp0wm5D64YdYFkxZ9KhXvb0mgxSoNzY4NqoqDQvNKCNqa0WusNEBHVK+ECzWstRLZPbzWAZt3BkwviaNG9WD1A4ZrGHL7cFUdnYObHN445aHvRRHSHM+5zqL7/BUJENHu61wUQERERERER1YrcQA5v/91bXpcxKdGiIXBvuKrHVFAYlrtgapmqHne6royO4O7WTMkJTqWmmMwxAS3jouV1C3q6tNVcyydFhQs0bHEQ2ucic4MOc6Gc+k6z0KS7eLwzifcyYaxJNsJStfqMjSEU4vpuWM4Imt2PQfj4lWDcHAIMAeu1gtelVMyupR+i7aIOXPtvr/e6FCIiIiKaxw5uOIB131iF4V3DXpdSHRIwbgmVbCrXlOmCSCGnjblQtcJfu65rHMEFkSku+nUUmteY0LOl/QH/fiMkIpq/Gt+1oeUUstfoUzaEm3QXTy5I4KcDLRiyqhOna5IO7m1LYVFo/ApqU/WwhanQ8J6NyK7JJ2Tzs4mIaGIMTRMRERERERFN05vfWQ8za3pdxsQkEPx8BCJ4shWqlCrbycrJJLX9yGv+PBl8VcMI7motDXPPJjCtD7toft2EnCBvWg+NZz2j0LLCQv4cB5lrDKhQZX6qqxvzODtk4qXhJhw1AxU5hhcy8ggsMYJ250JoPm61GdcFIQwBc3m+bsfMbPzuBrRf1IFz7jzX61KIiIiIaJ5JHkhi/bfWYO+KPV6XUlX6VQFosZOLW5erD6HgIi6r+1yeFTRxW3P21DsphaZNNgL9dfqlioioDkV3OJAjCqlbDECe+jOqQbpY0hnHzwZa0G8ZFaxK4dJIAZ9pzSCkTfyZcqqP08BhB00bLciRCpVHRFTntKl3ISIiIiIiIqL+LX3o+cU2r8uYlHFnCFpX6aTgSgemM9oxZOSRih5jtq5tzJUlMB046qDllYkD0/UmvNdF+/NFhHbbx5+sCmg1HHxxQQK3xjLQ6ii5W9AS6Ne3wIa/Xyj6VQEEHgjXR9p/Igp49WsvY3jXkNeVEBEREdE8YWZNvPEX6/DsnT+Yd4FpGIBxU7BkU7n6ECntEGyRL8tjTUeDdPBgewramFWqxor0OAjvnnyiJxER+VPogIuW10yI4tT9yIhUeGJBAqcFKjM8JaS5eLAthfvb0+MC01O1ZEVRoWmDiZaV4wPT9dNpJSKqPIamiYiIiIiIiKaglMLa/7bKt51HeZEO45rg1DuWUV4kkNB6Szf65Pm5sSmLT7WUToeaTWA61Oug+XULml3G4nxOKwKxN+3jQfGkW5ljCODG2Ai+3BVHhzF+6claZYkR9OmbURApr0s5Jf3yAAKLI3XbFTSzJpZ+9XkUUv4OsBMRERFRbVOuwo5/2Y4f3fE9vPv3b8Mx51+QVr8mCNFQOmW6HEzkkNYOl+WxpkODwkPtKUTlye/AE/UQAocdNLw3jxoERER1JtCv0LrchJad+vMqpCk81pnEmcHyBqc/Firima5hXBwtTnj7qfrXwQMO2p4vItw7cc+2XmckEBFVQp2eHiEiIiIiIiIqnw9f2Ilj7x/1uowJiWaBwH2Rqh7TRA5DcmdpJ1bBB51ZhVtiWdzanCvdOtPAtFKIbLMR22BB+CQIXm2BAYW2pSaiH1iAXZknYUHAxpe74riuMQfhl8T9HLnCxoDchqzo97qUU9IvNhB8NALoXldSGan9SSz/N0vh2pUJ/hMRERHR/Nb3wTH8fPGPseLry5EbyE19h3oUBIwbyj9lWkEhLveg5Mt4hb8u3tGcxRnB0gt6x/4oMuEits6qZllERFQBekqhdVkRenzqnlFQU3i0I4GzQxMHnGd0XKHwmZY0Hu9MolEvPfZU1xxpeYXYGhPNa6xxqyHys4iIaHYYmiYiIiIiIiI6BWvExBvfXut1GRPTgMDDEYjQybN55ZrsNBkbRQzq26HEqClaPglM39GcxU2xOQamXYXGd2w0cnoUhAs0bHHQ9qKJwNHKTE3TBXBHSxZLOhNo1uvkORcKcX03Eto+KB+fupDnGQg+HgUMryupjIPrD2DDn/v0vZuIiIiIalK2P4sVX1+Gnz30HPo+OOZ1OZ4yrg9ChMvfi8hqx2BqmdKNFew3XBQp4JqmkVPuIwoKzassaKNy1b5ogxAR0azIPNDyyvT6nYYGfL4jifPCs1/RbIFh4Stdw7i6MT/h7ZP2rpVCaK+DtheKCB3gdGkionJiaJqIiIiIiIjoFN79+7eR7ct6XcaEjE+HIE8vHRVbjslOk3HhYFDvgSPGLEvoeXdW4TMtGVw35kTnjAPTjkJsnYXIjtKGuX9jr9WhZxRaXrPQtN6EyFfm2TgzZOHprjg+3jCCennGM/IIBmUPXPg3DC4/piO4JAoEp963Fn3wvffQ88ttXpdBRERERDXOLth4539two9u/x52/EuP1+V4LyKgX1v+KdM2ikhqB+b8ONPVptv4XGu6ZNu47Lej0LzGhJ4tvcHzNggREc2JZgHNr1sI9U4dnNYF8HB7ChdFZhacFlC4sSmLp7riaDfG9Junmi6dO37BTmy9BW3MoOv66JwSEXmrThfhJCIiIiIiIpq79KEU3vvHd7wuY0LyfB3GddVLOiooDMmdsIS/lh4WUPhsawYfbyid1DHTwLQwFZpXmQj0l7adOT3qpPBeF8HDRWQ+qaNwnpxhIn1qAU3h7tYMzg8XsTzehKwjy/r4XihoCfSLLeiwL4GOkNflTEieqSP0xQYUfpwDKhSK99Kq//QaWs5pxcKrTvO6FCIiIiKqMUop7F2xB+u/tQapgymvy/EN48YgRLB0yvRcQ9MKCnG5p2qrWgWEi4c7kghoJ78DjesjKIWmt+xxfQIiIqoPQgGxDRa0EYWRy07d65QCeKAtBV0obMuFp3zsZt3GfW1pnBG0Jrz9VNOlw7sdNLxrl6xwUHLfKY9ORERT4aRpIiIiIiIiokms//O1cIpTT5uoNhETCDxQ2pwt11K4k0loe1HQEhU9xkwJKNzblp5zYFrLKbQuZ2B6OjQTiL1po+UVEzI58bKQc3Vu2MQzXcO4eIbTW/zKEiPo07tREP4NWWgLJUJfigLR+nvFO6aDl37nBWT7MlPvTERERET0G8O7hvDrJ3+Bl37nBQamRxGNAvonA6XbynBB7YgYGt9zqNjXE4V7WtPjpn6O/TEiPQ7Ce/zXEyIiovJqfN9G4yYbcE/dX9cEcF9b+jcr5U1G4croCJ7uik8amJ708TMuml+z0LRxfGCal+8QEZUXQ9NEREREREREEzi88SD2LNvldRnjaUDg4QhE+ORX+nJMdTqVjHYUWXmsYo8/GxoUHmhL4bJoabB2poFpmXDRuqwIPTm+9Vx/8dHyCQwotC01EX3fAuzyt+3DUuHB9hQeaEsipFUmnF1NrrAwILchK/q9LmVSWqdE6KkoRFP9vfJHBnN46XdegF2Y2ckqIiIiIpp/Csk81vzXlXju7h/i0BsHvS7Hd/SbgxB6eb8zuLCRkHvL+pin8snGPC6OFk+5T+Cwg4b37CpVREREXot86CC2Znp9zrtbM7imcfxqjBHNwSMdSdzTlilZyQA43rOelFII77DR/qKJ4LGJ+6D1160jIvIWQ9NEREREREREY7i2i7XfWO11GRMybg9BnqGXbKtkYHpEDCOhjTl56fFoCwmFh9pT405yzjQwbfS7aH3FhDzVcBCalHCBhq0O2l40EThamelbl0SLeKZrGOeETn1CuyYIhbi+GwltH5TXf0ST0Nokgk81QLTUX8uwv7sPK//otYpP5SciIiKi2uTaLrp/9AF+eNv30P3DD6Ac/rtxLNEsoH88MPWOM5SQ++CKURc4VvCpPz1g4o7mU69CIxMuYussiFF18NVARFT/QodctKwwIQpTv+t/uiWLG5qyJ/77vHABzywcxnlhc8L9J+tZy5SLlldMNL1tQ4y5VoefPURElaNPvQsRERERERHR/LLtp1swtGPQ6zLG0c7VYdwYrNrxTGQxLD8sHWWh4OloC10oPNyexLljGtAzDUwH9zuIrbcgan+Isef0jELLaxby5zjIfNKACpf3BdKou3i0M4kPMmGsSjbAUrUd6M3II7BFHm3OBdB82JrTmjUEn4qi+GwOari+/kB2/roHHZd04KrfucbrUoiIiIjIRw69cRBrv7EKwx8OeV2Krxm3hiBkeb/vFUQKOW3MijwV6jlENAcPtacw+kcY20sQBYXmVRa0MRluTvgkIpofAoMKrctNJO804DSeugd5W3MOQU0hrLm4sqEw7vZT9qtdhch2Bw2b7Un70/zsISKqnNo+y0RERERERERUZoVUARu/+4bXZYwjGgWCD4ZLtlVyYqqNAgb07VCju7Yenyk0hMIjHeMD08DMAtPhHTZiaxmYLrfwXhftzxcR3mVPsebk7HyiMY+nu+I4IzjxxJZaktfi6Ne3wMb4Eyp+oDVqCD0VhVhQf63DDd9ehwNr93ldBhERERH5QOpgEi/97gv41ZKfMzA9BdGmQV5mlPUxFVzE5Z6yPuZkBBQebE+hUS9tBJT0EhyF5tUm9Kwac18iIppP9LRCyzIT+jSGCVzfNDJhYBqYvF+tJ1y0LjPR+P74wDSnSxMRVYen42x27dqFFStW4MCBA0gmk/jKV76Cm2666cTtSiksXboU69atw8jICM4++2wsWbIEp59++ol9crkcfvrTn6K7uxsAcOWVV+KJJ55AJBI5sc/hw4fxk5/8BPv27UM0GsWtt96K++67r6LLFxMREREREVFt2vTXb6KQyHtdRikBBB6OQEROBhiVUhX7XuvCxqDeU7o87m/q8EpAuPhCRxJnhqypd56MUmh430Z0m1O+wqiEZgJNG22Eeh2kbzDgNJc3dNtiOHiyM4FNmQjWJxvg1PDpa0uMoE/vRodzMYKqyetyxhFRDaEvNqD4kxzco/XzN6NchWX/9iU8/sKTaDmn1etyiIiIiMgDZs7Eu/9rE97/p3fhFOvn37qVZNwWgtDK+/0rpR2CLarTf7k1lsWiU/UTlELTWzYCA4yrERERIAtAyysmUrcZMM+Q077fKadLOwrRrTaiWx1OlyYi8pin42KKxSJOO+00PP744wgEAuNuf+WVV7BixQo88cQT+OM//mM0Njbir/7qr1AonLxK55/+6Z9w4MABfO1rX8PXvvY1HDhwAN/73vdO3J7P5/FXf/VXaGxsxB//8R/j8ccfx6uvvorXXnutKj8jERERERER1Y7hXUPo/uEHXpcxjnFbEPKs0uueKxWYVnAxJHfCEiMVefzZCAoXj3cm5haYdhSaNljjAtM8HVoZgQGFtqUmou9bgF3eZ1mI41NcvtwVR6cxh9eED7jCQr/ciqzon3pnD4iwQPDJKLSzpn9yqBaY6SKW/vbzKKaLXpdCRERERFWklMLOX/XgR3d8H+/8r00MTE+T6NKgX1LeKdMmckhrh8v6mJM5P1zADbFT9zgiPQ7Ce/h6ICKikzQbaF5lIbTbnvZ9Jp0uPeSi9WUTDd3jA9PsTxMRVZ+noenLL78cixcvxtVXXz3uZK9SCitXrsQ999yDq6++GqeffjqefvppFAoFbNq0CQBw7NgxbNu2DU899RTOPfdcnHvuufjSl76ELVu2oK+vDwCwadMmmKaJp59+Gqeffjquvvpq3H333XjttdcquowxERERERER1RalFNb92Woox1/fFbWzdeg3BatyLAWFuOxFQUtW5XjTEdJcPLEggdOC029OjyUsheZVFsJ7SzvSCpzeUUnCBRq2Omh70USgApOKOwM2vtwVxw1NOYhaPr0gFOL6biS0fVA+/DlEUCD4RBTaOZ4uWFd2id44XvmDl+A6Uy+1SkRERES1r6/7GH6x+Cd49d8vQ64/63U5NSVwe6isj3e897AHEGr0xopo1m3c25YuPf6YYwUOOWh4d/Y9ByIiql9CAbE3bUQ32+M/QKbDVmh4z0LrMhNGYuL7sz9NRFR9noamT2VoaAipVAqXXHLJiW2BQAAXXHABent7AQC9vb0IBoM499xzT+xz3nnnIRgMluxz/vnnl0yyvvTSS5FMJjE0NFSln4aIiIiIiIj8bt/KvTi4/oDXZZRqEAg+FC650LiSFwBntCPIaf6ZeBvRHCzpTKArMPuTl1peoeUVE8GjDEx7Rc8otLxmoWm9CZEv7+tXCuC25iy+uCCBFr22T3Jn5BEMyR1w4b+fQxgCwUcjkBfUV3B6/+p9ePM7G7wug4iIiIgqKDeQw4r/sBw/e+A5HHv/qNfl1BztDAl5XnmnTGe1YzC1TOnGCnxB14XC4vYUQtrJ76FKlU4BlQkXsXVWyeH9dykrERF5raHbRuNGG3Cn/ylh9LvHV+Lb5pRcJwTws4aIyGu+PdORSqUAAE1NTSXbm5qakEgkTuzT2NhYcvJYCIHGxsYT90+n02hpaRn3GB/d1tHRMeHxd+/eXZ4fhGoOf/dERESzx89RIqpVjulg/X9d7XUZpQQQfCgCET15vbNSatxKTeUyIoaQlPtLN3qYLG6QDp7oTKDNmP2EYpl20fyaBT07vg3NwHT1hfe6CB4uInu1jvz5cvL1Kmfh9KCF3+oaxupkIz7IhlGrv+G8Fke/2IIO+xLoKO80t7kSukDgkQjM5/Nweiyvyymb9/7hbTgtDk771Blel0JEc8Tvo0RENJpjOtj/673Y8+yHcPLlX/lmvjDuKO/3EhtFJLVqXLCucHdrGp1jLsIe/TVUFBRaVlnQ7NH3qtVvk0REVGmR3Q7kiELyNgMwJv+0EJZCw/s2wjudST9T+FlDVD2Dg0MYZM9oXjr//PMnvc23oWmvnepJo/q1e/du/u6JiIhmiZ+jRFTL3v3fb2PkSM7rMkroNwchP1b6tb1SgemiSGNIfli60cMzhU2/CUy3zCEwrQ+6aFlpQiuWsTCaM80EmjbaCPU6SF9vwGkp3yJoAQ34bGsGF4SLWBZvQsaRZXvsarLECPr0bnQ4FyOomqa+QxUJTSDwcBimATjd9ROc3vqX3bjoxoux4PIur0sholni91EiIvqIUgr7Vu7FG99ci9T+pNfl1DTtbB1yUfniBAoKcbkHSjijN1ak9/DxhjwuixYm38FRaF5tQo65yJohNiIiOpXgERetr5pIfDoAFR7/qRE46qBpoz3u8wXghTlEXunoaEcze0Y0RvnOTJVZLBYDcHwa9GjpdPrEbbFYDJlMpmRpYqUUMpnMiX2ampomfIyPbiMiIiIiIqL5LTeQw9t/u9HrMkpoiySMW4NVOZaNAgblDpSsEehhB7dZt/HkgvicAtOBww5aVzAw7WeBAXV8ecr3LcAu74KUZ4dNPLNwGJdE8qjVxS5dYaFfbkVW9HtdyjhCCATvj0C/OuB1KWXjFG289NUXkBvw18UzRERERDQzw7uG8PxT/4Klz/yagekyMG4vb19iRAyhoCVKN1ag99AVsHBnS2byHZRC01s2AgO1+X2RiIi8ZQwrtC4zIdPuiW3CVGh600LLa9aEgWmAgWkiIj/xbWi6vb0dsVgMPT09J7ZZloXdu3fj3HPPBQCce+65KBaL6O3tPbFPb28visViyT67d++GZZ2cftPT04Pm5ma0t7dX6achIiIiIiIiv3rzf66HlfPRxNSIQPChSMlU6dEXC5eTCxsD+na4YszP71EHt1W3saQzgZjuTr3zJEK7bTSvsiDsqfclbwkFNGx10PaCicDR8i6XHdIUHmhP46H2FMLa7F9PnhIKcX03ktp+KB+GvwP3hKFfXz/B6eyxDF7+vRdgF/nmQURERFRrCqkC1n5jFZ67+4c4uG6/1+XUBXmBDnl6+aZMu7CRkHvL9niTCWsuHm5PQh/V1xjbUolsdxDeU97voERENL/o2ePBaX3QReCQg7bniwjv5mcLEVGt8DQ0XSgUcPDgQRw8eBBKKcTjcRw8eBDDw8MQQuDTn/40Xn31Vbz//vs4cuQIfvCDHyAYDOK6664DACxcuBCXXXYZnn32WfT29qK3txfPPvssrrjiCnR1HV9O89prr0UgEMAPfvADHDlyBO+//z5eeeUVfOYzn6nYssZERERERERUG/q6j6HnF9u8LqNE8MEwROPJr+tKqYp8f1VwMSh3wBb5sj/2bLQbNpYsSKBptoFppRDtthF70y4Zmk3+p2cVWl6z0LTOhJYv7y/vokgRzywcxnnh2h07npaHMSR3wIX/TrwE7gxXbSp+NRx77yjW/MnKil2oQkRERETl5Toutjy7GT+87XvY/P33oRz+O65cjNtCZX28hNxXesF2BX5VAgr3t6XGXYg9uqUSOOSg4T1eKElERHOnFYHW5SZaVlmQY1rs/BcJEZG/iWQy6dl79Ycffojvfve747bfcMMNePrpp6GUwtKlS7Fu3Trkcjmcc845WLJkCU4//fQT++ZyOfzkJz9Bd3c3AODKK6/EkiVLEIlETuxz+PBh/PjHP8a+ffsQjUZx66234v7772domsbZvXs3zj//fK/LICIiqkn8HCWiWqOUwi8W/wTH3j/qdSkn6DcFEbijvCcmJ6KgEJe7kdMGKn6s6VhgWHisM4GInGWLwlVo3GQjsqs0VKrAZQ9rjRsAslfryJ8vS89sl0F3NoSViUaYyrcLr52SoSLosC+Bjsq/R8yUtbEIa2XB6zLK5rY//RQ+/ltXeV0GEc0Av48SEc0/h986hLV/ugpDOwa9LqXuyEsNBB8+ea59rhdzF0QSA3rlL1i/OZbFzbHcpLfrCRcty0xoozLT7BsQERER1bfi4/8K1j2PeV0G+YynoWkiv2FznYiIaPb4OUpEtWbnr3vw6r9b5nUZJ2hnSgS/FIXQKn+6LqUdREoerPhxpmNh4HhgOqTNsj1hK8TWWQgdKp0kxROftc3sFEhfb8BpKW/AOWlreHk4hkPFQFkft1o0ZaDDuRhB1eR1KeNY7xZhvVIfwWkhBR760SM46+ZFXpdCRNPE76NERPNH+nAKG/58LXa/vMvrUuqTBoR+rwFaqyzLwym4OKZ/UPFVrs4JFfGFjuSk196KvELby0XIUZlq9g3IzxSAl9o+gV92XI+PFQbwzLE1OKs47HVZRHQK/Fwh8ieGpmkiutcFEBEREREREVWbmTOx4dvrvC7jpLBA4OFISWB6rpOcJpMTA74JTJ8eMPFoZxLBWQamRUGheZWJwGDp/dmgrn2BAYW2pSZGLpPIXqEDenl+o826iyWdCbyTiWBtsgFOjb1SXGGhX25Fm3M+oqrT63JKGJ8MQhgC5kv5ml+DVDkKy//NUjy+9IuIndXsdTlEREREBMAaMfHuP7yN9/7Pu3CK9tR3oFmRVxglgem59iZS2qGKB6Zj0sH9bamSwLRSoxYvchSa15glgWmAfQPyrz3hBfjaeV/Gq21Xntj2PxY9iMWD7+Brh5fj+vQeD6sjosnwc4WIqHYwNE1ERERERETzznv/8DZy/Vmvyzgh+EAYWlPpRN1KBKYLIoVhubt0o0cJ47OCJh7pSCIwy8C0lnXR8poFPc3AdL0SCohudRDc5yJzvQ7z9PJMOhMCuLZpBGeHinhpOIZ+yyjL41aNUBjWd8FyRhBzF0H46BWvXxkAdMB8IQ+4U+/vZ4VkAUuf+TUe/fWTCDTU5mRyIiIionqglMKuF3diw7fXIXss43U59U0Cxi2hkk1z6U2YyCGtHZ5rVackofBQexJhebI3UBKYVgpNGy0EBmr8yk6aF/Kagb846wF856z7UNRKv4c6QuIXndfjF53X49r0Hvy7Q8uxeOht6KrGv3wTEREReaC8a5wSERERERER+VzqYBLv/eM7Xpdxgn59APL8yoc2LYxgSO44nkT9iEcJ47NDRXyhIzHrwLQed9G6zBwXmAYYmK5Helah5XULTetMaPnynejuCDh4qiuOm5qy0GpwNHJaHsaQ3AEXjtellNAvDSDwSAQoT8bdU8O7hrHi68ug3Np7fRARERHVg/6tffjFIz/FK3/wMgPTVaBfFYAWOxkfUGr2/w5WUIjLPeN7EGV2Z0sGC4Olk8dH57wj2x2EexkqJf97qe0TuOKa/4lvfWzxuMD0WG83nYcll/4+zr/ur/DdM+9FUo9UqUoiIiKi+sDQNBEREREREc0rG/58LZyiP0KG2ukSxh2hqXecIwcWBvUeuGLMEsYeJIzPCxfx+Y4kjFl2JALHHLS8YkJWdnVf8qHwPhdtzxcR3mUfHx1WBlIAtzTn8MUFcbTqtbfEd16Lo1/vho2C16WU0C8wEHwsAtTYEO+J9L66B2/99Ztel0FEREQ0r+QGc3j9D1/FT+9/FsfePeJ1OfODARg3BUs2zWXKdFY7BlMbE3Qvcw/i8mgen2icvDkQPOig4b3a+55H88u+UAceuuzreOjy/4B94c4Z3fdQqB1/dO4SLLrh7/AH538Zu8MLKlQlERERUX1haJqIiIiIiIjmjUNvHsSe5bu9LuO4kEBgcQRCnjxrOJcpTpNRcDEoe2AL70OVF4YLeLg9CX2WJ0pDex00v25Bs8pbF9UOzQSaNtpoWW5CJso3Ley0oI3f6hrG1Q0jqMj4swqyxAj69G4URdrrUkrIcwwEn4gCpx6QVRPe/puN2L1sl9dlEBEREdU9x3Tw/j++gx/d8T1s/9nWWvuneU3TrwlCNJRnyrSNIpLagXKUNalOw8JdLZN/B9LjLprWWyU5bb6cyE8KmoFvLXoIl1/zP/FS+9Xjd5jB32BOhvD3p9+FS679Lh667OtY3XwJX+9EREREp6B7XQARERERERFRNbi2i7XfWOV1GScE7g+XLHsLzG2K00QUFIblrvHTnTxwSSSP+9rS0GbzIyqFyHYHjZwQRb8RGFRoW2pi5DKJ7BU6Zp3EH8XQgM+0ZnBBpICXh2NIO7IMlVaHKyz0y61oc85HVM1sMlUlybN0BJ+MoviTEaBQ26dsV3x9GXpf2Y3WC9rQdkE72i5oQ9OZMWiSMymIiIiIymHfyl6s++YaJPclvC5l/gkCxg2lVzvOtj+hoBCXe6CEM3pjWadMB4WLh9tTJStYKQV8VLLIKzSvMqGNaiGUuQSiOXm15XJ87fyvYE+ka/yNH72YZ/E3qISGl9qvxkvtV+PK7AF87dByPDawEUHFfhoRERHRaAxNExERERER0byw7SdbMLxzyOsyAAD6NQHoFxoVP05KO4ARzfuf+YpoHve0pkvO94w+oXlKSqHhHRvRHc7U+9K8IhQQ3eoguM9F5nod5unlCTkvCll4euEwXk80YlsuhJo5tS4UhvVdsJwRxNxFED6pW56uI/SlKArP5YCR2g1O23kbH76wo2SbDOpoPa8VbRe0l4apz4hBzOoKESIiIqL5J75nGOu+uQYH1uzzupR5y7g+CBEunTI929D0iBhCQRsTfC/rP40V7mtLocUo7RGcKNdRaF5tQuYqWALRLB0KtuL/c96X8KuOayffqUwDFbobFuHpi38P/+ncx/F7R17H7x19HR2W90MViIiIiPyAoWkiIiIiIiKqe4VkHhv/8g2vywAAaAsljDtDFT9OVvQhLQ9X/DhT+UTDCD7bWnpSZtqBaUchtsFCaL9ben/whCedpGcVWl63kD/bQfYaA2547q+OkKZwX1saF4SLeCXehBG3dqYJp+VhWGIEbc6F0OCPadnaAonQU1EUn8tBZWo3OD2WU7QxuH0Ag9sHSrbrYR2t57Wh7YI2tF7QjrbzjweqG09vYpiaiIiI6DeKqQI2/c1GdP/wA7i2O/UdqDIiAvq1wZJNsw1Mu7CRkHvLUdWkrm8awfkRc+IblULTRguBwfr5zkH1wRQSf33GPfjWxx7GiJygJzjtRtnM9Qea8Y2zH8H/WPQAnux/A39w+BVclvO+X0hERETkJYamiYiIiIiIqO5t+uuNKCTyXpcBBIHA4giEPHkiZC4TnCZTEEnEZW/pRg+Sxtc05vDplmxpGdM8DyRMhebVFgJ9DEzT9IT3uQgeKSJ7tY78+bIsJxwviBRxenAIr8SbsDtf+YsdyiWvxdEvtqDDvhg6/FG31i4RfKoBxeeyUMn6DjHYeRsDW/sxsLW/ZLsRMdB6ftvxqdTnt58IVTee1lj2zwEiIiIiv3IdFz0/34Y3v7Me+WEffE+f54wbgxDB8vQoEnIfXGGd3FDmL/CLgiZujWUnvT2y3UG4lwF88pdVzZfgD87/CnZGTx9/40dNsip8HyxqAXx/4R34/sI7cGd8K/7g8HLcHd8CDfX9/ZyIiIhoIgxNExERERERUV0b3jWE7h994HUZAIDAvRFoLaUTa8sdlDORw6DcAYhRJz08SBpf35TD7c2zC0xrOYXmlSaMROmJGwamaSqaCTRttBHa4yB9gwGnZe4ToqNS4fMdKWzNFvF6ohFFVRtTpy2RQ5/ejQ7nYgRVk9flAAC0Fu14cPrZHFR8/oUZrBEL/d196O/uK9keaAgcD1P/ZiL1R2Hqhq4GhqmJiIiorhx5+zDW/umqcSt1kDdEo4D+yUDptln++7MgkshppRcNlvMLfKN08EB7EqMXbhndYwge/P+zd9/hcV33mfjfc9s0YAaYAvZOFFKVIkhKtuMq2ZZsq1myJblJiZPdJJbkzSa/7GabHae4rLNxnMTJZp3EjktiJzGpRskiKZEUKVGVoiiqkKBYwII6vc+95/cHQABDDIg2mDszeD/Pw4fDO3fufEliyr3nPd9jouGlQvmekGiWzhpN+L01n8G/LHjXxDvZdL63w38FdvivQEfyDO4/8wQ+d34v3NYEHdyJiIiI6hBD00RERERERFS3pJTY84dPQZr2d03RNhrQ1utz+hwmcujTjkAKc3RjxZPGEu/xJfEeX7J46xQD02rEQvOOHNTk+PsYHaSpMvokAg/nkLpMReIqDdBm/9NzRUMGy505PDbgw8msMfkDqoAl8uhRX0PAbIVHtthdDgBA8Spwft6DzI+TkH3zLzhdSi6Rw/lXzuH8K+eKthteBwJrhztTtwVHfve0eBimJiIiopoSOxPDvj/djbcffsvuUugCBdA/4IQow7mSBROD6rEyFFWaAolbglF41NFrO2OvMWiDFrx780XXDDjpmuySFyr+csmH8dWVn0RCc43fYaoXyCrgTc8S/Hbbr+J/rLoTv352F37rzJNYkgvbXRYRERHRnGNomoiIiIiIiOrWOzu6cGrvSbvLgFigQL/BOafPYcFEn3oEpshe9ORz+rQXkXifL4HrfKnirVMcD9J7LDTtykFhcxsqAyEBz2ETjhMW4tdqyC1RZ31Mn2bh7gVhvBh34elIIwqyOgY6L0lIDGhvI2+m4LNWQFRBdEA0KHB+zoPsT1OwzpmTP2CeysWyOPfyWZx7+WzRdofPOdyVejhMPdyh2h1yM0xNREREVSWfzuOlv30BL33veRQy7AJcFZwC2jUGtE4Dirc8q+jElG4URKYsxyrlg81xLHXki7Zd+NqrpOXQdYQxP14MTJNd9vg68EDrvTjcsGzinarwnG1Qb8Q3VtyCby/7GO7sO4AHu7ejM/6O3WURERERzRmGpomIiIiIiKguFbIF7Pna0zZXAcAAHJ90F3VvklKWNdgmITGgvoWckijbMWdSxfXNcXQ2pou3TjEw7Thpwrc3D8H8JJWZlpBo3pFHZqWJ+GYdlmv2r73OxjRWOXN4eMCH87m57SBfLjG1G3mRQsBsh4LZB8hnS7gVOD7jQfafk7C6+cKfjmw0g3MvnsG5F88UbXc2u4rC1IG2APxtQbgDbpsqJSIiovlKSomjj7yFvX+yG4mzcbvLIQCiWYG2xYB2pQFhlO96RA5JxJTush3vYuvcmXHXGUaYEr6nxq9UVX2RVKp3PboXv7/mHvxo4a/YXcqsFBQNP13wbvx0wbvx7sib+HL3dtzc/xJU2L+CHxEREVE5MTRNREREREREdeng37+E6MmI3WXAuMkFxV8cUCx3J9CI8g7SymBZjzk9Eh9pjmPDDAPTrjcLaDxQ4MAmzSnnCQvGmSwSGzWk29RZd3cK6CY+v2AQ+2Me7I96YNXAT3BaGUSPOIRQYR00zG33+6kQTgHHPR5kf5aEdYLB6dnKhNM4+3w3zj5fHFpxBVwItAbhvyhM7WousVQ0ERER0Sz1Hu7B7q8+Ne47CdlDWa5C2+KA2qZNeC1iphO7JSQG1WNDy/yMbixbajmgFXCjP1b8nBeuM0gJ7/48jD6GOck+BaHgbxZfj/+56k7EtBKTVad6YawK7WvqwL6mDqxK9+JLZ57Afed2w2tOMIGBiIiIqMYwNE1ERERERER1J9mTwPPffc7uMqBu0KFdbszpc8SVc4irZ+f0OS5FQOJGfwxXNhQvxTulcSEp4XmlgIbXGJakylDygPe5ApxdJuLX6Sg0z245akUA7/ElsdaVxcP9PgwUqv9SW14kcV57FSFzHRzSa3c5EIaA4y4Psv+agnWMS7bPhfRAGt0Dp9H93Omi7e6QG/7W4Ehn6guhaqfP/kA9ERER1Z7UQArPfusZHP7nQ2BTUpspgLpeh77FAWXRxKvMXAhLz3Rid0I5h5xyUSfxMuVDDWHh9lAEhjL6wzT2OoP7sAnXcas8T0Y0A8961+L+1vtwsHHlxDvVaGB6rHdcLfjPaz+Hr6y8A/edexr3n3kCqzJ9dpdFRERENCvVP5JDRERERERENE37vrUX+WTe1hpEiwLjw3PbxTMtBhFWuoo3lrGr02QUSHw8EMV6T7a4hKkEpq2hrlCuruJBzgqWT/OY0SfhfziH1GUqEldpgDa7n7qFRgH3LRrA7kgDXoi7Ue0/xZbIo0d9DQGzFR7ZYnc5EJqA4043cr9IwXyTwelKSfWlkOo7he79p4q2e1o88LeNCVO3Dv3u8DpsqpSIiIiqmZkz8eoPX8GB7zyLXCw7+QNo7jgBbYMBbZMDinfyCaKzWQWrgCwiyskZP/7ShiZnB/TiCdYXynWcMtHwMs8byB59eiP+YPVd+IdF77e7lIqKay78xbIb8ZdLP4Jb+l/Eg93b8e7o21V+9YOIiIioNIamiYiIiIiIqK6cf/Uc3vj56/YWoQOO290Q+ujQwUyXu51IDgn0q28VZzMrHJi+JRhFu3v6gWmRl/DtzsNxhoFpso+QgOewCccJC/FrNeSWTNyBbSo0AXyoOYFWVxaPDvgQNWd3vDknJAa0t5E3U/BZKyBsfvUJVcC43Y3cw2mYr9k76WW+S/YmkexN4vQzxSGYhoUNQ2Hq1sBIqNrfGoCjkWFqIiKi+erE0+9gzx8+hXDXoN2lzGuiWYG22YB2lQFhzP33egmJQfUYpDDHbizbCX1nYwrrPKUD+NqgBe/evF2XQmgeMyHw/UUfwH9b/WmE9YbxO0ypg0Dts4SCX4Q24xehzeiMdeGB7sdxR98BGJKryBEREVHtYGiaiIiIiIiI6oa0JHb/r112lwHjRheUYHFgspyB6QKy6NOOzNkA5WRUSNwWimCtK1e0fUqB6bRE884c9IHi9Zo5yEl20RISzTvyyKw0Ed+sw3LN7idxuTOPX100gJ3hRhxKOlHtP9kxtRt5kUbAbIMCe4PeQhEwbnYhrwsUXs5N/gCqqMT5BBLnEzi150TR9obFjQgMd6P2j+lObXgMewolIiKiORc+Pog9X3saJ3Ydt7uUeU1ZrkLb7IDark14zaHcE7gBICX6kVHCxRvL9BRLHTl8oClR8j4lLdG0KwdlTJNpXkugSnihcTXub70XL3rXTLzTPAhMX+xF7xp8fv1v479k78ZvnXkSv3F2J/yFpN1lEREREU2KoWkiIiIiIiKqG29tfQPnXzlnaw3qlTq0K+cuKGahgD7tCExxUaCwQmMzmpD4ZDCCVTMITKsxC0078tDictx9829oiaqN84QF40wWiY0a0m3qrAY8HYrETYEY2twZbB/wImlVd9fptDKAHnEIocJ6aLC3a7AQAsZNLkADCs8zOF0LEmfjSJyN4+TuE0XbG5d6x4ep1/qhuxmmJiIiqlXZWBbP/8WzOPiPL8PKW5M/gMpPAdR1OrQtBtTFEw/1XwhLlzswbaGAsDo3YXmPYuKWQBTqmJJHrjWYEr6nclAvymPyWgLNpUHNg/+++tP4u0UfgBSK3eVUrbMOP/776k/jj1fcis+f34v7zzyOjpS912eJiIiILoWhaSIiIiIiIqoLuWQOz3x9j601iKAC46OuOTu+hES/+hbywp6uLYawcEcoguXO/Lj7JhuH1fotNO/MQcnMUXFEZaDkAe9zBTi7TMSv01Font2g6FpXDr+2aABPDHrxVtpZpirnRl4kcV47iJC5Dg7ptbscGB92AbpAYV/pZbmp+sW7Y4h3x3DiqXdGNwrAu9SHQFsA/rYgAm0BBFqD8Lf6oTl1+4olIiKiS5KWxOs/O4z939qLdH/K7nLmJyegbTCgbXJA8U5+nlLusPQFYfUdWGLMNYEytXoWkLglGEWjVhzGFwKAlPDuz8PoGz8Bm2guWBD4wcL34r+uvgv9Ronz46l0DpiH0qoDf7vkevztkuvx0YGDeLB7O64PH+bkBiIiIqo6DE0TERERERFRXXjxr59Hsqf0Eq4VoQGO290QxuhQQDmXwZWQCCtd45fArRCHsHBnSwRLHeMD05Mxuk007c5DFCbfl6gaGH0S/odzSF2mInGVBmgzfx27VYnbQlEcTmbx5GAjsrJ6u1NZIo8e9TUEzFZ4ZIvd5cD4gBPCAPJPMThdNyQQOx1F7HQU7+wc06FQAL7lTaNh6uEO1c1r/NCcvIRNRERkp7MvnMHur+5C72s9dpcyL4lmBdomA9rVRtH1BjtkRARJ5aKfgzKV9L6mRMkJ2gDgPmzCdZydzakyDjaswJda78VzvraJd2JgelKPB67G44GrcXniNB7o3o57evfDaU3/miIRERHRXOAVZyIiIiIiIqp50VMRvPx3L9hag/ERF5QWtWhbOTs7xZWzSKjny3a86XAqFj4dCmORY/qpZ+exArz7CxBsCEU1RkjAc9iE84SF2LUackvUyR90CZd7MljuyOGxQS9OZBxlqnIOCIkB7W3kzRR81goIm3tC6e92ArpA/pdsU1/XJBA9GUH0ZATHn+wa2SwUAd+K4TB1a3AkVN28uhmag5e2iYiI5lL8XBzP/MluvP3Qm3aXMi8py1RoWxxQ27U56xo9HRZMDKrH5uTYba4MrvWW7mDuOGWi8WXOwKa5F9Hc+MrKO/DXS26AJap3snOtOdywDL/R8Rv4b6s/jd84uxO/eXYHFuaidpdFRERE8xyvLBMREREREVHN2/vHu2FmTdueX71ch7bBmLPjp0Q/Iso7xRvLtATuZFyKhbtawlhgTHOQUkp4XjPR8AoHN6m2qQmJ5h15ZFaaiG/SYbln/sLzahbuaongpbgLT0cakZf2hx8mElO7kRdpBMw2KJhdYHy29M0OCF0g91h66L2P5g1pSUTeCSPyThhdT4yGdIQq0LSyGf7WAAJtQ12p/W0BNK/yQzXs/XklIiKqdYVMHi/97Yt48XsHUEjzfK6iFEBdp0PbYkBdPPEwfjlXtZqqmHIaBVH+iYzNWgEfC8SKtkk51MhXG7Tg3VvcmbZCl0JoHpEAfrzgPfj9NXejx2gqsYNkZ+ky6DN8+OOVt+Nbyz+Bu3qfxYOnt+Oq5Cm7yyIiIqJ5iqFpIiIiIiIiqmmn951C1+NHbXt+4Vdg3Oias+NnRRwD6tvFo4IVGiX0KCbuagkjZEwzkG5JND5fgPut4sdxcJNqmfOEBeNMFomNGtJt6qwGTTc2prHKmcMjA16czc3dhIvZSisD6BGHECqshwZ7u2NrGwxAB3IPpQGuzD3vSVMi3DWIcNdg0XcARVPQtLIJ/rbgSJg60BaEb2UTVJ1haiIiokuRUuLYY29j75/sRrw7NvkDqHwcQ993tc0OKN7JO9xWOjCdQxIx5UzZj6sLiduCUTiU0ZmRF/KpSlqiaVcOypjcPq8pULkd9izFl1rvwzNNHRPvxMB0WeUUHT9c+F78cOF78f7w63iwezs+NnAQCmdIExERUQUxNE1EREREREQ1yypY2P2Hu+wrQAUct7shHKMDKOXs+FRABn3qEUgxJiFYoVHCRtXE3S1h+PVpBqYLEr49eThPF6caObhJ9UDJA97nCnB2mYhfp6PQPPMle/26ic8uCOO5mAfPRD2wqvQVkhdJnNcOImSuh0M22lqLdrkBaAK5X6QA+xYXoCpmFSwMHhvE4LFBHHtsdLuiK2he7UegLQB/62ig2reiCYrGpbeJiIj6jvRiz1efQvdzp+0uZV4RTQLaZge0qw0IozrPByQkBtVjgJBjN5bhBF/iI/4YWi5a1UoIAKaE76kc1GTxI6rzX4hqUVx14qsrP4nvLv0ITMHJlXZ5uvkyPN18GdamzuP+M4/jC+f3oMHM2l0WERERzQMMTRMREREREVHNeu0nr2LgzX7bnl+/wQllYfHgSrkC0xYK6NOOwBLFS9FWYpTQp5q4e8EgmrTptXMVWYmmnTkYfcXdYRiYpnpj9En4H84hdZmKxJUaoM/sJ1wRwLt8SaxxZfHIgBd9eb3MlZaHJfLoUQ8hYLbCI1tsrUXr0CHudCP7rymAq8XTFFl5CwNv9WPgrX4Ab41sVw0VzWv88Lde6EodgL8tCN9yHxSVYWoiIqp/6cEUnv32Phz+ySFIi10+K0VZqkLb4oDaoVW8a/R0JZRzyCnx4o1lKHlDQxqXezLj75AS3v35cdcViMpBAvhZy7X4vTWfwVmHv8QOkp2lbXDMvRAPtt6L/7XyTvzauafwpTNPYFl20O6yiIiIqI4xNE1EREREREQ1KRNJ47lv77Pt+dV1OvROx5wcW8JCn/oG8iI1J8e/lGatgLtbwvBOMzCtJCSad+SgRRmYpvlBSMBz2ITzhIXYFg25pTPvTrXAKOALCwexN9KA5+NuyGp81QiJAe1t5M00fNZyCBtrVNfqcNzlQfZnSSBnWxlUB8ycif43+tD/Rl/RdtWhwb/GD3/bRWHqZT4IpQpfn0RERNNk5k0c+qeDOPB/9iMbY1fPihBD1xG0LQbUJRMP0Zdz9arZKiCLiHKy7MddZORxfXO85H3uwyZcx6d3PYJoKt5wL8YDrV/AU82XT7xTlbz25quI7sG3l38cf77sRnyy73k80P04ro0ds7ssIiIiqkMMTRMREREREVFNeu7/7EcmUqIrUQWIZgXGx11zcuyhpW+7kFWic3L8SwloBdy9IIwGdXoDlNqghaYdOajp8fdxuInqnZqQaN6ZR2alifgmHZZ7Zj/1mgA+0JzAWlcWjw56ESlU52W7mHoaeZFCwGyDAvuWMVZXanDc40H2p0mAOR8qMzNbQN+RXvQd6S3arjk1+NcGhsPUQ0HqQGsA3qUMUxMRUe04uecE9nx1FwaPsYtnRTgAbYMBbZMDim/ylSyqJTA9dG3iGKQwx26c9Um+S7FwazACdcxxLjT3dZw00fgyl5Oh8koqDvzRylvx50tvQl6pzvNsKmYKFT9ruQ4/a7kOW6JH8eXu7bit/wVokhMqiIiIqDz4rZCIiIiIiIhqTv9bfTj0TwfteXIVcNzuhnCMjvCVsxNUTOlGUukpy7GmI6TncVdLGB51ekvg6udMND2Vh5Kfo8KIaoTzhAXjTBaJazSk29UZd6ha5szjVxcOYme4Aa8mXajGqQdpZQA94hBChfXQMDcd96dCXarB+dkGZH6SBNJcvpvmXiFTQO/hHvQeLv6c1t06mtf6h7pStw6HqdsCaFzirZrgExERUeREGHu+9jTe2dFldynzgmgS0DY5oF1tFF0/qBUp0Y+MEi7eOMu/hoDEJwJR+C5a2UoIQBuw4Hum+MICV66i2ZAAfhHchP+89rM47QyW2EGys3QNOOBrxd2+VizP9OO3zzyBXzv3NJoKlV+Zj4iIaktYc+PRwDW4uf8lG69eUzVjaJqIiIiIiIhqipQSe776FKRpT0BO/5ATyqLi7qrlCkQlRR+iavmXvp3MguHAtGuagWnHOyZ8z+Qh2OiFCACg5AHvgQKcx03Er9VR8E/eSa4UQ5G4MRBHmzuL7YNeJEz7OjpPJC+SOK8dRMhcD4dstK0OZZEK5+c9yPw4CSQYnCZ75FN59B7qQe+hi8LUHh3+1gACrcHRztRtATQsamSYmoiIKiaXyOH57z6LV77/Eqw8T97mmrJUhbbFAbVdq9mVKCwUEFaPl/247/ElsdqVG7ddSUs07cpBjGkyzcA0zcZR1wI82PoF/NJ/1cQ78ft4TTnlDOL313wGf7jyk7j33G7cf+YJrE1XvukEERFVrzNGM7YFO7EtuBG7m9ahoGj48ZHv4la7C6OqxNA0ERERERER1ZTjT3bh9L5Ttjy32q5B3zw389KzIoYB9e3ijRUYJVxs5PCplgicyvTChu7XC2h8kcvmEpVi9En4H8khdZmKxJUaoM/shbzGlcOvLRzAE2Ev3kw5y1zl7Fkijx71EAJmGzwyZFsdSmgoOJ39URIyxuA0VY98Mo+eg+fRc/B80Xaj0YB/bQCBtiD8bUO/B9oC8CxoYJiaiIjKRloSb/zb69j3jb1I9SXtLqe+CUDt0KFtMaAunXj4vZyrVM2lsPoOLDGm63MZrk2scWbxbl+Jn0NTwrcrB/WixrHV/69E1SilGPj68pvxv5d/HDlFt7scmgNJ1Ym/WvoR/PWSG/DxgVfwYPd2vC/yBt8ziIjmqbdci7A11ImtwU684F077v6twU14b96EfS0/qFoxNE1EREREREQ1o5AtYO/XnrLluYVPwPi4e06OnUcafeoRQIwJ+1UgML3MkcMdoQgc0wlMS4mGFwvwHDHnrjCiOiAk4DlswnnCQmyLhtzSmXWLdqkStwajOJLM4JdhLzLWzLpXzxkhMaC9hbyZgs9aDmHTUKXiV+H4QsNQcDrMDopU3XLxHM6/cg7nXzlXtN3hdQx1ph4bpm4NwN3iqYmAFRERVY9zL53F01/ZOW4VBCozB6BdbUDb7IDim/x7ei18nmdEBEnlop+bWZbtU018PBAt2iYlICDh3ZeH0c+JjzR7Dwc24D+t/TxOuFrG3yklO0vXGSkUPBzciIeDG3F1/AQe7N6OT/U+B4dkgwcionomAbzUuApbg53YFtyENzxLLrn/1mAnrljiwgOVKY9qCEPTREREREREVDNe+f5LiJ6KTr5juSmAcbsbwjU6wFKuDlEm8ujTXoclLrqoP8djOSscWdwRikCfTv7SHBrQdL1THEjksrlEE1MTEs0788isNBHfpMNyz+zVst6TxTLHAB4b9OKdzNx0vJ+NmHoaeZFCwGyDgpkFxGdL8SlwfN6D7I+TkP0MTlPtycayOPfSWZx76WzRdofPiUBbAP7hEPWFULU76K6J8BUREVVO4nwc+76+F2/+4ojdpdQ10SSgbXJAu9qAcNTPZ7EFE4PqsbIeU4XEbaEIXOpoMPpCftVzyBx3fYFout5xhvDltZ/Ho8FrJt6J35nr2sHGlbhv3W/iv66+C//x7A78h7M7EcrH7S6LiIjKpCAU7PF1YFuwE9uCneh2Bqb82LyiIaNW3wqOZD+GpomIiIiIiKgmJHoSeOG7z9ny3PoHnFCXFJ9ClyOkJGGhX30DBZGZ9bGmY7Uzi9tDEWjT+CuInITvqTwc5xmYJpoJ5wkLxpksEtdoSLerMxq0bdQsfLolglfiLuyKNCAvq6vrdFoZQI84hFBhPTTYE+xWGhU4P+dB5idJyB4GMKg+ZKMZnH3hDM6+cKZou7PZNRSmHg5SXwhWuwNzszIGERFVr0KmgJf/7kW88FfPoZBml825oixRoW1xQO3QIJT6OxOOKafLfn3iBn8cC43in0khAMdJEw2v8GeVZi6j6Pjfyz6Gry+/BRnVGL8Du0vPO+cdzfjKqjvx9eW34DM9+/BA93Zcljoz+QOJiKjqpBUdTzZfia2hTjwS2IBBvXHGx3JNZyCM5g2GpomIiIiIiKgm7P/mXuRT+Yo/r7JWg35d+cN/EhID6lFklVjZj30pba4MbglGoU7jOpGSkmjakYMeLl4yl4FpoulR8oD3QAHOLhPx63QU/DMLPW9oTGOlM4dHBrw4kysxOGyjvEjivHYQIXM9HHLmF7NnQ3gUOD/bgOw/J2GdMW2pgagSMuE0zhzoxpkD3UXbXQEXAq1D3ajHhqldzS6bKiUiorkipUTX40ex94+eRqy7sueW84YA1A5tKCy9dOKh9XKtRmWXHJKIKeUNF17hSePqhvS47eqABd8zxdd3eH2BpuNx/5X48tov4Jh74fg7L4Sla/j1SLOTUQ18f/EH8P3FH8D1g6/hwe7t+MjgISiQkz+YiIhsE9bceDRwDbYGO/FL/xVITaVDNCdJ0QwxNE1ERERERERV7/zBc3jjX1+v+PMKr4Dj5uKAUbkGQqPKKaSUvlkfZzo63BncHIhiOg2x1KiF5idzUJPj7+OlKKKZMfol/I/kkFqvInGVBujTfzU16yY+uyCMA3E39kYaYFbRK9ISefSohxAw2+CRIVtqEC4Bxz0eZP8lCesUg9M0v6QH0ugeOI3u504XbXeH3PC3DoWoh8LUQ7cdPi5TSkRUi/rf7MPurz6F7v2n7C6lPhmAdrUBbbMDStPkkx1rOTAtITGoHgWEHLtxVif9C/Q8Ptw8PsgvUhLNu3IQY5pMMzBNU3XKEcB/XvtZ/CK0eeKdavi1SOW3w38FdvivQEfyDB7ofhyf7XkGbitnd1lERDTsjNGMh4IbsTXYid1N61BQphll5ec+zRBD00RERERERFTVpCWx+yu7Kv/ECmDc5oZwjw6OliswnRA9iKmnJ9+xjC73pHGTPzatwLTea6FpVw5Kdu7qIpqvhAQ8r5twnjARu1ZHbqk6/WMI4FpvCquHu0735vU5qHSGhMSA9hbyZgo+azmEDTEI4RBw3O1B9ucpWMe59DdRqi+FVN+pceE6z4KGkW7UgdahQLW/NQCHt/wrbRAR0eylw2k89+19eO3Hr0Ja7JpZbsInoG1yQNtgQDjmRwgjoZxDTkkUb5zFX90hLNwajEIfkzWXEhCmRPNTOaipsj0VzRM5oeL/LLsJf7zi1tJdJ9llkibxpmcJfqv91/A/Vn8Kv352J37rzJNYnIvYXRYR0bz0lmsRtgU3YmtoE573rrW7HJqnGJomIiIiIiKiqvbm1iM4/8q5ij+v/j4H1GXFp83lCExnRASD6rHijXPcVukqTwof9ceLxo8mG09ynDLh25OHYINWojmlJoHmnXlkVpiIb9Zhuaf/ZtBiFPCFhYPYG23AgZgbsopiBzH1NPIihYDZBgXTD4bPltAFHJ92I/dvKZhvMzhNVEqyJ4FkTwKn9p4s2t6wqHEoTN062pna3xqA0WDYVCkR0fxmFSy89qODePbP9iMbzdhdTt1RlqjQNhtQ1+kQ05ltXOMKyCCinCjjESU+HoiiWS++mCAg4d2fh97PoD9Nz86my/BA2714y714/J0XLm4xME1TNKA34usrbsW3l30cn+p9Dg90b8fGxAm7yyIiqmsSwEuNq7A12IltwU14w7PE7pKIGJomIiIiIiKi6pVL5rDvT/dU/HmV1Rr0d5d/qfo8UuhT3yjrkreT2diQwg3+eNG2yQLTrrcKaDxQKCqTiOaW86QF42wWiWs0pNtUTKstPABVAO9vSqDVlcUjA16EC9Vz2S+tDKBHHEKosB4aKt+5VqgCxh1u5LalYb6er/jzE9WqxLk4EufiOLn7RNH2xiWN8LcGEWgb7krdFoB/bQCGh2FqIqK5cuqZk9jz1V0YeHvA7lLqiwDUdg3alvGTpscq16pT1UZCYlDtghTW2I2zukZxnTeFVndu3HbPIROud6wSjyAq7YzRjN9b+xn8rOW6iXeqw9clVUZe0fDjhe/Bjxe+B++JvIkvd2/HJ/pfggpeDCUiKoeCULDX14FtwU5sC27EaWfQ7pKIilTP6AkRERERERHRRV78qwNI9iYr+pyiQcBxi6toWzkGSE3k0Ku9Dnlx6+Y5HN/Z3JjEB5uLl9i9ZGBaSngOFtBwiO2lieyg5AHvgQKcXSbi1+ko+JXJH3SRJY487ls4gKcijXgl4UK1LHadF0mc1w4iZK6HQzZW/PmFImDc6kJOA8xXGZwmmo34mTjiZ+I4+fQ7Rdu9S73wt40PU+su3aZKiYhqX+RkBM/88dPoeuLY5DvT1BmAdrUBbbMDStPk37nrMTANACnRj4wSLt44i7/qCkcWv+JLjNtunDTRcJCrvtDU5IWK7y75CP5w5e1IaK7xO0zWCYBomp5p6sAzTR1Yne7Bl7qfwL3n98Brpu0ui4io5qQVHU82X4mtoU48EtiAQb3y14CJpoqhaSIiIiIiIqpK0VMRvPz/XqzskwrAuM0N4RkdNC1HYNqCiT71DZgiO9sKp+xd3gTe21QcOL/kuJIl4X22ANex4sD0HDfCJqISjH4J/yM5pNarSFylAfr0XoWGAnzEH0erK4vtg17ETXWOKp0eS+TRq74Gv9kKjwxV/PmFEHB8wo2cnkbhxfHd74hodmLdMcS6Yzix6/joRgH4lvlGw9StF8LUfmhOhqmJiCaSS+Twwl8fwCt/9yLMHCe1lovwCWidDmgbDAjn/DzTtVBASvQjqfQiq8TKdtxG1cQtwWjRgjlSAlrEQtPe4kmLvM5AE9nt68ADbffidc+yiXdiYJrmyHHXAvxO6+fxlVV34L5zT+P+M09gZabf7rKIiKpaWHPjscAGbA124gn/lUipU1jBlROgqAowNE1ERERERERVae8f7YaZrezgsP5eB9QVxafKsw1MS0gMqG8jp8RndZzpPON7fUm8yzeNwHReoml3Ho4zxUvlciCTyD5CAp7XTThPmIhdqyO3dPrB59WuHH5t0QB+OdiIIyknquEVLYWFAe0t5M0UfNZyCBtqMj7qAnSg8CyD00RzTgLRU1FET0Xxzo6ukc1CEfAu9yHQFkSgNTASqm5e7Yfm5LAFEc1f0pJ48xdHsO/reyq+6lI9Uxar0LYYUNfpEIr934krTUIiK6JIKr1IiX5IYZXaacanCwokbg1G4Vbl6OEkoGQlmnfkMHbBLV5noFLOGz78/up78OOF77G7FCLENDe+s+wmfHfpR3Fr/4t4oHs73h19m+9dRETDzhpN2BbsxLZgJ55uWoeCMs3rOAxMUxXg1UciIiIiIiKqOqeeOYmuJ45W9DmVlSq09zjKftyIcgJpZaDsxy1N4oNNCWz2poq3XiIwLTJDg5j6gCx+DDiQSVQN1CTQvDOPzAoT8c06LPf0XplOReLmYAxtqSyeGPQibU2+/HglxNTTyIsUAmYbFFS+E7bxIReEIZDfXbkVAIholLQkoiciiJ6I4Pgvj41sF4qAb2UTAq1DIeqxYWrVqI6u+UREc+X8K+ew+6u7cP6Vc3aXUh8EoLZp0K51QF028ZB4OVaXqlYFZJBUepFQeiZf+WoW/wQfao5jiaO4m7Swhq41qMWXJ3idgYoUhIK/WXw9/ueqOxHT3ON3YCdKspElFPx7aDP+PbQZnbEuPNj9OO7oOwBdcgUIIpp/3nYtxNZgJ7aGNuF571q7yyGaNYamiYiIiIiIqKpYBQt7/vCpyj6pR8Bxq7tooLQcA6cJcR5x9cxsq5siiRua49jYmC7eeonxJTVmoWlHHlqcgWmiauc8acE4m0XiGg3pNhWYZoe8DncWSx0D2D7gRVem/BNEZiKtDKBHHEKosB4aKl+T/itOQBfI78hU/LmJqDRpSUSOhxE5Hi6aQCdUgaaVzUVB6kBbEE2rmqHqDFMTUW1L9iSw75t78ca/vm53KfXBALSrDGibHVCaJ58wWG+BaQsm0mIACaUHWRGd85P79e70uOsQkBLeZ/LjJmcTjbXf24oHWu/FwcaVE+9UZ69Pql0vetfgc+t/G/8lezd+68wv8etnd8Ff4IoQRFS/JICXGleNdJQ+4llqd0lEZcXQNBEREREREVWV1378Kgbe6q/cEwoMBaYbRgdTyxGYToswBtVjxRvnKI0sIPFRfwxXNRQH/y4VmNYGLDTtyEEtkRXkkBRRdVLygPdAAa4uE7HrdBT80+sa3aBauLMlglcTTuwMNyIn7e86nRdJ9GivImiug0M2Vvz59WsdgA7ktzM4TVTNpCkR7hpEuGsQ2D4aplY0BU2rmuFY4kR40wCC60IIrQuhYVFj3YXgiKj+FDIFvPL3L+GFv3wO+WR+8gfQJQmvgLbJAW2DAeGcX58BEhI5kUBS9CCp9EGKynRBDeoFfNQfG7fd/boJ1wmrIjVQ7enTG/FfV9+Ff1z0frtLIZq2Mw4//tvqu/DHK27F58/vxf3dT6A9zRUiiKg+FISCvb6O4aD0Rpx2Bu0uiWjOMDRNREREREREVSMdTuPZb++r6HNq73ZAXVV8ejzbkE0OSfSrbxanj+cwMP2xQAyXe6YemDbOmPA9nYdSKH89RDT39H4J/yM5pNarSFylAfr03lyuashghTOHRwd8OJ015qjKqTNFDr3qa/CbrfDIUMWfX9/ogNAFcg+nh96riahmWAULg0cHgKPAuadHV/dweB0IrgsN/eoY+j3QFoDutv89j4hISonjT3Zh79eeQvRU1O5yap6ySIV2rQF1nQ4xzdVYap2JHJJKLxJKDwoifemdy3xNwhAWbgtGYIyZhyklYJwz0fgSLzbQeCYE/m7xB/HfV30aEd0zfodLXcgiqjIp1Ym/WXID/mbJDbhx4BU82L0dHwq/zkYURFRz0oqOHc1XYGuwE48Er8GAXvmmFkR2YGiaiIiIiIiIqsaB/7Mf2Wjlun0qy1Xo73WU9ZgFZNGnvV7c2WmOAtMKJD4RiGKdJ1u0/VLjTM4uE959eQgGA4lqmpCA53UTzhMmYtfqyC1Vp/X4Js3CPS1hPB93Y0+kAabNQ3tSWBjQ3kLeTMFnLYeocD3alQagAbmtaYBN8YhqXjaWxZkD3ThzoHt0owCaVjUPhag7QgitH/q9camXXamJqGIG3u7H7q8+hdPPnLS7lNomALVNg7bFAXX5xMPd5VhFqtpIWEiLQSSUHmREeOrXGsr6zyBxUyCGgF7c0VpNWmjelb9oT65mRcALjatxf+u9eNG7ZuKd6uy1SvPH9sAGbA9swBWJU3ig+3Hc3bsfTosrSBBR9Ypobjwa2ICtwU484b8SKdU5+YM4uYnqjIhEIhwmJRp29OhRtLa22l0GERFRTeLnKBHNVv9bffjJjT+ENCt0muoWcH6xAYp3tC3SbAdULZjo0Q4hL5LlqPCSVEjcGoyi1T3FwLSUcB820fgyOz4R1aPMCgXxzTos9/Tfw/pyKh4Z8KEnr89BZdPnsgIImG1QML0geDmYR/PI/msKqMyK5kRUBYxGYyRIfaE7daA9CMPDrtREVD6ZSBrP/dl+HPrRwcqd89YjHdCuMqBtMaA0V/67op1ySCCp9CKp9MIS9p7Xb2pM4kPNieKNBYnAL7LQUqObGJimAa0B/2P1p/B3iz4AKZTJH0BUB0K5KP7D2Z34zTNPYkE+Znc5REQAgLNGEx4KbsTW4CY83bQOBWX+9Nn92iYv7r+cHbSpGEPTRGMw7EVERDRz/BwlotmQUuIXn/k5Tu87VbHndNzlhrq2fAFBCYl+9Q2klcGyHXMimpC4LRjBGleuuIaJAtOWROPzBbjfKk4BcgCTqL5YOpC4RkO6TQWmuTS5KYF9UQ+ejXkgq+CdwbAaEDTXQUN5VwOYCvOdArI/SwJsDEU0r/lWNI2EqC90pvYu9UFM8/2ViOY3q2Dh8E8P4dlv70MmnLa7nJolvAJapwPaNQaEc/68D5vII6X0IaH0TD45u0In+EsdOdzTEi4+3ZASzdtzMPoYO6AhFgT+ceF78V/X3I0BvURIid0qaR4wrDzu7tmPB7u348rkabvLIaJ56G3XQmwNdmJbsBMHfPN3DJ+haSpl/kwbICIiIiIioqp1/JfHKhqY1q4zyhqYBoCwcrwigWldSHwyFMFKZ27cfSXHm0wJ3548nKesos0MTBPVHyUPeA8U4DpmIvYuHQX/1Dt5qQJ4b1MSa11ZPDLgw2DB3suGOSWBHvEqguY6OGRlL2qrqzQ47vEg+89JIDv5/kRUn6InI4iejKDr8aMj23SPjmB7CIF1IYSGw9SBjiAcjZWf4EFE1e/0/lPY/dVdGHiz3+5SapaySIW2xYC6Xp83k1YkJDIijKTSi5QYAMQUg8gV+OfxKCZuDUaLAtNSSnifzzMwTSNeaViBL7Xed+lwFgPTNA/kFB0/WPQ+/GDR+/CB8GE82P04bho4CAV8vySiuSEBvNywEltDm7At2IkjnqV2l0RUtRiaJiIiIiIiIlsVMgXs/aOnK/Z8ylIV+gecZT1mXDmLhHqurMcsxRAW7gxFsMw5tfanIivRtCsHo7f4YjwD00T1TR+Q8D+SQ2qdiuTVGqQ+9Vf8YkcB9y0cwNORRryUcMHOdwtT5NCrvga/2QqPDFX0udVlGhyf8SD7kxSQ4YAmEQ3JJ/M49/JZnHv5bNF27zLfcEfqIIIdIQTXt8C33AdF5TL0RPNR9FQEe/94d9GkC5oGAaitGrRrHVCXTzyULaWEqKPgZR5pJJUeJJVemGL8JGm7KZC4JRhFg1o8Idt5woL7TWuCR9F8EtHc+F8r78D3ltwAS/A7ENFYTzVfjqeaL0dr6hzu734CXzi/Bx6Ls7SJaPYKQsEzvnZsG+4ofcoZtLskoprA0DQRERERERHZ6pW/fwnRU9HKPJlLwLjNXdSharYDrSkxgLByvHjjHKSSHcLCp1vCWOwoTGl/JSHRvCMHLcrANNF8JCTgOWLCedJEbIuO3DJ1yo/VFeAGfxyt7iweG/AiZk79seUmhYUB7S3kzRR81nKICr6DqYs1OD/nQeYnSSDJ4DQRTSx2OorY6SiO//LYyDbNpSHQHkRoXctQmHq4M7XDV97Je0RUPXLJHF786+fx8t+9ADNr2l1O7dEB7SoD2mYDin/y75/1EJi2UEBK9COh9CCnxC+9s80n8+9rSmD5RRO41YiFpj1Tm9RN9UsC+NGC9+D319yDXsNXYgfJztJEw466F+GBtnvxP1fdiS+e24XfPvNLLMvO/cqFRFRf0oqOHc1XYGuwE48Er8GAXtlV+ojqgYhEIrziTzTs6NGjaG29xFJBRERENCF+jhLRTCR6Evjh+7+PfKoyg2zGp9zQ2vSyHS+HBHq0Q5BiTFelORjIdClDgemFxtQC01rYQtOOHNRUeesgotqVWaEgvkmH5ZneG1TGEtgRbsThpBN2T7lwW0H4zVYoqGyI2xowkf1REjLOy6hENHuNSxqHulGva0Fw3VBn6qZVzexKTVTDpJR4a+sbeOZP9yDZk7C7nJojGgW0TgPaNQ4IV/0HKyUksiKKhNKDtBgovp5QpdpcGdweKp7sLnISwZ9noYy5TMFJ2vPPa55l+FLrvdjX1GF3KUQ1SZUm7ug9gAe6H8eWeJfd5RBRFYtobjzmvxpbQ5vwhP9KJNUpTMjmxCUAwNc2eXH/5QyWUzF2miYiIiIiIiLb7P/GnooFprUtRlkD0wVk0acdmfPAtFsxcVdLBC1TDEzr50007cpDYbMnIhrDedKCcTaLxAYN6XYVUKb2ZuVUJD4eiKHVlcXjg16kLftCfSmlHwVkEDTXQYOjYs+rBFQ47muAdaIAGbVgRSzIqAUZlZAxC2AjSSKahviZOOJn4nhn5+hKJapDQ6A9gGBHCKH1o52pnU0uGysloqnoOXQeu//XLpx7+azdpdQcsVCBfq0D6jodQq3/MEcBGSSVXiSUHpgia3c5U9asFfCxQKx4oyXRvJ2B6fksprrw1ZW34y+XfgSmsG9lIqJaZwoV/7LgXfiXBe/CtdG38eXu7bi1/0Vosvon1BDR3DtrNOGh4EZsDW7C003rUFCmGfNkYJpoQgxNExERERERkS3Ov3IOb/zbkYo8l7JYhf7B8i2FbqGAPu11mCJXfEeZr0E1qCbubgkjoE8tkec4YcK3N48aaFRFRDZQ8oD3+QJcXSZi79JR8E89AN3uzmKpox+PD3pxNF2+99PpyikJ9IhXETTXwSEr1yFE8SpQrjTGbZdSQsblcIh6NEw9Gqy2gKnNeSGieczMFtB7qAe9h3qKtjcsbEBwXWjoV8fQ782r/VA0dqUmsluyN4n939yLIz8/bHcptUUAaqsGbYsD6oqJh6mllBB1EPKwYCItBpBQepBVopM/oMroQuK2YBQOZXTFFSklfPvz0CPF+9b+/xZNhQTwLy3X4ffWfAbnHM0ldmBHS6KZes7Xhrt8bViR6cNvd/8Sv3buKfjMtN1lEVGFHXUtwNbgJmwNduKAjys8E80VEYlEuK4k0bCjR4+itZUfOkRERDPBz1Eimg5pSfzLbT9Gz8Hzc/9kTsD5xUYoTaPhktkMwEpY6FOPIKNEylRgad7hwHTzFAPTrjcKaHy+wIFKIpoSKYDUOhXJqzVIfXrvHIcSTuwMNyIr7QvtCanAb7bCI0O21TBVMmnButCZekyY2roQqq6dRoNEVAVUhwr/2gCC64eD1B0hhNaH4PK77S6NaF4oZAs4+A8v44XvPodcIjf5A2iIDmhXGtC2GFD89d2VVkIiJ+JIil4klT5IUavLkkh8IhDDZZ5M0Vbn0QJ8+zkrcD56w70Y97fei6ebL7O7FKJ5oaGQxn3nd+NL3U9gTabX7nKIaI5IAC83rMS2YCe2hTrxumeZ3SXVna9t8uL+yyvXfINqAztNExERERERUcW9+YsjlQlMAzA+7i4KTAOYRWBaIqwcn/PAdJNWwN0tYfi0KbSMlhINLxXgeb1WB2KJyA5CAp4jJpwnTcS26Mgtm3p45cqGDFY4c3h0wIdT2fHdlytBCgsD2lvImyn4rOUQVTxlRHgUqB4FWFz6fpkZClNbRd2qRztWI82eF0Q0ysya6Hu9F32vFwcnPC2e8V2p1/ih6vUdTiSqFCkl3tl5HHu+9hSiJyJ2l1MzRKOA1mlAu8aAcNV3l3wTOSSVXiSUHhTEJJ1BJaq+NfOGhvS4wLQ6aMHLwPS8k1Ad+KMVt+HPl96IgsJ4CVGlJDQXvrv0o/jLJR/GJwZexoOnt+O90Ter/eODiKagIBQ842sfCkoHO3HKGbS7JKJ5h99qiYiIiIiIqKJyiRz2fX1PRZ5L6zSgdehlO15cOYOEOrdhb/9wYLpxKoFpU8K7Pw/X8eJ9a2D8lYiqhJoEmnflkVlhIr5Jh+WZ2ruHT7Nwz4IwXoi5sTvagIK0510npp5GQaThN1uhoDaDgcIpIBaqUBaWrl/m5EXdqYs7VssEQ9VEBCR7k0j2JnFy94mRbYquwN8aGOlIfaE7tSfksa9Qoho0eHQAu//wKZzac8LuUmqGWKhA3+KAul6HUOv37FTCQloMIqH0ICPCUz8Rr/J/kkVGHtc3x4u2iZxEYHuu2kunMpIAfhHchN9Z+zl0OwMldpDADJsSENHUSaHgoWAnHgp2YkP8HTzY/Tg+1fssDMkGGkS1JK3o2NF8ObYFO/FwcCMGdHY+JrITQ9NERERERERUUS/89QEke5Nz/jxioQL9emfZjpcS/YioJ4o3ljmdHNQLuKsljAZ18sC0yEn4ns7DcY6BaSKaPedJC8bZLBIbNKTbVUCZ2jvJJm8Kq1xZPDLgw/lc+SapTEdK6UcBGQTNddDgsKWGuSQMARFSgZBaMhYuCxIyNhqmHtexOiaHPhyIaN6x8hb6j/Sh/0hf0XZX0I3QmI7UF7pSaw4OGRGNlYlmcODP9+PVH7wCafLDdCrUVg3atQ6oKyZ+P5FSznj1p2qRQwIJpQcppQ+WqK/Oyy7Fwm3BCIqy7pZE8+M5jP2r8tpDfTvqWoAHWu/Fk/4rJ96pxl/HRLXolcZVuHfdb+K/rL4Lv3l2B/7D2R0I5hN2l0VEE4hobjzmvxpbQ5vwhP9KJNUpjFdxUhJRRYhIJMKzfKJhR48eRWtrq91lEBER1SR+jhLRVERORvCj6/8BZm6OO2EYgPOLDVD8o/Gy2QzMZkUMvephSDEmoFzmEcIFeh6fbgnDrU5+mq6kJZp25KAPFu/LQUsiKod8QCB2nY5CYOpLqFsS2B/zYH/UA8umdyJVGgia6+CQ7NQylrQkZEwWBalHgtWRocA12KCKaN5TNAXNa/wjIepgRwihdSG4Wzw1H24kmi7LtPD6P7+GZ//3M0gPpu0up/rpgHalAW2zASVQmyt/TIWJPFJKHxJKD/JikongNXpyLiDxqVAEq1y5ou2N+3NwHx29HlKjfz2agpRi4E9X3IJvL/sYcoo9k2KJaOqcZg6f7XkGD3Q/jvWpM3aXQ0QAzhlNeCi4EVuDnXiqaT0KCicn2+1rm7y4/3JeL6ZifGUSERERERFRxTzzx0/PfWAagPExV1FgGsCMwx4FZNCnvjGngelFxlBg2qlMHphWoxaad+ShJsbvy0FLIioHfUDC/2gOqXUqkldrkPrk7y6KAN7jS2KNc6jr9ECh8pcdTZFDr/oa/GYrPDJU8eevVkIREE0CaJo4BG/FLciINdSxOjLUtXpsx2rkK1gwEdnCKlgYeKsfA2/1462tb4xsd/ldCAx3pL7QndrfGoDm5PAS1afu505j91d3jevQTuOJBgFtkwHtGgPCNfXJdrVEQiIjwkgoPUiLQUBMsRdZjZ6cv8eXHBeYdnYVigLTQM3+9WgSDweuwZdbP4+TzhLnUux6SVSVMqqB/7f4g/h/iz+IGwYP4cHu7fjI4CG+TxNV2FHXAmwNbsLWYCcO+NhgjKgW8KoWERERERERVcSpZ06i64ljc/482jUGtMuMshzLQgG92uuwxEVpsTJeeV7qyOHOUASOKQSm9T4LTTtzULLle34iolKEBDxHTDhPmoht0ZFbNrWugYscBdy3aAC7Iw14Ie5GpSMVUlgY0N5CwUzDay2D4FDhlCiNCtA4cdhJJi90px7TsToy2rEa/FwiqlvpwTS6959C9/5TI9uEKtC82j/SkfpCd+qGhQ3sSk01K9YdxTN/shtHH33b7lKqnligQN/igHqZDqHW52s+jxSSSi+SSi9MkZv8AXVgjTOLd/uKO2irEQvefQWbKqJKOe4M4T+t/TweDV4z8U78fCeqek/6r8ST/iuxLnkGD3Rvx2d7noHL4gxoorkgAbzSsBJbg53YFurE655ldpdERNMkIpHIFKfEEtW/o0ePorWVs36IiIhmgp+jRHQpVsHCT278AQbeHpjT5xEtCpy/2gChzX4wR8JCr/o6skq0DJWVtsKRwydDYRhTaMplnDbRtDsPMfeNuomIxsksVxDfrMPyTP399VRGx6MDPkRNe5Zpd1tB+M1WKKjfZeKrhcwMhamt6IVO1Rd+DXWsRoqXoInmA4fPieC6IELrWoa7UwcRaAtCd+l2l0Y0oXwqhxe/9zxe+tsXYWYZDr0UZa0G/VoH1JUT9+SSUtbs5AkLBaREPxJKD3JK/NI7l3n1Kbv51ALuWzRYtPqVyEsEfpGFmraxMJpTGUXHt5Z9HN9YfjMyaonmA+wuTVTTAvk4fuPsTvzWmSexKBexuxyimlcQCp7xteOh4EZsC3aWXpmBqtLXNnlx/+WNdpdBVYadpomIiIiIiGjOvfajg3MemIYBOD7pLgpMz3TAVkJiUD02p4HpVc4sbg9GoE8hMO16u4DG5wpTXgmYiKjcnKcsGOeySGzQkG5XAWXy99blzjx+ddEAdoYbcSjpRKWTJSmlHwVkEDTXQYOjos893wingHCqUBaUDqjL3GiH6lIdq2WCH3BE9SAbzeDMc90481z3yDahCDStakawI4jgupbh7tRBNC7x1mywkuqDlBJvP/QmnvnTPUicmyQgO59pgHalAW2LASUw+US0WntdS0hkRRQJpQdpMQAprKk9sLb+mpekCYnbQtGiwDSkRNPOHAPTdWy7/yp8ufXz6HItHH/nhbB0jb2eiajYgN6IP11xK/73so/jU73P4cHu7bgmccLusohqSkbR8WTz5dgW7MQjgWvQb3jtLomIyoShaSIiIiIiIppT6XAaz/7Z/jl/HuNG17hB3JkO2MaU00gqveUoq6S1rgxuDUYxaUNsKeF5tYCGV9lemojsp+QB7/MFuLpMxK7TUQhMPuvDoUjcFIih1ZXB44NeJK3Kdn3OKQn0iFcRNNfBIdlRxC7CEBAhFQipJft+y4KEjI2Gqcd1rI7JoY6ORFRzpCUR7hpEuGsQRx99e2S74XUg2DEUoA6tb0GwI4RAewC6u0SnS6Iy63ntPHZ/5Smce/GM3aVULdEgoHUa0K4xINxTmOlbYwrIIKH0IKn0whRZu8ux1Q3NMSw0irusN7xcgNEz+uWrzhprz2snHUH8ztrPYlto08Q7MSxNVFfyioYfL3wPfrzwPfiVyBv4cvd2fLz/Zag8ySYqKaq68FhgA7YGO/F44CokVefkD+LqDEQ1h6FpIiIiIiIimlPP/dk+ZKOZOX0O9Sod2hXlCVgkRS+i6qmyHKuUdlcGNwejUCe7hmZJND5XgPtocWCag5VEZDd9QML/aA6pdSqSV2uQ+uTvSq3uHJY4BvDEoBdvpacw2FBGpsihV30NfrMVHsmlM6uR0ASEXwX8pe+XloSMy9HO1CMdqy3IyFDgGpxfRFRTcrEszj7fjbPPj3alhgCaVl7oSh0aClWvC8G71AcxhRUOiCaT7Evi2W89g9d/9hon40xALFCgb3FAvUyHmPSktbZYMJEWA0goPXO6qlStcCkWrmlI4aqG4us1RrcJ9+HRL1a8BlEfckLFny37GP54xa1IqyVW4WHYi2he2Nu0Dnub1mF1ugdf6n4C953fjUZzbq/bE9WCc0YTHgpuxNZgJ55uWo+8Ms04JT9DiWqOiEQivCxANOzo0aNobW21uwwiIqKaxM9RIiql/80+/OTGH0Jac3fqKYIKnL/WADGF0N5kMiKKXvUwIMYuS4uyjRCud6fx8UAMk2Y+8hJNe/JwdBcvDczBSiKqNqYHiG/WkV0+9Q7Sh5NOPDnYiKysfNdCn7kcXmsZBN9N644VH9OZOjLUtXokWB21gLzdFRLRTBkNBgLtw0HqC2HqjhCMBnalpqkxcyZe/ceXceAvnkUunrO7nKqkrNWGwtKrJg6ISClnvJqTXSQkciKOhNKDlOiHFPN7lpVHMdHmzqLdncVyR27ctQklYSHwUA4KvzfVlR3Nl+HB1nvxlnvx+DsZliaa17yFFH713NP40pknsDLTb3c5RBV1zLUAW4Od2BrsxAHvWkhRf6ur0JCvbfLi/su5AiEVY6dpIiIiIiIimhNSSuz+6lNzGpiGDjg+6S4KTM90IDePNPrVN+YsMH2lJ40b/bGisahSY1MiI9G0Mwejv/jfjYFpIqpGahJoeiqPzHIT8c06LM/k71SXezJY7sjhsUEvTmRKdDmbQ1H1FPIiBb/ZCgVTD3pT9VMaFaBRAZaWvl+mLFjDYWo5JkxtDYeska1svUQ0dblEDudeOotzL50t2u5b7isOUq8Lwbe8iV2pqcg7O7uw52tPI/JO2O5Sqo8GaFca0DYbUIKTfy+qpcB0AVmklD4klB4URPrSO9f5yXajaqLdnUW7K4OljvzE+VhTonlnnoHpOtLt8OP31nwGP2+5duKdauh1TUTlF9Pc+PNlN+Evln4Ut/W9gAe7t+O62NF6/likeUwCeKVhJbYGO/FQsBOHG5bZXRIR2YihaSIiIiIiIpoTXU8cQ/f+U3P6HMZHXFBCxYO7MxnINZFHn/Y6LFEovqNMV4g3NKTwEX+8aFupwLQSt9C8Iw8txsA0EdUW5ykLxtksEtdoSLermKylvlezcFdLBC/FXXg60oi8rNy7XErpRwEZBM110FDZ0DbZR7gVqG4FKNFgDwBkRo6GqEt0rEaKCzYSVZvoqSiip6LoeuLYyDbdrRd3pV4XQrA9BIeX7/fzzeCxAez52tM4+fQ7dpdSfTwCeqcBbaMB4a6fjnoSFtJiEAmlBxkRnvpJdB2ebPvUwlBQ2p3FEscUUtCWhG9PHhoXqK4LeaHiL5Z+BH+48pNIqs7xO7C7NBFdxBIK/q1lC/6tZQs2xY7hwe7H8cm+56HL+b1CA9U+EwLP+NqxLdSJbcFOnHSG7C6JiKqEiER49kN0wdGjR9Ha2mp3GURERDWJn6NENFYhU8A/Xf8PiJ2OztlzqFfocNzinvVxJCz0qK8hp8Qn33kGNjUm8aHmRPFzlhif0gYsNO3IQc3MSRlERBWTDwjErtNRCEwthDOYV/HIgBdnc8YcV1ZMlQZChfUw0FDR56XaJPOjHapLdayWcV5mJ6pmjUu9CI3pSB1cF4JvRRMUtX4CozQkG83gwF88i1f/8RVYBcvucqqKaFGgb3FAvUyH0OonMJlDAgmlBymlb/xE6HnEr10ISmew0Jj6v4MattC0Kw8twe8y9WC3rwP3t92HI54Sy68wLE1E07A0M4DfOvNL/Pq5XWgupOwuh2jKMoqOJ5svx0PBTjwcuAb9htfukshmX9vkxf2XN9pdBlUZhqaJxmDYi4iIaOb4OUpEY73wVwew/5t75+z4IqDA+WsNEMbsBnskJAbUt5BS+stUWbFrvUm8v2nywLRx1oTvqTyU+Tu+S0R1RgogtU5F8moNUp/8vdqSwLMxD/ZFPbAq2O5PSAUBsw1uGazYc1J9kuaFEHWJjtVRCzImAWb3iKqK5tRGu1KPCVM7fSW6clLVs0wLR352GPu/tRfpgbTd5VQVZY0G/VoH1FUTL0AspZzRqk12MZFHUulFUulFXiQvvXPdLt0kEdRNdLgzaHNl0TKNoPTQwyX08xaad+YhxjQSrdt/rjp33vDh/1tzD36y4D12l0JEdcZtZvCF83txf/fjaEuft7scopKiqguPBTZga7ATjweuKr3SwsU4mWjeYGiaSpn47JiIiIiIiIhoBhLn43jhL5+buyfQAMft7qLA9EwHeKPKyTkKTEu8x5fEe3zFg7elrsM5u0x49+UhOKWZiOqIkIDniAnnSRPxzTqyy9VL7q8I4N2+JNa6snhkwIu+vF6ROqWw0K+9CZ+5HF5rGQQjIjRDQhUQfhXwl75fWhIyXtyd+uKO1eDKx0QVVcgU0PPqefS8Whz+aFjcOBKivtCdumlVMxSNXamr1Znnu7H7K7vQ93qv3aVUD21odSZ9iwNKcOLvYRfOpWshMC0hkRFhJJQepMUgpnwSXf1/tWmQWKCPdpQO6NP88mBJ6H0SjlMmHKescd2lGZiuPQWh4K8X34CvrLoDMa3EamwMhBHRLKVUJ7635AZ8b8kNuGngFTx4ejs+GHmdnxdku/OGDw8FNmJrqBNPNV2GvDLNCCQ/H4nmNYamiYiIiIiIqKz2fWMv8qn8nB1f/7ATyoLiQd+ZDPAmxHnE1O5ylTWGxPubErjWW7xs4bhxKinhft1E40tsL01E9UtNAk1P5ZFZPhSetjyXfr9eYBTwhYWD2BNpwAtxN2SFhuGi6inkRQp+sxUKLh3wJpoJoQgInwB8E4cuZXxsh+oxHasjw6Hquft6RURjJM7GkTgbx4ldx0e2qQ4NgbZAUUfq4LoQXM0uGyul2JkY9n19D95+6E27S6keHgG904B2jQHhmTzoXwth6TxSSCg9SCl9MEXO7nJsILHIKKDdnUGHO4smbfKgdNH1B1PCOGfBccqC47QJNTPx46r/p4HG2udtw/1t9+JQw4qJd6qB1zgR1Y7HAhvwWGADrkycxAPdj+Ou3mfhtHiiSpVzzLUAW4Od2BrsxAHvWkjBia1ENDMiEomwlxXRsKNHj6K1tdXuMoiIiGoSP0eJCADOvXwWP7vtJ3N2fHW9DsftJTrnTFNGRNCrvl7cmaosLZUkrm+Oo7OxeDnocYFpS6LxxQLcb7ClJBHNH5YGJK7RkG5Xh1pLT+J0RscjA15Ezcr1fTCsBgTNddDgqNhzEk2VTA2FqUeC1cNh6gt/xiVCUEQ0NzwLGkZD1B0hBNcF0bzaD1XnBJy5lE/n8dLfvoCXvvc8ChlOQgUAEVKgb3FAvVyH0Go/JGmhgJToR0LpQU6JX3rnumyPLLHUkUe7K4s2dwY+zZr8EWOvO+QlHGesoY7S3RYU5tnqSq/uxX9dfRd+sOh9dpdCRPNcSy6K/3hmB/7D2R1YkI/ZXQ7VIQngYMMKbA12YltwEw43LLO7JKpBX9vkxf2XN9pdBlUZhqaJxmDYi4iIaOb4OUpE0pL4l9t+jJ6D5yffeQZEswLnFxsgHLMbDc0hiR7tEKQYE1guU2D6I81xbJgsMG1K+Pbm4TxpXfToOhznJSIqIR8QiF2noxCYvBtMzhLYGW7Aq0kXKvUuqUoDocJ6GGioyPMRlYvMypEw9WjH6uGgdcQCUhwKIKoE1VDhXxtAcF0QwXUtw4HqINxBj92l1TwpJY4++hae+ZPdiJ+ZJEg7TyhrNOhbDKir9Qn3kVLWREdpCYmsiCKh9CAtBiDF5EHheiIgsdyRR5s7g3Z3Fg3qNIPSGQnnaRPOUxaMsxbm2T/fvGBC4P8u/hD+x6pPIaKX+EwZdwGKiKgyDCuPe3r24cHux3FF8rTd5VCNMyHwjK8d20Kd2BbsxElnyO6SqMYxNE2lVK5NCxEREREREdW1N/799TkLTEMFHLe7iwLTMxn4NZFDn3akODANzDqHJyBxkz+GKxqKWzxePF4lchJNu3IweopDSwxME9F8og9I+B/NIdWhIrlBg9Qnfgc0FIkbA3G0ubN4bMCLpDX3nTtNkUOPdggBsw1uGZzz5yMqF+EQEC0q0KKi1CtF5uVIkNoaDlOP7VgtE3LoSwkRzYqZM9F3pBd9R3oBHBnZ7g55RgLUoXUtCKwLwb/GD9VgV+qp6D3cg91ffQpnn++2uxT7aYB6hQ59swNKaOKfnwvnzNUemC4gg4TSg6TSC1Nk7S6nohRIrHDm0O7Oos2VgVud3gexkpRwnjLhPGVC75VFi2lRfXm+cTXub7sPLzWunninKn+tE1H9yik6/nHR+/GPi96PD4YP48HT23Hj4KtQeIJJU5RRdOxovhzbgp14OHAN+g2v3SURUZ1jaJqIiIiIiIhmLZfIYd/X987Z8fXrnVAWFQ8GT3fg14KJPvVI2QdhFUh8PBDFek/xcS8OTCtJieYdOWgRBqaJiIQEPG+YcJ40Ed+iI7v80oGxNa4cvrhoAE+EvXgz5Zzz+qSw0K+9CZ+5HF5rGQTfqakOCF1ABFUgOEGo2pSQsdEwtRUZ263agoxJgF0riWYs1ZfEqb4kTu05MbJN0ZWhrtQdQ2Hq4PoWBDtCcIfcVR90rZTUQArP/u9ncPinhzixwyOgbzSgbTQgPJOv2FHNP0MWTKTFABJKD7JK1O5yKkqFxCpXDu2uDFrdWTiVafxgSwklKuE6ZcFxyoQ+MN9fFPVvQGvAf1v9aXx/0fshxeSveyIiu+1qvhy7mi9HW+os7u9+Ap8/vxcea35NiqKpiaouPBbYgG3BjXjcfxUSmmvyB3FVBSIqE4amiYiIiIiIaNZe+KvnkOpLzsmx1Q4N+ibHrI4hITGgvo2ckihTVUMUSNwSjKLdfenAtBq20LwjBzU1/hi8xDe/pRQDT/ivhGEV8MHI63BZebtLIqooNQU0PZVHZtlQeNryTPyu6FIlbg1GcSSZwS/DXmSsuQ8NRNVTyIsU/GYrlJIxU6L6IVQB0awCzaXvl5aETMjRztRjO1ZHLMiYBRQqWzNRrbPyFvrf6EP/G31F210B11CQet3wr44Q/K0BaI75M6xn5k0c+uFBPPfn+5GLze+gjQgp0Lc4oF6uQ2i1ewYpIZETcSSUHqRE//gVoOqYJiRWO7Nod2ex1pWFY5pBabVfwnXKhOOUBS3GoPR8YEHgHxa9D3+w+i4M6CWWk2dojIiq3Nvuxbi/7T78z1V34ovnduG3zzyJpdlBu8sim503fHgosBFbQ514quky5JVpnt/ws4+IykREIhGeWRENO3r0KFpbW+0ug4iIqCbxc5Ro/oqcCONHN/wjzFz5BzxFk4Dzi40QztldDAsrxxFXz5apqiEqJG4LRbDWlSvafvG4ld5joWlXDkoORCPCmht/veTD+O6Sj4wsN+gxM/jYwCu4s/c5fHTwVQaoad6xNCCxQUO6QwWUS7/vxwsKHhv04p3M7CbVTJUu3TBkI1RpQIUOVRpQoEOVOlQYEFDZjZoIgEwMB6kjYzpWj+lWDX4fIpoxoQr41/gR6AghtD40Eqr2LGio6o7CM3Hi6Xew5w+fQrhrfgdrlNUa9C0G1DX6hPtIKav+/7+ALJJKL5JKLwoifemd62gpJkNYWOPKod2dwWpnFsZ05vtZEnqPBedJC47TZskJ2FS/Xm5YiS+13YfnvWvtLoWIqGxUaeKO3gN4sHs7NseP210OVVCXswVbQ53YGtyE57xruXICVdzXNnlx/+UlJqHRvMbQNNEYDHsRERHNHD9Hieavh399K47/8lj5D6wAji94oC4Z7TYwkwHhuHIOYbWrrKVpQuKTwQhWTRKYdpww4dubh+BS9jTsvOHDny+9EX+7+HrEL7HkIAPUNJ/lAwKx63QUApMPorwcd+GpSAPy0uYBFymgwoAq9aEw9fBtFQYUWfxnBqxpPpOpoTD12CC1jFqwhkPWyHC4gmi6nM0uBDuCo12p14UQaA1Ac04ctK1W4eOD2PO1p3Fi1zwO0qiAeoUOfYsDSmjiVS6qPSwtYSEtBpFQepAR4boJQk/GISysdWXR4c5ilSuLaTUGNyWMMxacp0w4TltTmnhdRxlzwtDk6v+16k78zeLrYTFQRkR17Lro2/hy93bc0v8iNMkL5/VGAjjYsAJbg53YFtyEww3L7C6J5jmGpqkUhqaJxmDYi4iIaOb4OUo0P53cewJbP/uvc3Js/QYn9C2z6yCaFoPoU48UjyLOclTREBbuCEWw3HnpEKvrjQIany9wAJMAAO84Q/j2so/hHxa9D1nFmNZjLwSo7+g9gI8Ovgq3xTadVP+kAFIdKpIbNEj90u+k4byKRwa8OJOb3mvLNlIUd6seG7a+qJO1Ao0Ba5pXZFaOBqlLdKxGksMZRFMhFIGmVc0IrQ8hMNyROrQuhIZFjVUZtM3Gsnj+u8/i4D+8DCs/T4MzbgG904C20YDw1GZYUkIijyQSSg9SSh8sUbC7pIpwKRZaXVm0uzNY6cxBnc5LLCfhOD0clD5jQUxhAS8GpeuPBPBPC34Fv7/mbvQZvhI7XDRDn4ioTqzI9OFL3U/gV889DZ85yWoUVNVMCOzztWNrsBMPBTfihKvF7pKIRjA0TaUwNE00BsNeREREk8vGsggfHxz6dWwQg12DiLwTRi6fw6LLFyPQHkSwI4RAWwDepT6ISZZWJ6LaZRUs/PijP8Dg0YGyH1tt1eD4tGdWx8ghgR7tNcixo46zHF10CAt3tkSw1HGJwLSUaHi5AM/hKYx2Ut173b0E31x+M/55wXUwxQSd4qYxAMoANc03phuIb9GRXT5xp0UAsCRwIObGM9EGmPUUIxkOWJcKVDNgTfORzEvI2GiYeqRjdcSCFWaommgyDq9jtCP1cJg60B6E7rKnK7W0JI78/DD2fXMv0v0pW2qwmwgq0Lc4oF6hQ0yrLXH1MJFHUulFUulBXkzy/1gniV+3YqLNPdRRerkjh+lc/hQZCccJE85TFozzFgQ/uua1Q55luL/1Xuxr6rC7FCIi2zQU0rjv/G58qfsJrMn02l0OTVFG0bGj+XJsC3bi4cA16De8dpdEVBJD01QKQ9NEYzA0TURENERaEvFzcYS7BhHuGsTgsYGR28ne5JSPo3t0BNqGQ9TtweFAdRAuv3sOqyeiSjn4Dy9j91d2lf24wifg/GIDhGu0u9Z0lx4uIIse7VWYYkygdJaDs07FwqdDYSxyXKJbliXh3ZeH63hxd7Q6GRemaXjOuxbfWH4zHg5unLPnYICa5pPMMgXxLTosz6XfTXtzGh4Z8KI3b0/4y1aXDFgPdbRWLvzOgDXVKZmVkOGhAPXQ7ybk8G0Zk0NfyoiomACaVjUj2DHUjfpCoLpxqXdOu1KfffEMdn9lF3pf65mz56hmymoN+hYD6pqJv7NM9zy4kiQkMiKMhNKDtBjEfEj+Nqgm2oc7Si9z5KfV+FdJWnC8Y8F50oTRX///VjS5mOrCV1Z+En+19MMTT7AmIppnhLRwc/9LeLD7cfxK9E1etahCUdWF7YGrsTXYicf9VyGhuSZ/EFdMIJsxNE2lMDRNNAZD00RENN8UMnmEj4eHgtHDoehw11AX6UJ67pbQdIfcCLSHEGwPItAxFKYOtNnX2YiIpi89mMIP3vd9ZGPZ8h5YARyf90Bdqo1smu5AsYUCerTXkBdTn+QxGbdi4a6WMFqMid8bRV7C93QejrMMTM9XEsCO5svxjeU34+nmyyr63AxQ03xgaUByg4ZUh4pLtfMzJbA32oADMTck34FLkxgOT48PVBf/eSiEzYA11QNZkEMdqseGqgeHA9VRC+AiIURFjEZjqBv1cEfqC12pDY8xq+PGz8Wx70/34K1tb5Sp0hqiAurlOvQtDigtE4ckqzksnUcKCaUHSaUXlrjECkx1wqeaaHdn0ObOXnrFqYtJCTUq4XjHhOuUBY3D8TRMAvjnlnfh99bcg/OO5hI7MFhGRAQA18TfwQPdj+NTvc/CkDxZs9N5w4eHAhuxLdiJXc2XIa9okz+IqIowNE2lMDRNNAZD00REVI+klEj1p0a7RncNIjzcOTp2JlY9nbYE4FvRhGB7CIH2wEh36qaVzVA0ZfLHE1FFPfXfd+DQPx0s+3H1Dzqhv8sx48dLSPSpR5BRwmWryaOYuHtBGEF94ouzSlqiaUcO+mDxmyoD0/ODBYFtwY34+opb8FLjarvLgcfM4KaBg7iz9zkGqKku5f0CsXfpKAQu/R2xO6vj0QEvwgUO5syKxFC4ejhQXbqTtcGANdU0aUnImCzuTj04Gq5G/ecCiabMt6JpJEQd7AghtD4E71IfxCUmNAFDE/df+tsX8eL3DszpRP2q5BbQNhrQNxoQDbV3jctCAUmlD0nRi5wSv/TOdXAS3KwV0O7Oot2VufRKUxeTEtqAhOO4CdcpE2r55nFTnTjiXoIHWr9Q8UnWRES1bFE2jN888yR+49xOBPMJu8uZN7qcLdga6sTW4CY8510LKWrvOyzRBQxNUykMTRONwdA0ERHVMjNvInoyMq5r9GDXIHLl7gRbQapDhX9NAIGO4HBn6qEwdcPChqrtukNU7/rf7MNPbvwhpFXe00lljQbn3Z4ZP15CIqwcR0I9V7aaGlUTd7eE4b9EYFqNWWh6Mg8twdPr+SYvVPxkwbvxrWUfx5ueJXaXU5LbzOBjDFBTHZICSHWoSG7QIPWJvxPmLOCpSCNeSbhQ8wmeWjASsB4fqB79sz7S5ZoBa6oVMjGmO/VwoPrCbaT5HZBI9+hDk+DXhRAaDlMHOoJwNDogpcSxx97G3j/ZjXh3zO5SK0oEFehbHFCv0CG02vrMk5DIiigSSg/SYgBSWJM/qIYFtAI63Bm0u7OXXGFqHEtC77Xg6rLgOG1Cqd1LsDSHEqoDX1txO76z9KMosDsnEdGMOM0cPtezFw90P451qbN2l1N3JICDDSuwLdiJbcFOvNaw3O6SiMqGoWkqhaFpojEYmiYiolqQiWYQPjaI8PHicHT0ZARWob4HMMZy+JwItA8HqduDQ6HqtiAcPqfdpRHVNSkl/v3un6H72dNlPa5oFHD+egOEe7RjwXSXJI4pZxBR3ylbTT7VxN0LBtGkTfzeqvVZaN6Z48DoPJNSDPz9ovfjz5Z9DKecQbvLmTIGqKkemW4gvkVHdvnES9wDwPG0gccGvUiYl96PKqhkwHo4UC1HO1szYE3VTmaGO1QPmpARC9ZwoFqGLcg4h19ofvMu88FoNNB/pM/uUipKWaVB32JAXatPuM90z3crpYAMEkoPkkovTFHPJ7oSLfpwR2l35pIrS41TkDDOWXAdN2F0W1CmkLGugwbcNAMSwL+FNuM/r/0czjj8JXaQQBW+DxARVbuPDLyKB7q348Ph1/j5OgsmBPb52rE12ImHghtxwtVid0lEc4KhaSqFoWmiMRiaJiKiamGZFuJnYiW7Rqf7U3aXV9UaFjci0BZEsCM41OWoPYjmtX5oDnbxICqHY9vfxqP/8aHyHlQAjs95oC4ffZ1OdwA5JQbQr75RPAo5i1HJZq2Au1vC8F4iMG10m2janYeYZytLz2cRzY3vLb4ef7H0o+gzfHaXMysXAtR39B3AjQMHGaCmmpdZpiC+RYflmfiNP2MJ/HKwEUdSTjC2UmMkoEArHaiWFwWvoUOAy8ZSdZB5OS5IfaFjtYxawPyZ90xU/1RAvVyHvsUBpWXiSVrVGJa2YCIl+pFUepBV6rkbuMRCY7ijtCuL5ikEpUcyrTkJR7cJ13ELxjkLdd54m8rgbddCPNB6L3b4r7C7FCKiurU+2Y0Huh/HZ3qegcvK211OTcgoOnY2XYZtoU48HLim5q9xE00FQ9NUCkPTRGMwNE1ERJWWT+UQ7goPBaOPj4ajw8fDMLNM4ZWLUAWaVzUj0BEaCVQHOkLwLfNBKNU1UEVUzQqZAv7pQ3+PWJmXVNbf74D+npl3ic+KOHrV14qXC55FYDqgFXD3gjAa1IlHQZ1HC/A+W4DgGfW80KN78efLbsTfLr4eMc1deqca7hDFADXVC0sDkhs0pDpU4BLf8d5IOvDLsBdpi8HaeqVIrXSgWhoMWFPVkJaEjFqQg2OC1GNC1eAlAaLa4BbQrjGgdxoQDbXzmSIhkRNxJJQepEQ/pJhGp+WaIrHEyI90lPZdYmL0yCOGT+1ExoLzpAXncRN6r+SUO5qSlGLgT1fcgm8v+xhyysTd5omIqHyCuRh+4+xO/ObZHViUi9hdTtWJqi5sD1yNbcFObPdfhYTmmvxBNXytm+hiDE1TKQxNE43B0DQREc0FKSWSvUmEjw1isGugqGt04mzc7vLmNc2lIdAWRKA9iGDHUFfqQHsQnpDH7tKIqtLzf/kcnv3WM2U9prJKg+Me94w7bRWQwXntVVhiTCeJWQSmW/Q87moJw61OcKosJTyHTDQcZIplPjjhDOLbyz6Ov1/0PmQVo/ROdXYBmQFqqgd5v0DsOh2F4MTBpYSpYPuAF10ZRwUro2qkSK0oUD2uk/WY+xiwpkqyYiW6U4ctWIMmkLW7OiISAQXaFgPaFQaEXjvnAwVkkVR6kVR6URDpS+88i3NrOwlILHPk0e7OoM2VReNUg9KQUFISzncsON8xoQ9yCJ2mTgJ4OLAR/6n1czjpDJXYob6uHRARVSPdKuDTvc/iwe7t2JA4aXc5turRvXgouBFbg5uwq/ky5BWuxkvzF0PTVApD00RjMDRNRESzUcgWED0ZweCxwTHB6AFEjoeRSzBwVEtcARcC7aGhjtRtQ4Fqf1sAhmeCwBzRPJA4H8cPP/D3yKfKuMxdg4Driw1F3bims1SxhQJ6tEPIi1RZyllo5PHpUBiuiQLTlkTjgQLcbxd34KrRcWS6hNfdS/Ct5Z/ATxe8C6aYYGnteTDgyQA11TIpgHSHivjVGmBM/Fo9mHBhV7gBOckwLE1uNGCtj+lkfXHAeug2A9Y0l2SqdHdqa9ACkhzyIZpLyioV+hYH1LUTd5CdznltJUhYSIkBJJVeZES4Lk9gFUgsd+bQ7sqizZ2BZ6Lz+jEuBKXVuISzy4TzHQtanO+hNH1dzhZ8ufXz2B7YYHcpREQ07L2RI/jy6cfxsYGXoWJ+fL53OVuwLdiJraFOPOtthRS8LkEEMDRNpTE0TTQGQ9NERDQV6cEUwl1hDHYNIjzcOXqwaxCxU1FIi1+t6plvuW+4G/VwoLo9iKZVzVD1CQJ1RHXkiS8/hjd/caR8BxSA4zMeqCtHOxxMZ2BZwkKfegQZJVKWchYbOXyqJQKnMsH7eEHCtycP5+niDlUMTNeXA41r8I0VN+OhYKfdpVQdBqipVpluIL5ZR3bFxN/XIgUFjw74cDrLCXJUPkKqxYFqqUMd7lqtoDhszYA1lZPMydEg9aAFGbFghc2h2zGJeZIXICovFVAv06FvcUBZMPF3imoKS0tI5JFEQulBSumDJepvtSQVEiudObS7M2h1ZSeeAH0xKaGGJVzHTDhPmFAnabhNNJG0ouObyz+Bby7/ROnVqebBZGsiomq3Jn0eX+r+Je49vxuNZsbucspKAni1YQW2BjuxLdiJ1xqW210SUVViaJpKYWiaaAyGpomI6AKrYCHWHR0KRh8bHAlGh7sGkQnzSjqNUg0VzWv8w2HqIILtQQQ6Qmhc3Fg1A2VEs3XupbP42e0/KesxtV9xwHifc0aPlZAYVI8hqfSUpZZljhzuDEVgTBCYFhmJpl05GH3F9zMwXR8kgJ3Nl+Eby2/GU82X211OTXCbGdw0cBB3MkBNNSSzTEFssw7ZUPqdW0rg+bgbeyINMPnuThU2FLAeE6iWRsmAtQIdCjhhk2ZOmhIyMtqVemyXahmxAHPyYxDNKy4BbaMBvdMoWiGpmpnII6n0Iqn0TL4qUw2e1GpCYrUzizZ3Fmtd2YknPl/MktAGLLjeNuE8aUGZwiJaNfjPQxX0mP8qfLn1CzjuWjD+ToaliYiqjq+QxK+eexpf6v4lVmT77S5nxkwI7PO1Y1twI7YFO3HC1WJ3SURVj6FpKoWhaaIxGJomIpp/svEswseHwtDhY4MjtyMnIjBzHC2kmTMaDQTahgLUwfbgSGdqZ5PL7tKIpkVaEv9y64/R8+r5sh1TWaHC8VnPjCcWRJXTiKony1LLSmcWnwxGoE8w/q0kLDQ/mYcWY2C63lgQeCi4EV9ffjNe9K6xu5yaxQA11RJLA5JXa0itUwGl9Lt4X07FIwM+9OT1CldHNDUTBaxLha0ZsKbpkFJCxoa6VA+Fqs2iUDX4EU/ziAgo0DYb0K40IPTqP/OTsJAWYSSVHqRFGBD1NfSrCwtrXDm0uzJY48pNOOF5HFNC7xkOSndbEFO41MtzfZrMSUcQv7P2s9gW2mR3KURENAOqNHFb3wt4sHs7rosds7ucKckoOnY2XYZtoU48HLgGfYbP7pKIagpD01QKQ9NEYzA0TURUn6QlkTgfL9k1OtmTsLs8mmc8CxpGAtRDnalD8Lf6oTkZzKHqdOTnh/Hk7z5evgO6BVy/3gDROJpSns4SxknRhwHtreKNMxzVXOPM4rZQBNoEj9UGLTTtyHGp3jqTFyp+2vIufGv5J/CGZ4nd5dSVCwHqO/oO4MaBV+GxsnaXRFRS3i8QvU6HGSw9Y8aUwL6oB8/GPJCMzVANuxCwVuRot2oVF8LWw7dHtjFgTZcmE8MB6shol+oLoWqkOMxE9UFZqULf4oDaOvE1mumcv861PFJIKD1IKr2wxBRaJ9cQh7CwxpVFhzuLVc7shBOdx8lLGOcsuN404Thv1Vt+nGyUFRr+bNlN+JMVtyKtOsbvwO7SREQ1Z3PsGB7s3o7b+16ALqurkVZMdWF74CpsDW7Cdv9VSGhTaMjEzyKikhiaplIYmiYag6FpIqLaVsjkEX4ngnDXwFAwejggHXknjHyqvgYOqL4IRaBpVTMCbYGRztSBjhB8y31Q1NpY/pXqUzaexQ8/8H2k+iZZ0ncaHHe7oa4ZHYCezoBzVsTQo75W3DVrhoHpNlcGtwSjUCd4rHHOhO+p/JSW7KXakFZ0/P2i9+Pbyz6OU86g3eXUPQaoqdpJAaQ7VMQ3aMAEHSTPZjU8MuDDYEGrcHVElSekMiZEPaaT9diw9fB9AioEJxTQGDIjx3SlNke6U8uwBRnjEBRVORVQL9Ohb3FAWTDxBJJqCUtbKCCp9CEpepBTJmkGUWNtk52KhVZXFu3uDFY6cxNOcL6YyEoYZyy43yjA6Od7DpXfk82X48HWL+Bt9+LxdzKgRkRU85ZmBvDbZ36JL57bheZC+cZCpqtH9+Kh4EZsC3ZiV/NlyClstkRUDgxNUykMTRONwdA0EVH1k1Ii1Z9C5PjgSOfoC12jY93RocEAojqhOTX4WwMIdAx1pB7qTB2Eu8VTFQN1VP+e+foevPS958t2PO3dDhgfcM7osXmk0aO9CksURjfOcAB4nTuDTwSiUCZ4rPO4Ce++PIQ1o1KpykRVF7635AZ8Z+lHuXShTRigpmpmuoHYFh25ZUrJsEPeAp6ONOKlhAs1lToimkNCKsPdqo2SAeux9zFgTbIgi7pSy/CYTtVRC+B3brKLS0C7xoDeaRSthFSNJCQyIoKk0ou0GICso5NVt2Kh1ZVBhzuL5c7chBObi0gJkQGcp024jpjQo7wgTHOj2+HH7675DP615Vq7SyEiogpwmxl84fxe3N/9ONrS5yvynMedIWwNbsLWUCee9bZCiur+XkpUixiaplIYmiYag6FpIqLqYeZNxE5FhwPRAyMB6XDXILIxBm1ofnM2uxBoDwwFqYcD1f62AByNJZaGJJqhyIkwfnTDP8LMlWdZOmWZCsfnPBATJZUvwUQePdohFER61nVc7knjJn+sdGBaSrhfN9H4UqHEnVRrenQvvrP0RvzNkusR09yld2JHqIpjgJqqVWaZgth1OqSr9HvCuayGvryGtKUgZSpIDf+etsTI7ZwUYLCaqNjFAeuSYWsGrOctaUnIqCzuTj04HK6OWABXfaE5IPwKtC0GtCsNiAlWm6gWeaSRVHqRVHphivr53tygmmgb7ii9zJGfcEJzESmhJCWcJy24jhSg2dcEkuaBvFDxnaUfxddW3o6kWmLyP68lEBHVNSEt3DRwEA92b8cHIkfKepYqAbzasALbhjtKH2pYUcajE1EpDE1TKQxNE43B0DQRUeVlo5mRTtGDXYNDHaSPDSJ6MgKrUD9dU4gqoXGpF8H2IAIdIQTaggh2BNG82g/VmHh5WaKJPPzFX+D4k13lOZhLwPnrDVC8o10Sprq0sYSFXvUwskps1mVc5UnhxkC8+PgXxrmkRMMLBXjeKE9InOxz0hHEt5d/DH+/8P3IqEbpnTjAWRUYoKZqY2lAYqOGdLs6o/eIggTSYwLVKWs4VH3h9kX3ZSwByYAo0SgpRrpVqygOVCtSL7qPAev5wYpbxV2qB0dvI8OhLZoeZYUKfYsDatvEy5xP9Tx1LlkwkRL9SCo9ZTkPrhZe1US7O4N2dxZLHVOcESEl1JiE87gJ11smVJ4uUAU83bQOD7TeiyOepePv5LUEIqJ556rESTzQ/Tju6tkPh5xZsxUTAvt9bdga7MRDwU6842opc5VEdCkMTVMpDE0TjcHQNBHR3JCWRPxMrCgcHe4aQLhrEKk+tgUhmkuKpqB5jR+B9iAC7UEEO0IItgfRuMQ7o46/ND+c3HMCWz/3r2U7nuPTbqitEw9MT0RCYkB9Gymlb9Y1bGxI4Qb/BIFpU8L3TB7OE8WTdSTYL7SWHHEvwbeWfxw/bXkXCopWeicOcFYtt5nBjQOv4s6+5xigJtvl/ALR9+uwGud2SVQpMdKpenzYWkHKHH+fyU8moiFjAtYKdGjSARUOaNIBDU6o0jEcruZrpl7JtDXanTpswRocvS0THPaiYQqgXqZD3+KAsnDiCeV2h6UlJLIihqTSg5QYgBT1MZm3SSugfbij9GLHFENGloQalXAdM+F824Q6hYfx3J3K4ZzRhP9vzT346YJ3210KERFVoQW5CP7jmR34D2d3oiU/+cS2rNCws/kybAtuwsPBa9Br+CpQJRGVwtA0lVLVoemHHnoIDz/8cNE2r9eLb3/72wCGLmI8/PDD2LNnD1KpFFatWoV77rkHS5YsGdk/mUzin//5n/Hqq68CAK666ircfffdcLsnWJqX5jWGpomIZiefyiF8PDwmGD3863gYZnZms2+JaG7oHn24G3VoTKA6CJef35PnOzNv4ic3/hCDRwfKcjztWgPG9a4ZPTainERMPT3rGjY3JvHB5kTRtgvZWZGTaHoqD+M8A9O16vnG1fjG8puxLbTJ7lKoTFxmFjcNHGSAmmwlASSvVJG8SsPU1oyvjKwligLVKfNCwFoZ8+fR+3JSgJ9oNG9JAQ0OqNIBDQ5o0ll8GwYE5nZyBNlD5iRkpDhIPRSwNiGjcuhNnuqbS0C7xoDWaUCZ40lQs1FAFkmlF0mlBwWRufTONXKSGtAKIx2lFxhTD0prAxZcR024uiwILj5IFVQQCv5qyYfxlZV3IK6VuH7FyddERDSGw8rhnp79eKD7cVyRLB67iKkubA9cha3BTdjuvwqJUp8rF+PnDNGcY2iaSqn60PQLL7yA3/3d3x3ZpigKGhuHfpC3b9+ORx99FPfddx8WLlyIhx9+GMeOHcMf/dEfwel0AgC+853vYGBgAF/4whcAAD/4wQ8QCoVw//33V/4vRFWPoWkioslJKZHsTSJ8bBDh48Wdo+Nn4pMfgIiqmjvkQbAjiEBbEIGOoVC1vzUA3TX9LsFUmw7+w8vY/ZVdZTmWskSF4/MeCHX6F/0SogeD2tFZViDxLm8S721KFm8dvg6pJCWaduagh+VFj6qJseh5TQLY1XQZvrHiZuxqvtzucmgOXQhQ39F3ADcNHGSAmirOdAHR9+jIL1JqchCrIFGii7UYvX3RfRlLQPJTkOYLiZHu1OpwkLroNhwMVdchaUrIqAU5eFGn6uHfUR8Nfuct4VegbTagXWVA6NX5eSZhISUGkFR6kBGROjj5lAjpBXS4s2hzZRAypvgiMiX0PguuNwtwnuS3D7LHPm8b7m+7F4caVthdChER1aAPDb6G3z7zJHoNL7YGO7Gr+TLkFI6lEVUbhqaplAnWq60eiqLA5xu/TIGUEjt37sSNN96IjRs3AgB+9Vd/Fb/zO7+DAwcO4H3vex/OnTuHw4cP4/d///exZs0aAMDnPvc5fPOb38T58+excOHCiv5diIiIaomZMxE5UaJrdNcgcomc3eUR0RxJ9SVxqi+JU3tPjm4UgG9FE4LtoaEg9XBn6qaVzVA0hgjqSXowhef+bF95DuYUMG53FwWmp7rkcUZEMKgeK9447SSzxHt9SbzLVzowrUYsNO/IQU2OfyQHa6uXBYGHg9fg68tvxgvetXaXQxWQVh34t5Yt+LeWLQxQky3UNOB/Mo9ci4DpVWA5AMshIId/H/oFSKeAZQCYwUShuaQJoFGz0IiptWyUEiOdqseHrUc7XI+9z+QnJ9UqAZjIwhQTf54oUh8JUA91qR4OVg/fVqBWsGAqB6EKCL8K+NVx/3tSSsi4hBws7k49EqjmV4+qpaxQoW1xQG3VJjznnOr56FyQkMiJBJKiFymlD5ao9RX5JBYaBbS7hjpK+/UpBqXzEkavBdeRApxnq7anF80DvboX/2XN3fjhwvfaXQoREdWwnf4rsNN/hd1lEBHRDFR9aLq/vx+/+7u/C03TsHr1atx2220IhULo7+9HNBrF+vXrR/Y1DANtbW3o6urC+973PnR1dcHhcIwEpgFg7dq1cDgc6OrqYmiaiIgIQDqcRvjYaLfo8PGhoHT0VATS5MVrIgIggeiJCKInIuh6YrTzr+pQ4V8bQKB9OEjdEUKwIwjPggbbBiJpdp799j5kY+VJAhifcEHxFYfqp/JzkUcKfeobgBjzGTSDwPQHmxLY7E0Vbx0OTOs9Fpp25aBwDlDNyAsV/9JyHb65/BM44llqdzlkEwaoyU5GrwR6Jw8EWTogh4PUI4HqMX8eue0UMIfvg4aq6WItBOBWJdyqCUwxAJW1RFGgOmVeCFgrY/48el9OCnCKEtUKS+SRE3nkUHplLUVqpbtUD4eqBVQI/rzXDCEEhFcAXgVYOf5+mSzdndoatIAUr6FVnAKo63XoWxxQFk08geFCWNqO6xQmckgqfUgqPciL1KV3rvoljyQWG3m0u7PocGfg0yafkCWlhJIH9HMWPIcLMPr5OiF7mRD4v4s/hP+x6lOI6J7xO1y4cERERERERHWtqkPTq1atwr333otFixYhFovh0Ucfxde//nV89atfRTQaBQB4vd6ix3i9XoTDYQBANBpFY2Nj0YUQIQQaGxtHHj+Ro0dnuww01Sr+3xNRPZKmRKonheSpOBKnE0ieSiBxOo7k6QRyUSbGiGhmzKyJvtd70fd6b9F2vVFHw0ovGlc1onGVF40rvWhc5YXewGXJqlnseBSv/eTVshxL22RAa5/+/7eJHHq11yHFRSGtaQamP9wcxzWN6eKtw+NejpMmfHvzuPgpqDqlFR3/sPD9+Pbyj+GkM2R3OVRFGKCmaqXkAeQl1AQwlH6anFQwLlxd3M166D7TKWA5h7ZBB6BUR6DDoUg4FBNNU7zSXJAo0cVajN6+6L6MJSCrO0VG85glCrBQQF6UWL4EgJDqaJfq4Y7VmnQOh6sdUKAzVF1DhEeB6lGAEnP4ZFYWB6nD5lC4etCCjDEoWlZOAe0aA1qnAcU7+epXlQ5LS1hIizCSSg/SIlw8IfhSqvCtQEBiqSOPdncGba4svFMNSmcB44wJz2sF6JcejiWqmAONa3B/2314uXHVxDsxME1ERERUd/r7+nH06Hm7yyAbtLa2TnhfVYemr7iieBmD1atX4w/+4A+wf/9+rF69ek6f+1L/aFS/jh49yv97IqppuUQO4eODxZ2juwYRORGBmWM6jIgqIx/PI/zaAMKvDRRtb1jciEBbEMGOIILtIQQ6gmhe44fmqOrTknlBSol//+8/AyYf/5yUskiFfr1z2o+zYKJPfeOSy6NPRkDio/4YrmrIFG2/EJh2vVlA44FCNY5F00Wiqgt/s+R6fGfpjeg1fHaXQ1WOAWqqdcIC1DSA9IVQ1dTCVZaBceHqi7tZW87h3x0C0gCg2v8pqAmgUbPQOMUvHlJipFP1+LD1aIfrsfeZ/LSnKiGFiTxSE3aYFVIZCVCPDVNrcEKVDqgwGKquEcIhIBaqUBaO73gsCxIyMqY79eCYjtURqyznYfOBaFagbTGgXWlAGNX3usghiaTSi6TSC0vk7S5nxgQkljtyaHdn0e7OwqNO4QdUSihpwHHKhPtwAVrpeSTFD0FV5sSpDvXrDfhvq+7C9xd/wO5SiIiIiMgGwVAQra2NdpdBVaam0glOpxOLFy9Gb28vNmzYAACIxWIIBAIj+8RiMfh8QwOqPp8P8Xh8ZOktYCgMEI/HR/YhIiKqNVJKJM7FMdg1iEjXhXD00K/E+YTd5RERTShxNo7E2ThOPv3OyDahCjSvakagI4RgexCB9iACHSH4lvkgqqR74nxwbPtRdD93evYHcgDG7W6IMYGssedjE5GQGFDfRk4pvez5VAhIfCwQw+WeEoFpSHheLqDhNU4gqna9uhffWfpRfG/JDYhp7tI7cblcuoSLA9Q3Dr6KO3ufY4Ca6pKSA5ScBOLAlLtaqxgfrr64m7ULkM7h+wwxdAXZxvddIQC3KuFWTUCf2md51hJFgeqUeSFgrYz58+h9OSnA6BbZQQoLBaRREOmJdhjtVH2hS/XY2zAgMHmnXbKX0AREUAWCKi6OVEtLQsbkaHfqQauoYzVqN3tbNspyFdoWB9Q2bcJzy6mcd84FCwUklT4kRQ9yyiTXZas4JaxAYqUzh3Z3Bq2uLNzqFL5XSAklKeE8YcF9uAB1ml+1q/SfguqIBYG/X/R+/MHqT2NQLxGS4bUFIiIiIqJ5q6ZC0/l8HufPn0d7ezuCwSB8Ph+OHDmCVatWjdx/9OhR3HHHHQCANWvWIJvNoqurC2vXrgUAdHV1IZvNYs2aNbb9PYiIiKaikCkgciKMcNcgBo+NBqPDxweRT3HEhIjqgzQlBo8Nvc8dfeStke26W4e/LTDcmTqEQPtQh2p30GNjtfWpkMnjmT9+uizHMj7mhtJcHNqYysB1VDmJtDIw6X4TUSBxczCKDnfxKK2UgJAS3v15uLqKu2NV8Xj1vHTSEcSfLbsJ31/0AWRUo/ROFwY0OahJU5RWHfj30Gb8e2gzA9REw4QJqClATUlMOWgtAGkUh6sv3DadAtIFmK6hrtbSISB1AakDsHECnEORcCgmmqZ49bsgUaKLtRi9fdF9GUtA8psEVYKQKCCDgsig5CeXxEh3anU4SF10Gw6GqqucUAREkwCaFKirxr9pybgF60KX6uFA9YVQ9egKBXVIAdT1OvQtDiiLxnfvvuBCWLqSgWkJiYyIIKn0ICUGADHF/4cq+9jQhMQq51A36bWuLJzK1ILSakzC+Y4J1xETKi9RU5V6qWElvtR2H17wrp14J15bICIiIiKat6o6NP3zn/8cV155Jfx+P+LxOB555BFks1m8613vghACH/rQh7B9+3YsWrQICxYswKOPPgqHw4EtW7YAABYtWoTLL78cP/rRj/C5z30OAPCjH/0IV155JRYuXGjnX42IiAjA0IX99EBqKBg9pmN0uGsQ0dPRqY5hExHVnXwqj56D59Fz8HzRdlfAhUB7CMGO4NDv7UH42wIwPBOELGlSL//dS4h1x2Z9HG2jAW29Pu3HJcR5xNTuGT+vColbg1G0lgpMFySadufhOMPAdLV6w70Y31r+Cfyk5V0oKBNcomBYmsqAAWqimRMSEFlAyV44QZ3aiaqlAXKkk7UYuW26AdMtIF2jna6lDkhdAApseb/XBNCoWWiENfnOGPpoutCpenzYerTD9dj7TH77oLkgABNZmGLizzJF6iMB6qEu1cPB6uHbyrjex1RNRKMCtVEBlo2/T6blUHfqcHF3ahm2IOM1elHRCWgbDGibHFC8kwf+KxmWziONpNKLpNIDU+Qq9rzlpAuJ1c4s2t0ZrHHl4JhqUDoi4Tpmwv2WCcEFnKiKhTU3/ueqT+FvFn8IUnDSEBERERERlSYikUjVXjn5v//3/+Ltt99GIpFAY2MjVq9ejVtuuQWLFy8GMBQ0e/jhh7Fnzx4kk0msXr0a99xzD5YsWTJyjGQyiZ/+9Kd49dVXAQBXXXUV7rnnHrjdEyzzS/Pa0aNH0draancZRFSHzLyJ2OnoaDh6uHP0YNcgstGM3eUREdU833IfAh2h4c7UQQTag2ha1QxVZwDgUuLn4vjhB76PQrowq+OIBQqc9zVAaNMbsE6LMPrU14sTzNNINGtC4vZgBKtdxQPWUgIiK+HfkYM+UHzKy8B0dXixcRW+sfxmbA12ciCTbHUhQH1H7wHcNPgKGkwGqInsJAXGdLMeDly7hoLWllvAdAlIp4BlANIQkBoAFTUxsSZriaJAdcq8ELBWxvx59L6cFOC3FqoERWqlu1QPh6oFVAj+LNYcmZfjgtQjtyNW1TVqEM0KtM0GtKsMCKN6ft4smEiJfiSVHmSV2U82toMhLKx1DXWUXu3MQp/K6ZcloQ1KuN424eoyIaY2p4jINhYE/mnhr+C/rL4LfYZv/A4XJmMTERER0bzztU1e3H95o91lUJWp6tA0UaUxNE1Es5WNZhA+HsZg10BR1+jIyQisPK8uExFVkmqoaF7jR6B9OEjdFkSgI4TGxY0V7UZVzR5/8FG8tfWN2R3EAJxfbIDiHw2oX1gi+VJySKJHOwQ5tk3VNBLNupC4IxTGCuf49YCVuIXmJ/PQarW7Wp2SAJ5uWo+vL78ZO/1X2F0O0TgMUBPVLkvHSDdr0ylgeQDLI2C6BSzn0C9pAJY+NmiNqg7PFCRKdLEWo7cvui9jCUgGW2kOCKmOdqke7litSedwuNoBBTpD1TVGWhIyclGQenD4dsQCZjendlqU5Sq0zQ6o7dqE55BTOb8sJwmJrIghqfQgJfohazAx7BAWWt1ZdLgzWOnMYUrzm00Jvd+C600TzpMWBE+nqUa86lmO+9vuxX5fu92lEBEREVEVYmiaSplg7VsiIiKaiLQk4mdiCB8f7ho93Dl6sGsQqb6k3eUR2UMf/n18dpHINmbORP8bfeh/ow9vjdlueB1DAer2IIIXAtUdITh9TttqtcPZF8/MPjANwLjJVRSYBiZfItlEDn3akRkHpg1h4c5QBMtKBKbVQQv+J3NQuJBD1bAg8EjgGnx9xc143rvW7nKIJpRWHfj30Gb8e2gzA9RENUbJA0peAglAn2L7VKkMdbW2nGKoi3UDYLoVWO7hbU7AMgSkTUFrTQCNmoVGTC2sJyVGOlWPD1uPdrgee5/JoCtNgRQm8kghL1Il7xdSGQlQjw1Ta3BClQ6oMBiqrjJCERB+FfCrKLU2kxW7qDv1oAUrbEKGLaAcX4kUQF2nQ9tiQF088TDlhbB0pQLTBWSRVHqRVHpQEJOcUFbhEkYuxUKbK4N2dxYrnDmoU6mvIKH3WnC/YcJxZvKgdBX+tWkei6oufHXVJ/FXSz4MU3ClOSIiIiIimjqGpomIiCaQT+cROR4eDkYPdY4e7BpE5HgYhUwFW64QVYMGAcWnQHgVCJ+AGL6t+Ib/7Bpa21MmLFgRa7RjUWR06VcZY4saqg65WBbnXjyDcy+eKdruWdAwFKBuDyLYEUKgLQh/awCas/5Om6Qlsfuru2Z9HHWDDu1yY1qPsWCiTz0CU1w02j7FkVeHsPDpljAWO8Z/FutnTTQ/lYfgx3RVKAgF/9JyHb65/BN43bPM7nKIpoUBaqL6JyxATQNqWgLhC+cqlw4oS2Ckm7XZCFgNCkw3YLoFpFPAcghYBiB1AJqArGDQWgjArUq4VRPQzckfACBriaJAdcq8ELBWxvx59L6cFGBcji4mhYUC0iiI9EQ7jHaqvtCleuxtGBBQKls0XZLiVQCvAqwYf59MjelOHbZgDY6Gq5Gc5LqPE9A2GNA2OYaeYxKVCEtLWEiJASSVHmREZOpvcVXyVuhRTLQNd5Re5shDmUpdeQnjnAXPkQL0nsnXKBgblK6SvzbNcxLAT1vehf9vzT0472gusYOs6tVEiIiIiIjIfvU3+k9ERDQNUkqkepOjHaOPD46Eo+PdMbvLI6oMFUNh6CYxJgg9/Ms7tE1MaR1PQDQoUBsUYOn4+2RhePnXyOiSryPB6ogF5Mr89yKapmRPAsmeBE7uPjGyTSgCTauaEWgf7Uwd6AjBt9wHRa3dgf0j/3oYvYd6ZnUMEVJgfNg1rcdISAyobyGnJGb0nC7Fwl0tYSwwxqeiHccL8D1T4BLCVSCt6PjHhe/Dt5d9DCdcLXaXQzRrDFAT0QUCgMgCSlZCjwLA1MLJloahTtaNCswGAWs4aG0NB62lgaGO1ioq2tHaoUg4FBNNUxwlKEiU6GItRm9fdF/GEpg8jkd1T0gUkEFBZEo3KZaACqO4S/WY2yocUEr2QyY7CLcC1a0AS8bfJ3PyoiD1UHdqmZHQrjSgXW1AGPa/J0hI5EQCSdGDpNJXvAJSDWhUTbS7M2h3ZbHUkZ/Sx4XISRhnLLiPFKD3T++d2f7/MaJRr7uX4P62e7Gnaf3EOzEwTUREREREkxCRSIRDykTDjh49itbWVrvLIKI5YOZMRE9GRsPRXQMjt3NxJjWpzrkEhFdcFIYe6hCt+BSIhuoIfsrkmDD1RcFqGZeY4irbRBWhOTX4WwMIdAQRbA+NBKrdLZ6KLR08U9l4Fj/8wPeR6iu9vPaU6IDz1xqgBEfDCxeWTr6UsHIccfXsjJ7SrZi4uyWMkHHRgLaUcL9uovEltpe2W0x14W8WfwjfWXYjeowmu8shmnMuM4uPDr6KOxmgJqI5YOmA2SiGfnmGflkuAcuJ0aC1JiA1AMr/396dxzlVHuoDf95zsmcmszMMA4PIJrhQcUEQVETBDXBD1Nat6u1yW9va2nKvv9bb1V2v1t62aq11qVqpUFEQEbUyKIjsyrAvwzL7vmQ95/39keRMMpPMZCAzmeX5fj58Jjk5Sd5khpycc57zHCCxetHeJSWMpuqOYeu2huvI2zTG8ygGRZqDYWqEW6ptwUB1qLFaYT8QJUCDDy1KJVqUSvhFF+vDkfXKfUCGGsD4UKN0rLMuxSI8EtbDGhwlGkx1PISF+rcm1YZfj7wGTw+/DAGFn/lERERElLhfn+PC909LT/UwqI/hWgUREQ0onno3aveGg9G1RjC6obQeUmPikgYggbY2aCMULaBEXu8DLT6JEE4FqrOTluqGiGbquuhgNVuqqbcFPAFUbq9A5fbotmZblj3YRj0+NypQbUmzpGikHW14Zt2JBaYBWC63RwWmga5PndykHDvuwHSaGgxM57Q/1byUSNsQgLMkenof27894FWZ0/H08Mvwf4WXosHkjD0TT49LA5BbtWJJ3rlYkncubJoPl9duYYCaiJJG8QNKrYS5NrFtGVIAug3Q0kTwn1NAdwpodgFpA3RLu0ZrBT2+bBYCcKgSDlUD2n+Pi8Ori6hAdasWDlgrEdfbbvNJAX7zG/h04YdP+OFD7DPWKNIUaqa2Ge3UkZcVmCD4d9KnJHLQbVKeBzrcog4tSgXcog4Jn5qoD/y5ZJuCQenxDg+GxjjbUixKq4T1kAbHLg2mBm4Lp/5PAlicNwU/GfMNHLVmx5iB2xqIiIiIiKj72DRNFIFN00T9g67paDzSaASj28LRNXDXuFM9PKLkMsMIPyuhQHRbU7QCkS4g+mCjWG+TLaEwdX34FLBsqaa+JX24KximPiXPCFVnnZwN1dK7p5muO1CHVy79K3S/ftyPoZ5hhnWeo1v3aRU1qFZLonc8J5hsdoUC01ntgza6hOsTP+yHol8LA9O9p9SagydGXIm/FFwEt2qNPRN3YNIgFA5QX1+1HlfWMEBNRH2TBCAtgG4T0OxtYWvdEWq0tgpIayhsbQKgok+2WQck4NYUNGsKmjUVjZqCJk1Fs6agKRC83qyp8Mu+N3bqPUKqMCHUTC1tbZdDjdUKzAxVDzA+tKBFqUCLUgVd+FM9nARJ5Jo1nOLwYJzdiyGJBqWbddgOaLDv0WFq4gYwGjh22Qvwg7G34YPs01M9FCIiIiLqx9g0TbGwaZqIiPo0d50bFVvKUL61HDW7qlG3rxb1B+ugeRNrKCLq85wiZhjamGZXUj3CfsFoqS7seJvUZDBAXR8dpg43VrOlmnpD05FGNB1pxIHV+41pillB1snZwVbq8bnIDQWq04e7eqxxa81vPj6hwLTIVWC5zN6t+/jQjBp113EFpjNNAdw0pA4ZpnZj1iQyV/lgrYjeIczAdO/Y6SjAoyPm4tX88+OfFjcclmZgmgYhj2rp0EDNADUR9TUCgPABik/C1AigouugnTQBulVAt4XC1k5AcyrQHIC0C+g2Ad0CSCNo3fPfA0wCSDfpSDfpAOIHDD26QGMgFKYOBaubAqGfoesena3VA5UUGvxohV/EPuOOkEqonbpjS7VJ2qDCwlB1P6AjgBalCi2iAj4ldiu5oc+sPErkm9sapTucXSkOtVGHbZ8G+z4daguD0jSwtChW/G7kfDwx4kr4421zICIiIiIiOgFc0yAioj5D82mo3lmF8s1lwX9bylB/oC7VwyI6fipCIWjRFoQON0RniOBlU5/YQzOgCVVA5KhAjopYnb6yNXaYWtbrkI1sqaaeo/t11OyqRs2u6qjpljQLssfmIPeUvFCYOhiqtmd3r925vUP/PoADH+w7/gcwAdZrHRCWts+trk6pHIAXVaYdkCIi9JzgzunsUGA6vX1gOiCRvdwHcx0D071tY9pJeHjkfCzJPRtSxDmoh2FpoigMUBPRQCICgBqQUFuAthWl+AfkSQXQrcGAtW4VkDZAswtoznCjNUKN1gK6GcG9FT30HcKmSNgsAQzpZB6/jqgQdftQdVNAQauuQPJb54AjhY4A3AiIOGewkwIqLNEt1aHLbaFqHvSeChISHlGPFqUCraIGEAluxEnpf2OJAksApzg8GO/wItOUQFBaSqgNEvY9GmwHNaix8/8Rz8D1Y+p/JIC3c8/Cj8bcilJbbowZeCYrIiIiIiJKDoamiYgoJaSUaDrWhPLNx4yQdOWXldC8iZ12kKhPsAmIDNExDJ2hQHEpEOncYdYfCIcC1dFJS3VDuzA1W6qph/mafcayMZIjzxkMUI/LRc4pwWbq7LE5MNvNXT6m5tfwya8+OqFxWebYoQyJPvSgs8C0jgCqTF9BE+3+oySwfyvXHAxMO9XoEI7wSmS/7YUpxg5i7jbrGRLAvzMn4KGi+YmdEpc7MIniYoCaiAYboQOqG1DdEokcjSoBSEsoZG0Lt1qHLjsFNLuAbheQVkC3CEgLACV53z3MCpCtaMg2awD8MefRJEJt1cEQdXMoVN2ohVqsA8HrOr+dDixCQoMXmvAi5hJbwghVhxurIy+rsEKJeRg3HS8/3GhRKtCiVHZc5+yDBCQKrX6MtwcbpV3tDw6ORUqYaoNBaeshDaqnO89H1L/ssw3BD8behvdyvhZ/Jm5vICIiIiKiJGFomoiIeoWvxYfKbeUo21yGii1lKNtUhtaqllQPiyg+AYh0EQpCK0ZjtBJ53coNtQOdUAVEtgpkJ9BSXR/dWM2Wakq21qoWlFa1oHTNobaJAsgYmYnc8XnBIPX4YCt15klZUExtB25sf2UravfWHvdzq6eZYTrTkvD8EhLV6s64p7/uTL7Zj4VD6uBQo/8DiVYdOf/yQe37+8MHBB0C7+aciYeK5mF9xthUD4dowGGAmoioIwFA+ADFJ4FGIKGgtSkYstbsgO4ItlhrDgHN1dZoncxwtSqADJOODJMOWOPP16IpwYbqQERTtaZEtVf7JQ+0HjAEoMHXaXhXkeZgmBq2YFN1uKU6dFnh7rou6QigVVSjRamEV2nsfOY+ULUsIFFk9WO8w4NxDi/S1ASC0rqEqVqHfY8O22ENCr8W0gDnVsx4uGgeHi26Cl4lxnYntksTEREREVEPEPX19YxyEIXs2bMHY8cyEEB0oqQuUbu3BmWbQgHpzcdQu7sGUucih/oQM4xAtBJuiDbaopVgYDqJjVU0+MRsqY4IVseupyJKDtWqIntMDnJPyUX2uFx88Yf18DYe3x+dyFZguzMt4QNFJCTqlH1oVsu7/VwFlmBg2qZEf2dQmnTkLPVBSWAfM52YgFDwj7zz8EjRPHyZNiLVwyEadGyaD5fVbsWCqnUMUBMRJYkEgm3VjrYgteYQ0NIFtLTgdd2GYCK6F3l0ERWiDrdXR1736AIpT35Sr1CkKWZLdfiyAhPEIPxbkJDwika0KBVoFdWQom+vFCqQGGnzYbzDi3F2T4eDgWPSJcwVOux7NVgP61BiF90TDTjvZn8NPxp7K/bb8zveyLA0ERERESXJr89x4funpad6GNTHMDRNFIGhaaLj01rdgvIt5SjfdAzlW8pQsbUcvmbWQFKKOUXMMLSSoUC4BISDjU6UWrK1XZg6fJkt1dSXqIDtjjQoQ9u61qWUEJ3suGpUjqBePdjtpxpu9WFBXj2s7QLTphod2e/4BmE8oHd5FDNeHHoBHh9xFQ7Yh6R6OEQEBqiJiHqTBCAtMALVwYA1gs3V6aFgtV1Amnv3W6lfB5o0Fc2agsZQqLo5FKpuDE1v0RRIflse8IRUYYI12EwtbW2XYYNJWqHAPKBC1QF40aJUoEWpREB4Op85xa3SKiRG2X0Yb/dgrMPb4SDgmDQJS5kO+z4NliM6lEDPj5Oorzhoy8W9Y27B27lnp3ooRERERDQIMDRNsfB8X0RE1C0BbwBVX1WifEsZyjcH/zUebkj1sGiwUREKQotgGNoVbouOCEmbBs6OIhqYhEOB6lCAYR1vM1qqI5qpIxur2VJNvcV8qS0qMA2g08B0q6juGJhOYAf2SKsP1+XVwdLueBZzmYas9/0DaNd/39Oo2vHnYbPw1PDLUG7NSvVwiCiCR7Vgad45WJp3jhGgvr5qPa6s2Yx0rYvwEBERdYsAIHyA4pMwddIzI1UEQ9VOAc3R1l6tpYVaq+0C0oKktWOaFSBb0ZBt1gDErp/VJdCktYWpmwIRzdWh682aAo3fqvs1KTT40Qq/aI15u5CK0U4d/GmLuqzC0udD1To0uEUtWpQKeER94kHoFLwsk5AYbfNinMOLMXZvh4N/YwpIWI/qsO3XYD2qQ2g9P06ivsQrTHh8xJV4cOR8uFVrxxnYLk1ERERERL2EoWkiIopLSomG0oZQODrYIl31VSV0f98+DSINADYEW6EjGqLDYWglQ4FIZ0s0DWxCFRDZKpCtQo1xu3THDlPLeh2ygS3VlBzqBDPMZ8fYiRWHVzSiRt0dPTGBwPQomxfX5tbD3O6j3XJIQ+bH0YHpFBeIDShV5nT8vnAO/q9wNurNztgzcYclUZ/BADURUd8gNMDUJIGmToLVAtAdiGisFlHhat0hoNsAKMn5nqUIIMOkI8PU+fa6Fk0Eg9SBiEB1u+t+ye0t/ZUUOgJwIyDc8WaACkt0S3Xocluouvd//xISPtGMFlGBFqUKsg8niS1Cx2i7D+MdHpxs83Y46Dcmv4T1sA77AQ2WYzpEJ/9Nub5LA9n7WafjB2Nvwx5HQccbw9seuP2BiIiIiIh6CUPTRERk8DZ6Ub61DBWby1C2uQwVW8rgro2zoZ3oeAlApLc1RIcD0UrkdSs3kBJ1RtgVqPY4LdV6qKW6XZg6HLIGc1WUAJGlwHKVPeH5A/CgSi2BjNwDnMAe3zF2D67ObUD7kwPY9gSQ8Wn0+Ym5Azk5Dluz8eSIK/BcwcWxm50A7rAk6uMYoCYi6tuEBNQWQG2RiHdEqwSg2xAVqtYdAppDQEtHcJpdAGryvo85VQmnGsBQS/x5PLowQtTNoVB1Y6CtxbpRU+HRBfjNvB8SEhq80IQ39smrJIxQdbixOvKyCiuUmId1Hx8NPrQolWhRKuO2Z0eOLVV/clahY4zdi1McXoyyezusu8YifBLWQxpsB3VYyjsPSkfd78SGStQnHbFm48ejv4F/DpkSfyZueyAiIiIiol7G0DQR0SClB3TU7K4OtUgHm6Rr99WynZROnBkdw9Dhfy4FwiUgktSmREQdCUVAZKlAVryWagm9TotuqQ6HqxslwJMJkApYrrFHHcAipYSIsxNLRwCVpq+gi3anC+/io3683YN5uQ0dciD2rwJwfcHAdLLtshfg0aKr8Gr+dPiVOJsCGJYm6ncYoCYi6p8EANUDqB4Jc238YLU0A7pTtLVWO0LXw63VdgFpSd53N5siYbNoyEP8tl+/DiNEbTRWB6Kvt2gKJL/B9y8iGGTWhC/uLIo0B8PUsAWbqsMt1aHLShe7HCV0uEUtWpRKuEVt4it5vfynZFd0jLV7Md7hwUk2X0LHLgivhO2ABoPhF5wAAHWKSURBVNshHeYKHYLb2GmQ8wkVTw2/HL856Rq0qLaOM/DMVkRERERElEIMTRMRDRLNFc0o33QM5VvKUL6lDBVbyxFwB7q+I1F7TgHFFQxER4ahlVBIWjh4KleivkzYBVS7qZOWahkdqK7T2FI9yJhn2aAOi15VjBeYltBRpZbEPwV0HKc63Lgyp7HDGcmdm/1I29YxoMHdaMdvU9pJeLhoHt7KOwdSxFlGMyxNNCAwQE1ENLAIAMIPKPUSpvr4CUypomOoOnw9PXhZWpG073pmBchSNGSZNQD+mPPoEkZTdXSoOjQt1F6t8Zt+v6ILP3zCDx+aY96uSFPMlmoFZrhFDVqUqo4H2/YRTkXDWEewUbrI6uuwrhqL4g4Gpa2HNJgreZgAUdhHmRNxz9jbUeIs7Hgjtz8QEREREVEfwNA0EdEA5Hf7UfllBco3BQPS5ZvL0FzWlOphUX+gIBSCbgtEK+2ui0TOQ0kDloSEhAYNfmjCZ7QQ6aEdpcZpW6UldOpWEwR3G/UbwZZqAWQpwKiOt0t3MFAdDlFHXW7Q2VI9AKjjTTCfa01oXgmJWnUvvEpDt57jDKcbl2c3Ru0fk1IifUMAzpL4jXaUOAngk4xT8NDI+ViVfUbXd+DOSqIBp32Aek7tVixggJqIaEASGmBqkkBTJ8FqAeh2QHOGw9XCCFdrzuBl3Q4klBRNgCIAl0mHy9T5SmKLJozW6saAagStG0Oh6iZNgU/y4Pz+QhcB6AjAL1q6d8cUnVooXdUwLtQoPcLqT2i1SGkJN0prMFUzKE0U6ZglEz8d/XW8nj8t/kzc/kBERERERH0AQ9NERP2c1CXqD9ShfEsZyjYdQ8WWclSVVEJqPAcgxWBFWxA61BAd1RidJuK2idLAJiGhw2+EoIM//VGh6PB0KRJPxgqpQIXFOFWrKq1QYWGwup8SdgFhV6EUqB1uk7qEbJSQdZEt1W3Bani4XOrrRIaA5SpHwvM3KofRolR26znOTGvFnOx2B3JJCdenfjj2MnV/oiSAd3LOxMNF87AuY1yqh0NEfYRHteBfeefgX3nnwKr7cFkNA9RERIONkIDaCqitEsFvjR1JANIW0VodEbDWnG0N1kjiwfROVcKpBpDfyTweXQTD1EZbdfv2ahVuXYDnp+nHevFXl6FqGO/wYLzDi0JrYq3XaqMO20Ed1kMaTLUMShO1FxAKnimcjV+edD2aTPaOM4TbpYmIiIiIiPoIhqaJiPoZd50bFVvaGqTLt5TD28Ad3QRAACJdhILQitEYbQSkMxQIKzdODjY6tJjB5+DPtus6/D2yk0oKHQF4EBAeeOPMEwxWB0PUHYPVwbC1gMpgdR8nFAGRKYDMOC3VHtkxTB2+zJbq1FMAyzUOCHvb/zMpZdwDaVpEJRrU0m49xTnpLZiV1e40zlLC9Ykf9oP8AzgRAaHgzbzz8EjRXGxPK0r1cIioD/MqHQPU11d9jqtqNjFATUQ0yAkAwgMoHgnUdhKsNqNdqLrteniaTOL2J5siYVM05Jrjn5UmIBEzVN2sKWgMXW/RFDDuOjhlmQIY7/BivN2DAmsgofuo9TpsBzXYDulQ6/mXQxRPccY4fH/sHZ1vi2BgmoiIiIiI+hiGpomI+jDNp6F6Z1UwHL05GJSuP1CX6mFRqpgREYgW7dqilWBgWuUGyMGgYyt0RPhZRLdFd6cVOlWCwWo3AsLdZbDaFApUq7LdZQar+zxhExAFCbRUh5qpZX1bYzXcbKnuaeaZNqjDo1cP4wWmPaIBNeqe6IldnE55qqsFF2a2C0zrEhkf+2E73Pc/p/oqj2LGS/kz8FjRVdhv76yfj4ioIwaoiYiouwQA4QeUBglTQ/z1NKmGG6tjNFeHwtXShqQF6UwCyDJryDJrAGK3B+sSwcZqTQ39VNAYUI1pjQEFzZoKjdsVBoQcUwCnhBqlh1gSC0qbanTYDmmwHtJhauzk7xvsNSeqMLuwaPRNeHnoBakeChERERERUbcxNE1E1EdIKdF0rAnlm48ZIenKLyuheRPbqEsDgCMchBZRYWhjmkNJ9Qiph6W6FboviwxWxyOk2tZQHRWsbmuxVvj1t0+KaqmOQXpkzDA1W6qTQxljgnmqNaF5/XCjWi0Jnt87rNM9xhIzMlpwfkZL9GRNIvMjP6xH9Yg5B91H23FrUm14dtjFeHL4FSi3ZqV6OEQ0ADBATUREySQ0wNQkgSYg+E2/IykA3d4WqtadIipgrTkB3S6AJBUEKAJwmXS4TJ2vQLZqIqqtun17dZOmwCe5ja7vkRhiDjVKOzydNpNHMlVFBKWbEztgm+utNJhpEPhT4SX4xagFaDA5O84gJZuliYiIiIioz2NqhIgoRXwtPlRuK0fZ5jJUbClD2aYytFa1dH1H6p8UQLhCYejQP8XVLiBt5sbEgSi6FTq6BTq6FdoPKRLboUOxSaF1M1gdClOHLofD1gxW9z3CJiCGqlCGdtJS3T5MHQpZs6W6c8IlYJ1nj5ompYzZMq3BjyrTV9BFuwO6OglMX5TZjPNcre0eSCLzAz+s5QxMd1e1OQ2/L5yD/yucjTpzWuyZuIOSiE4QA9RERNQbhATUVkBtlYgbrAYgrWhrqTbC1WgLVzsEkMRtag5VwqEG0Nl5XLy6iBuqbtYUNGoq3LoA13J6mkSBJYDxDg/G272hpvGu7iJhrpShoLQGtbXruxBR0DrXGHx/7O3YnD4q/kzcHkFERERERP0AEyFERL1A6hK1e2tQtikYkC7fUoaaXdWQOoNcA4YVba3QrnAwOiIQnS5iBtCo/9KhddoGbbRFw8d9ZH1IosFqUyhQbYSpIy/DCgUdw7uUGlEt1Sd1vF16ZTBMzZbqjhTAco0j6kwG8QLTEjqq1RIERKJhOYlLsppwdnq7/2sBiaxVPlgqZcSc/JjsymFrNp4ccQWeL5iJVtUWe6ZwWJrfN4goiRigJiKiVBIAhBdQvBKojb8dVTcjdqg6osFa2pL3PdmqSFgVrdM244AEmjUVTYFgiLo5HK4OKEbQullTILk21E0ShRY/TnF4Mc7hQUYXzeHBu0hYynVYD+nBoDS/whB1S7U5DfePuhF/GTYz1UMhIiIiIiJKCoamiYh6QGt1C8q3lKN80zGUbylDxdZy+Jp9qR4WHS8BiLSIluhQY7RiXFcgkrjjhVKnrRXaH6cNuq0tmq3QA5cUGvxohV/ErxtqC1Zb25qrpTUqbM1gdd8grJ20VMtQS3VEM7URrB4ELdXmC61QR0SvEsYOTEvUqLvhVRoTfGSJOVlNOLNdYFr4JDJX+WCpZmA6UbvtQ/Fo0Vy8kj8dfiXO6jvD0kTUS9oHqOfUbsOCyvUMUBMRUcopfkBpkDA1xF+HkwpCIerQz4hQdbi1Wrcjad+rTQLINGnINGkA/DHn0SXQoikd2qrbX9cG+VqTgMQIqx/jHR6Ms3uRnkhQWpewHNNhLdVhK9WgeHt+nEQDjQ6BvxRchPtPXohac3rHGXimKyIiIiIi6qcYmiYiOkEBbwBVX1WifEsZyjcH/zUebkj1sKg7TGgLRGcIKEZTdOhfuoBQufGvP+uqFTrytkG+H4oS1N1gtSkUrmawum8RQkBkCCBDiXm70VIdbqaODFY36EA/PnZCOdkE8/lxGovbaVBK0apUJzSvgMQV2Y04PS06PCe8Elnv+2Bu1w7Hj9zYNqeNxMNF8/DPvHMhRey/T4aliSiVvIoFb+eejbdzz2aAmoiI+gWhA6ZmCTQDwcM3O5IC0O0RLdXhMHXouuYM3oYkbSdUBJBu0kMh4EDc+Vo10SFU3aypaAxdb9YUeKXAQFrDUiBRZPNhvD3YKO1UEzioWQsGpW0HNViP6FDYYUJ03L5IH4Xvjb0DX7hGx5+J2yOIiIiIiKifYmiaiKgbpJRoKG1A+eYyVGwpQ9nmY6jeUQXN149TU4OBQwRboV0iOgztCk13xgkjUZ8WbIUORLVAsxWa+pJEgtWKNLWFqaUVaihQHdlizWB16iTUUh3RTG1crteB1r7bUi3SBKzz7VHTpJQxW6abRTka1cMJPa4CiatyGjDRGV3hJdyhwHR9331P+gIJYE3GKXi4aB5W5kzq+g7cOUlEfQQD1ERENFAICaitgNoqYY4XrAYgrTBaqttaqxF1XVqS933doUo41ADyO5nHqws0hxuqAxFN1RHXW/W+HaxWIXGSzYfxDg/G2r2wJxKUDkhYj+iwloaC0rFLvXmWI6IE1Zqc+PmoG/DssIvjH8RNRERERETUzzE0TUTUCW+jF+Vby1CxuQxloaC0u9bd9R2p9yhoC0OHGqKVUGN0+Lowc5N4fyKhxww+d2yF9gf3ZhH1Y7oIQEcggWB1KEzNYHWfEdVSPbLj7dIro5upIxurU9lSLQDLNY6oA4biBaY9oh616r7oiXH2NKuQmJfbgPGO6MC00iqRtdIHUyM/r+ORAJZnfw0PjZyPzzLGpXo4REQnJFaA+vrK9biqZjNcGteliYio/xMAhBdQvBKoi7+eo5vbN1Z3bLCWtuRts7QqElZFQ445/spmQCIYrA5EBKrbXW/WFMhejBebhMTJNi/GObwYY/fCpnS97ij8EpYjoUbpYzpE/JLutvskYaxEA5kOgZeGzsCik29CtcXVcYbw2a6IiIiIiIgGAIamiYhC9ICOmt3VKN9cFvp3DLX7auOdrZF6kXAJiFwVSla4JToiEJ0uYga9qG+JbIWO1wYdvs5W6P5DgYRd0eFUddgVHQDQrA3M08KmUluwuiXuPOFgtUkGA9XG5XCLNawQYDtMbxJWAZGvQslPoKU6HKYOhax7sqXaPMMKdWT0amCs5agfrahSS6IPTukkMH1NXj3G2KPPfaw0BxumTU38MhVLQChYnDcFjxTNxba0GMl7IqJ+jgFqIiIazBQ/oDRImBrirw9JBUZLteYMhqnbB6t1OwAlOdtXTALINOnINOlx59El0KIpUSHqxoBqtFg3agqaNRUBefxjMgsdo+0+jLd7MNrugyWRoLRPwloaapQ+qkPEfwlE1E1b0kbi+2Nv7/xAbu6DISIiIiKiAYShaSIatJormo1wdPmWMlRsLUfAnUAtBfUYIxydp0DJUyHyFCi5KoSVG+T6quhWaH+MNmgfW6H7EQEJmyLhVHU4FB2O0M9wKLr99M5Ok+rX2wLUzZqCFj3YXBT8qaBFD54eluHq5EgsWG2GilBbdShQHW6uDgesGazuHV22VPtkzDD1ibZUKyepMM2wdjmfBh8qTV91PIglxn9Vk5C4Lrceo9oFptUmHVkrfVBDf5I8FXIbrzDhpaEz8GjRXOy3d3aCbSKigYMBaiIioo6EDqjNEmozEK+5QwpAtwG6MyJM7RAR14OBa6jJWeNSBJBu0pFu0gHE31bu1kRbU7UW3MbT/nrkNh+r0DHG7sV4hxejbF6YE9j8IDwS1lINtkM6LOUMShOdqIBQsN82BCXOQuxwFKLEWYgSRyG2po2ELrhNkIiIiIiIBg+GpoloUPC7/aj8sgLlm8pQviX4r/lYU6qHNWiJdAGRFxGOzg39ZDi6T4jfCh0dhNbhh57I+S8phSSsIhSCjgg8O5Xo6+GfdkUmq7wIZgXIUjRkdXJaWKDt1LCRAetYlz06w9UnShd+6PB3Gaxu31DdvrmaweqeJyxdtFQ3yegwdURjNVriHMzgFLBe7YhqlZZSdmiZ1qGhSt0BTXi7HKdF6Lg+rx5FNn/UdLVBR9b7PqitoecB//cCQJNqw7PDLsb/Dr8CZdasVA+HiChlGKAmIiJKnJCA6gZUt4Q5XrAagLQiRqgabY3VDgFpSd6amV2VsKsBDOlkHp8u0KQp8OgKhlr8CeW6FbeE9ZAG6yEdlgqdHQxEx8EnVOyxDw2Fo4djp3MYShyF2OUogE8xd/0AUrJZmoiIiIiIBjSGpolowJG6RP2BOpRvKUPZpmOo2FKOqpJKSI1bWHubEY7ObdccbeMGt1Roa4WODEK3b4UO3sY9En2VhEXIDoFnpyo7BKDDP5NUNNRjEjk1LBAMV7d0Gq4OXnczXH1CdOGHT/g7nSd2sDqyuZrB6p4khIBwCcDVSUt1ZDN16LL5PCtEWtvvJVZgWkKiRt0Nn9Lc5TisQscNQ+pRaG0XmK4LBaY94cfk/8gaUxp+P3wO/lA4G3XmtNgzcYckEQ1SDFATERGdOAFAeAHFK4G6+Nv0dBMiQtURIetQqFp3COj25K2XWBSJHEVDV6dLUlqCQWnbIQ3mKsnNkkQJ8ihm7LIXdGiO3uMYCk10PBA/Ydw+QUREREREAxxD00TU77nr3KgItUeXby5D+ZZyeBs8qR7WoCLS4jRHMxzd4yQkJLSoEHT7VuhwWzRbofsmk5DtAtA67KGf7QPQTlWHqS/9t5IyuFPOI6F4AAhAtwd3uiV7oCYBZJh0ZHQRrtYimqtbNAVNmhIzbN3KcPVx6zJYLQEF4WB1sKk6HKxua65msLqnCIuAGKJCGdL5zrH2gWkAqFcOwq3UdPkcNkXHwrw6FFijlyumGh1Zq3xQQiXVgz0wfdSShSdGXIHnh12MFtUWe6ZwWJo7JImIOgSoZ9dux4LKdQxQExERJYkSAJRGCVNj/FSyVMLB6uiW6qgWawdwoqcqU5p02A7psB7SYK6WMdcdB/s6JVFYs2rFTkewLXqHsxA7HcFw9H77EOiC29eIiIiIiIi6i6FpIupXNL+G6p1VKN/UFpKuP1CX6mENGsFwdLAtOqo5OokNJBQUbIX2x2mDjg5Fs36lb1EQ3QTtjNkALY1wtFXpW78/4ZVGCDr4M/Zl4ZFQvLH//CQAaYbRUKQ5AN0e2rlmDzcXAZpdAObkfn6o3QhXt3TSWB2+rVVXEHvXHcUlAB3hYHWcxmKJUFO1BSqsMZqrg9MF3/te06SUoUk92uV8DkXHjUPqMMTSLjBdHQpM+9qmDdbf3h57Ph4tmouX82fAr8RZ5WZYmoioU17FgmW5Z2FZ7lkMUBMREfUioQNqs4TaDAS38HQkBaDb4oWqAd0pYh5QrzbosB7SYTukwVTbdVCaa0s02DSodqMtekfoZ4mzEIdseakeGhERERER0YDC0DQR9VlSSjQda0L55mPBBunNZaj8shKal225PS5NQAm3RYfao5VcBcLO1oIT0bEVOjoIrUe1Q/PvvK8QkLCHg84xA9BtDdEOVYetr4Wg/W1hZxEvBO2WULyheTrPGif2nACEH1AaJNDQSXsRQuFqI1AdGbQO/tRD02QPhKtdJh0ukw4g/v83PSpc3bGxupnh6uMjYHz2dR2sDgaqYzVXBxur+b53h5SyQ8u0W9SiTtnXbkZ02EOdpmq4cUgdcs3Rp1Y2V+jIXO2D0kkB+WCwJW0kHi6ah3/mnRu/6YlhaSKibosVoL6+aj3mVm9igJqIiCgFhARUN6C6Jcw1cYLVAKQFwVC1TUBtlVAbut5ywjUlGgxqTGlRoegdoZ/HrNmpHhoREREREdGgwNA0EfUZvhYfKreVo2xzGSq2lKFsUxlaq1pSPayBzSmghNuiw+HoPIaju6vzVmh/VChaJiORSidIwqbItqBzJwFoh6LDrsi+lW3TJBR35y3QkdeF1vVDpooRrvZLoJNTwwKAbkJbQ3U4UB0OWEeEraUlub8sRQDpJh3pXTRXh8PVLXrn4eoWjeHqhEUFq+OICFZHNlRHNlczWB2tfWDah2ZUq7ui90zHCEynqxpuGlKH7PaB6TINmR/6oQziY33WZIzHw0Xz8F7O17qeuU8tUIiI+h8GqImIiPoHAUD4AMUnEa+xmmggkwAqzS7scA7vEI6utGSkenhERERERESDGkPTRJQSUpeo3VtjNEiXbylDza5qSJ0bUHuEM05ztIPh6HjYCt2fSFiFhF3VOzRBO9Vg6Ll9OFrpS5k1PU7w2S0hvG2XFW+wEVoEBmfrjhIAlEYJNAKd7WyTJkCzC+gOtGusDk7TQmHrHgtXo/NwtZSICFYHA9UtmoKmUKA6HLBu0RTog/I33U0RwWofmmLPIxGzoTqyuXqwBqsD8KLKtAMy8uiKGIHpDFXDTfm1yGx38IDlqIbMj/x9+uCMniIBrMiehIdGzsenGeNTPRwiokEpMkBt0f2YU7uNAWoiIiIi6lUSwFFrthGILnEUGi3Sdea0VA+PiIiIiIiIYmBomoh6RWt1C8q3lKN80zGUbylDxdZy+Jo7aY6k4+OI0xzNcLQhGIVu3wYdOxTNVujUMQvZZQN05E9TX8o66sGAs+iyCTp4WfgGZwi6p4gAYGqSCOZnOwlXq+HGahit1ZrRXt3WZi2tyf3tCAGkqTrS1K7D1a1R4eroQHVTKGzdzHB11wSgwQtNeDsJVouIILWlQ7DaJK1QYB5QwWodAVSZdnRs8m73ErNMAdw0pA6u9oHpwxoyP/ZjsC0qNQgsHjIFjxTNw9a0kakeDhERhfgUMwPURERERNRjdAiU2nLahaODLdJNJnuqh0dERERERETdwNA0ESVdwBtA1VeVKN9SZjRJNx5uSPWwBhZHnOZo5+AMR7e1Qsdvgw4Ho3X4mVBNARUy4QC0Q9Fh6WN/ysIjoXhDQWd36LI7dhha+ADB0vw+T2iJh6t1u4BmRyhQHdFeHW6ztgtIW/LD1c5QW3p+F/O2asIIVEeGq9u3WWv88ItPSCNYHVeMYLXJaKruX8FqCYlqdRf8oqXT+XJMAdyUX9ch5G89qCFjTTAwHaOYekDyChNeHjoDj424CnsdQ1M9HCIi6gQD1ERERER0vDQI7LcPQYljuNEYXeIsxE5HAVpVW6qHR0REREREREnA0DQRnRApJRoPN6BsUxkqtpShbPMxVO+oguYbhOdp7wn2OM3RgygcHYxDe+EX7lCgzR8RgvZBD11nK3TvEujYBO1UY7dDO1QdNqVvpYiFr5Pm5xjTGYIevIQGqM0SajPQabhaCQWoHe2bq0PXQ2HrZIerAcChSjjUAIZ0MV9kuDrYVq2gJUbYmuHqOLodrA4GqoPBaqsRtk51sFpCok7ZD49S1+l8Q8x+3DikDg41+u/etl+Dq9gPIQdHYLpZteK5govx5IgrcMyanerhEBFRN7UPUM+u3YYFVetxVfUmZDBATURERDRo+YWKvfb8UGN0Wzh6l6MAXsWS6uERERERERFRD2Jomoi6xdvoRfnWMlRsLjOapN213NF4wmKFo3MViLTBEY6ODEYHhAcBuCMuM7HaOyTsiuy0Cdqp6LAbP/vY7yQQEXTuogla8QDM2FOyCR1QWyTUFqDrcDVCgWoREbCODltLe+rC1W5NxGisDoWtdQVNAQUtuoqAHOhx2eNwHMHqqObqXghWNynH0KyWdTrPUIsfC/PqOnzW2/YE4PosMCgC0zWmNDwzfDb+UDgbteb02DNJGayFJyKifsGnmPFO7ll4hwFqIiIiokHDo5ix2z4UJc5C7HAUosQ5HCWOQuyx5yOgcDc5ERERERHRYMS1QSKKSw/oqNldjfLNZaF/x1C7r7azLBh1xRYKR+epELkRzdGDIBwdDkYHhCcYiIYHAeE2LjMYnWwSViHjBqCDP6Vxm13RofSl3JcWuwU6bjt0INUDJkpMMFwdDFh3Ga62IaKxOtRWbUdU2Fq3IemhTbsqYVc15KHzs0Z49Fjh6ujrLZoKP8PV0RIMVptCTdVtwepQc3XosgJTt4PVraIG9cqBds+FqPTzMIsPNwyp73CGAPvOANLXB4xZB+pv9aglC0+OuALPDbsYLfFOuxsOSzMwTUTUb8UKUF9f9TnmVm9kgJqIiIioH2pRrNjpKAiFooehJNQevc+eD10M/P0vRERERERElDiGponI0FzRbISjy7eUoWJrOQJuJhGPiw3tgtGh5uj0gb1xLhiM9iEQCkIH26LbGqMl631PgIRZSCPs7OwQgI4IR4euq30py6VLKF4EW6A9EiLicswQtH/gBvKIEiF0QG0F1FYJc2fhahFsrtbtAlo4TG0X0B3t2qxtQLKPjLApEjZFQ645sXB1i6agSVPREiNc3awp8MuBvYzsFiFDBxd5OplFMRqqOzRXh8LWkcFqr2hCjbor+sO1XWB6hNWHBXn1sLQLTDt2BJC2ITCgP5f32vPx2Iir8NLQGfAp5tgzMSxNRDQgMUBNRERE1H80qnYjFB1sjg6Gow/auzq3GhEREREREVEQQ9NEg5Tf7UfllxWo2FyGss1lKN9ShuZjTakeVv9jDYWj8xQouaHW6DwVygAOR0tI6PCHGqLdbc3RoaA0g9GJUyGNluf2TdBORYc99DM83dyX/qxkKPjcIfQcJwTtZQiaqCcIGRGurukiXG2D0VDdFqhGW9ja0Rvhan/c+bxGc3VboDpWwNrHcDUAQAq9W8Fqv2iNXka3C0yfZPPiutz6Dssax7YA0jYP3MD0VmcRHh45D4vzpsRvnmJYmoho0GCAmoiIiKhvqDU5sSMUiC6JCEcfseWkemhERERERETUzzE0TTQISF2i/kAdyreUoWzTMVRsKUf1ziroAYZbExYrHJ2rQnENzOBWdDDag4Bwh4LRHgTgZjA6DgWyQ/NzdAN09O1WJX7AMRWET4banwHFG3E5IgAtwpe9wbAmEfUPQgKqG1DdEqjtIlxthdFQrYWC1dHXg9OSHa62KhJWRUNOF+FqX1S4OjpQ3aKpaAqFrb1SYLAfrtFpsDrirRlt8+KavHqY2r1dzs1+OLdpA/JdLM4Yh4eL5mFFzpldz8ywNBHRoNQ+QP215kNwBdxwah6kaV6kaR449OBPZ+h6muaFM3Q9PM2ph2/zwKH5oHRyFhEiIiKiwUQCqDK72sLREe3RFZbMVA+PiIiIiIiIBiiGpokGIE+9G+Wh9ujgz3J4G+K3EFIEK6DkxmiOHoDh6GAwOhAMRIcao41wNDyQQkv1EFNOQMKudAxCO+MEo+1q39r5Lfwxws4xWqCNeZiFJxr0hARUD6B6JNBJoMcIVxuN1QKao12TtV1AdyDp4WqLIpGtaMg2d76c8ulAS0Sgujnqctv1wR6uHmf3YH5uA9R2b0HaRj+cXw6s7wISwHvZk/BQ0TyszTwl1cMhIqJ+xKeY8blrTFIey2GErL2hcLUnKlztjJoeHcqOHdIOzqsyjE1ERER9lARwzJIVFY4O/6wxp6d6eERERERERDTIMDRN1M9pfg3VO6tQvqktJF1/oC7Vw+r7LDGaowdoOFqDH4FQEDqyLdov3IMwGC1hU6QRdHYqOuyhn7EbomXfKpfUJBR3/NBz2+VQCHqw/XqJqNdEhavrOglXA5BWtDVUOwS0iObqyDbrDqndE2RRAIuiIauLcLVfR3Rbtd4+YB287NEHXrh6gsODuTkNHXLt6Z/74SjRIDEwXrEGgX/mTcEjRXOxJf2kVA+HiIgGuVbVhlbVhqokP65N88UIX7cFq4NN122t18H5OjZjR4aynboXJsmja4mIiCgxOgQOW3NiNkc3mhypHh4RERERERERAIamifoVKSWajjWhfPMxo0G6cnsFNG8g1UPruyxxmqMzBlY4Wkcg1BAdDEVHXtbFQP77kLAIGdUAbVd0OFXZoRnaHrqe5EzeidHjBJ/dEsLbdlnxIjgtMDDCa0Q0eAgg+HnmTTBcHdFcrTsQ0VjdFrZO9ge5WQGyEghXBySiQtQtmoImTQ39bLveX8LVpznduCK7sWNg+jM/HLsHRmDaK0x4Zeh0PDbiKuxxFKR6OERERD3Ko1rgUS2oTvLjWnWf0YwdGbyODFantQtedxXSTtO8MEse5UtERNRfaRA4YB+CEkehEZDe6RiGEmchWlRbqodHRERERERE1CmGpon6MF+LD5XbylG2uQwVW8pQtqkMrVUtqR5W32SO0xw9gMLROgIRbdHuUDDag4BwD8hgtEXoyDBpcKk6XCYNLlVDmhrdBO1UdZj6UqJLjwg7d9oEHbwsfP0/kEZElAxR4er6LsLVFoRC1MFgdVtbdXTYOtkLCJMAMk06Mk2dty0GJNCitW+r7the7U5huHqSsxWX5zRFT9QlXJ8GYN/X/wPTzaoVzxfMxJMjrsRRa3aqh0NERNSveRULvIoFteb0pD6uWQ90aLZ2GMHqtrB1+5B2muYNzefpENJO0zywMIxNRESUNH6hYp8Rjh5utEfvdBTAq1hSPTwiIiIiIiKi48LQNFEfIXWJ2r01wQbpzWUo31KGml3VkHr84NCgZI5ojs5ToeSGwtGZAyMcHR2MDgaiw9d14U/18JJIwqmEQtEmHS5VCwWkNWSYgiFpm9I3/vaFN0bo2R0M9inu0PTQZeEDRN8YNhHRgCQACB+g+CRMiYSrjUA1jAbrtjZrQHP0TLg6w6Qjo4twtRbRXN3SSbi6Ncnh6rPSWnFpdozAdLEf9gN6vw5M15qc+EPhbPx++Jz4wS4pAdFfXyEREdHA4VdMqFPSUGdOS+rjmvRAMHgd1YDt6RCydkbMEzlfdBC7rU3bqvv77XckIiKirniFCbsdBVHN0SXOQuyxD4Vf4a5kIiIiIiIiGli4pkuUIq3VLSjfUh4KSR9DxbZy+Jp8qR5W3xEZjo5sjh4A4WgdGgIIhqKNcDTcAyoYrUIi3aQhQw2GosOBaJdJR4aqId2kpawhWvhiNz+LOI3QDEETEfU/UeHqhi7C1eaIQHWoudposo5srjYnd8GldiNc3dIuUN2iKWjSFLREBKxbdQWyiyjPlPQWzMxq7vAEGZ/4YSsNjqM/hoGOWTLx5Igr8FzBxWg22WPPFA5LMzBNREQ0oAUUE+oVE+rhTOrjqlLr0GztjGjFDgewHZq3XYO2ty2Qrbe/zQMbw9hERNSLWhULdsUIR++1D4Uu+v++FyIiIiIiIqJEMDRN1AsC3gCqvqpE+ZYyo0m68XBDqofVN5gAkRtqjY5ojhaZAqIfh1qCwehQU3Q4HB0KSmui/4fjrSJ2S3Q4IJ2mdh4AS6pA+6BzjCboyBB0Lw6NiIj6NgFA+AGlQQKJhKuNQHVb0Lp9m7XsgXC1y6TDZdIBBOLOp0eFqzs2Vhda/ZiW0RJ9J00i82M/rEf658Jxn20IHiu6Cn8begF8ijn2TAxLExERURJoQkWjyYFGkyOpj6tIPaINOxS81jsJZ7cLaTujwtkeI6Tt0L0MYxMRDWJNqg0ljmEocQ7HjlAwusRRiIO2XEiGo4mIiIiIiGiQY2iaKMmklGg83ICyTWWo2FKGss3HUL2jCppPS/XQUisyHB0KRit5CkSm0m/D0RI6Agi3RbujLvfvYLREmqobLdFGKNqkwaXqcJk02JReqF/WJNRmCbVFQmkJXW6NDkArHgkRPz9GRESUFEa42i+Bxs6XgbopHKhGKEwt2pqsI8LW0pLc7z+KANJNOtK7aK42BCQyP/LDeqz/Baa3OUfg4aJ5eHPIefGbsBiWJiIion5AFwqaTHY0xTtbxnES4TC2Hh28drZrwnZGBbC9bcHtOCFth+aDAp6Si4ior6gzOYy26Mhw9GFbbqqHRkRERERERNRnMTRNdIK8jV5UbCtH+aZjRpO0u9ad6mGljgkQOeHm6FA4OleByOqf4ejoYLQHAbiNyxq8/fIc9iokXKZwO7Qe0RKtIcOkI13VoPbC6xLedqHolojrzaFW6J4fBhERUVIpAUBplEAjgE4CJdIEaHYB3YGItupwyDp8W/LD1QAg/BKZq32wVPSvwMta1zg8PHIeluec2fXM/fB7JxEREVGySKGg2WRHM+yoSPJjOyIarSMD1m3ham+H0HXHkHa7dm3NC5VhbCKiuKrM6VGh6B2hn+XWrFQPjYiIiIiIiKjfYWiaqBv0gI6a3dUo3xwMR5dvKUPt3prO8jADlxrRHJ2nQORGNEcr/SukEgxGexEQoUA0PMbl/heMlrApMtQSHRGKDgWjM0w6nGovNErqEoobRhA6ZiiaDdFERDSIiQBgapJAE9BpuFoNN1bDaK3WjPbqtjZraU3sC4vwSWR+4IOlqn98gZUAVmafgYeL5mFN5oRUD4eIiIho0GtVbWhVbUl/XHtEENupR1zWvNGhayOI7Y1owPYiXXPDFXAjXfOEfrphkYP8zH9E1K9IAGWWTKM5OjIcXW1xpXp4RERERERERAMGQ9NEnWiuaA4FpIMt0hVbyxFwD7Kkp9pJc3Q/CkdLSCMMHRAe+BFqjg6FpPtLMFpAIi2qHVqPCEgHr1uVXghBBUIh6MhQdHP4OqC0Soj+kcUiIiLq04SWeLhatwtodoQC1RHt1Q5AtwkoHom0LwIw1fX9hbQGgbfyzsUjRXOxOX1UqodDRERERD3MrVrhVq2oRvKCgVbdB1fADZfmRnrAEx2s1txID4WrjXk0D9KN+YM/XQE30jQPm7CJKGkkgMPWHCMQXeIsNFqkG0zOVA+PiIiIiIiIaMBjaJooJOAJoHZ7DTZ9VI+yUIt087GmVA+r97QPR4ebo/tROFpCQoM32BYdCkO3XfaiP6R4TUKGws9aKAzdFpDOMGlIV3X0xq9DeDoJRbdIiP6TMyciIhoUhIbgsroZ6DRcjb69DPcJFa/kT8ejRXOxx1GQ6uEQERERUT/mVSyoslhQhYwTfixnRIN1OEjtighhG2HriBB2WiBynuA0p+bt09/HiSh5NAgctOUZrdHhcPROxzA0m+ypHh4RERERERHRoMXQNFFI5fYKrPtRcaqH0fNUQGS3a47uR+HocDA6IEKBaLhDwWhPqDG6LwejJeyKNALQwWC0brREZ5g0ONReGL8uobTCCESrLdGhaKVFQhlkhepERESDRV/9tteiWPH8sJl4YsSVOGrNTvVwiIiIiIiitKg2tKg2lCHrhB5HkXowcB0OVkdcjmy5jtWGna5Fh7Btur/Pfr8nGkwCQsE+W35UY3RJKBztUS2pHh4RERERERERtcPQNFHIkNOHQKgCUuvLodtuUOI0R2f3/XB0MBjtQ0CEAtHwBNuihQd+uPtsMFpAIl1ta4Z2mTRkmHTjskvVYFF6YSD+6JboDqHo1j77FhIREdEgU2dy4A+Fs/H74ZehxpweeyYpAdG3v78SERERESVCFwoaTQ40mhwn/FgmPRBqt25rwU6PaLdOC7VbG4Hr8Dztprk0N8xSS8KrIxrYvMKEPY6hwdboiHD0LkcB/Ap3txIRERERERH1F1yLJwox2cwwOUzwN/lTPZTuUeI0R/fxcHRkMDoAT1tbdOi6FHqqh9iBWUS3RAfD0DoyQqHodFVHb7zlwh0dilbaBaSFt++2SBIREREBQJklE08OvxzPDpsV/7TE4bA0A9NERERERB0EFBNqlXTUxjv4sBusui+i8drTod06svE6HNROb9eQHQxqe6CCbQ3Uv7kVM3Y5hqHEUYgdzraA9F57PjShpnp4RERERERERHSCGJomimB2WfpuaNoIRwcD0lHN0WrfDJJISOjwh9qiQ03Rwt1Hg9ESDkUG26GNlmgNLlU3ptnVXtjgr4dD0IgfimbxCxEREfVT+215eKxoLl4cegF8ijn2TAxLExERERH1Kq9iQZXFgipknPBjOSNardvarT1GCNsIW0eEsNMiGrLTQw3ZTs3LYgjqUc2q1WiN3uEsxE7HMJQ4C7HfNgRS9MYpI4mIiIiIiIgoFRiaJorgGOZE69GW1A5CAUSWEtUareSpfTYcHQ5GB4QHfoQC0eFwNDyQfSThq0AiXdWjWqIjQ9EuVYO5F7aDCr+E0hyjKTo0TXEDgmUsRERENMBsc47AI0Xz8I8h50GPt/OZYWkiIiIion6vRbWhRbWhDFkn9DiK1JEWClu7NHfU5ciW63AbdrDpOrohOxzCtul+BrAHsXqTAyVGc/Rwozm61Jab6qERERERERERUQowNE2UKu3D0bmhcHRO3wtHB4PRAQSEOxSM9gQv96FgtEXocJn0iDC0BpdJNy6nq3qv5G8Ud+ehaOEDN9ATERHRoPGpayweLpqHd3Mndz0zw9JERERERBSiCwWNJgcaTY4TfiyTHgi1W7e1YKcH2oWtIwPXRkO2OyKoHZzfLFO/LZxiqzanYYdjOEqchaEG6WBz9DFrdqqHRkRERERERER9CEPTRD1NACJbgZLbrjm6D4ajtVBjdAChQHTE5dQGoyWcSnQoOrIlOsOkwab0Qj2zJqG2hkPRaGuHjgxF6z0/DCIiIqK+TAJ4P+t0PDxyHj7JnJjq4RARERER0SAXUEyoVdJRa04/4ceyab5gmNoIXHui2q3TA+3C1ponqhk78n4qeMrB7pIAKiwZ2BFqiw62Rwd/VlkyUj08IiIiIiIiIuoHGJomShbRrjk6T4GSGwpHm/pOOFpHwGiIjmyLDgg3dBFIyZgUyGD4ORSCDrZEa8gIhaRdJg298RYKX0QzdPum6BYJxQ0IbscmIiIiikmDwNK8c/Bw0TxsSh+V6uEQERERERElnUe1wKNaUIUTD+g6I1qt29qtPUbgOi2iDTsc1I41zal5B9zZDSWAI9bsiHD0cJQ4h6HEUYg6c1qqh0dERERERERE/RhD00TdFQ5Hx2qO7iPhaB0BBOAJtUUHG6P9cKcsGG0V4TC0HmyIDgWjw6HoNFXvlbOxK62h8HNzjFB0s4Ti7/kxEBGlmgTgUcxoUa1oUWzBn+F/ihUtaqxpVrSGrjertg7TwvcTkMbOvbSINqV0zYN0zRM83W3E9fRQs1J6+1Pkah6YJKv7ifoLn1Dxav50PFp0FXY7hqV6OERERERERP1CcBuMDWXIOqHHUaRuNF67NHfU5ciW63Abdni7TWQbdnibjE3392oAW4fAIVtuh+bonY5CNJnsvTgSIiIiIiIiIhosGJomikcAIjNGc3Ru3whH69AQQCgQHQ5Hh4LSuujN9K+EUwmHoXVkGC3RwZC0y6TBpvRCPbPWFoRuC0UjqilaMH9HRP1EV8Hm1nB4+TiCzS2qFbpQemzsjSYHjlpP/HHCp7uNDFgb1wOetlB2RBA7LSqk3TavQx94jUtEfUGLYsVfCi7CEyOuxBFbTqqHQ0RERERENCjpQkGjyYFGk+OEH8ukBzoEqdMD7cLWEdti2g6Ajwhhh+Y3S8143IBQsN82BCXOwqiA9E7HMLjVJGxIIiIiIiIiIiJKEEPTRCFutxv+UQFYhtnbmqPNqY1YBYPRHgSEOxSM9hhBaU34emUMCiTSVQ0ZqoZMczAEHQ5Gh0PRai+8TcIbCkSHWqHViJ9Ki4TiBgNxRNSrJACvYkZzJ8Hmlshwc4wQc/i23g429wfJPN2tInVjJ1+48TocxA7vCEwLeDoJaUcHsdmCTYNdncmB/yucjd8XzkG1xZXq4RAREREREVGSBBQTapV01JrTT/ixwgfEOzUvjlmz4FPMSRghEREREREREdGJYWiaKETTNPjHaTDB0qvPGxmMNlqjQ9d7PhgtYYJEmqojwxRAjklHhlmDS9WM5ug0tReCYVJCaUXnoejeLM8mogEjHGyODCh3N9jcotrQzGBzv6YLBQ0mJxpMzqQ8nk3zhcLW7lDrtadDC7bLCGl3bMGOPC2uXffxoB/qN8osmfjf4Zfjz8NmoTneaZKlBAT/qomIiIiIiAa7tgPiiYiIiIiIiIj6DoamiUKcTifgB9ADZQcSOgIIBaJDoehwc7QGbw9WJEuYhYRDCTZC55g15JkCyDAH26IzTDosiuypJ28TCIWgWySU5rbLanMoKN0qIVjaSTRoJRpsNsLNDDZTioV3+lUmoQVblVooUO1pa7Nu12wdbsGODGKnRTViB4PYaWzBph6y35aHx0dchRcLLoBXiXOAYTgszcA0ERERERERERERERERERH1UQxNE4UIIaBAQMfxhYjDwei2tui25uieC0YHQ9E2RUe6qiPbpCHP4ke+OYAsk4Z0kw6lF3IrwhMvFB1qifb0YC6ciHpFZLA5qpk5RrA5MvwcK8TcwmAzkUETalJbsO2aNyJ83daC7QpENF63a8FuH9JmCzaFfekcjkeK5uKNIVOhCTX2TAxLExERERERERERERERERFRP8HQNFEEW4Ydra2tcW8PBqO9CAh3KBjtMS73TDBaQgVgVXSkqToyTBpyzAEUmP0otPiRZu6FlmhdQmlF56HoQM8Pg4i61j7YHBVu7mawuVW1opnBZqJ+x61a4VatSWvBjgxXp3USsE6P0YIdDGq3zace54Fp1PvWucbgoaJ5eCf3rK5nZliaiIiIiIiIiIiIiIiIiIj6CYamiSJYrVa0tLYYjdEB4YY/1BgdCIWkeyIYbRYSDkXCZdKQbQpgqNmPYRY/cq0a1J7OoQSC4efYoWhAaZUQzDgRJU13g83h2xlsJqLepgkV9WYn6s3Jb8F2BdrC1entWrBdEUHstIAnKqQdDmLbdD9bsJNMAliVdToeLpqHf2dNTPVwiIiIiIiIiIiIiIiIiIiIko6haaKQ+uZq7Gz5EF5TK5KfEpawCok0VUO2ScMQsx9DrMHW6EyTBlMPpn6Ep10oOtQQHZ4meqIgm6ifixVsNsLNxxFsDrY6M9hMRINbb7RguyKarY1QdqgF26W5kWY0YnvYgh2iQ2Bp7tl4eOQ8bEw/OdXDISIiIiIiIiIiIiIiIiIi6jEMTROFOGzp8MqWE0oQ2xUdmWoAOWYNeZYAskwaskzBYLS5JzKSuoTSilArdLum6GYJtVVCBHrgeYn6gHjB5mBA2RYz2BwOLccLNke2PjPYTETUdyW7BdthtF6HGq4jWrBd4esRIe1wC3ZkSDs8vb+0YPuEir/nn49Hi+Zil2NYqodDRERERERERERERERERETU4xiaJgqxmKwwwYoAvJ3O51CCbdHZZg3Z5nAwOhiOTnYwWvgjmqFb2oWiW4KB6aSXYhP1MAmgRbWiQXWgweRAg8mOBpMDjWrb5eD1tssNJjua1OA/BpuJiCjZWlUbWlUbKiwn/liRLdiuULi6fbN1mhYKXEe1Zbe1YAdvC86X7BbsVsWCvxTMxBMjrsBhW25SH5uIiIiIiIiIiIiIiIiIiKgvY2iaKES2VmGIuQXH/CY4FQ1Z5mAYOtsUMC5nmTRYlOQFVxR356Fo4T2h4muipNMh0KTa2gWb7WgMh5tDQefGcBg6FIxuDIeh1eBlhp2JiGig6o0WbFdEs3X7FmxXwB0MYEe0ZadrHgDA8wUz8fTwy1BtcSVlbERERERERERERERERERERP0JQ9NEYX7g6rw6WBUJazKC0XooBN0CIxTdPiAttBN/GqJEaRBR4eVwg3NjVKuzPdT4HLsBukm1QTLwTERE1GuS2YIdl5SA4KF6REREREREREREREREREQ0sDE0TRQiXLnI8GmQjsQCI8LXMQgdddkNiOSeTZ0GsYBQQkFne0TLc/T1BtWOpqhQdHQDdLPJnuqXQURERH1JOCzNwDQREREREREREREREREREQ0CDE0ThQkB1Q0EHMGrSmsoBB0nFC18AOMllAifUKOCzQ0mR1SY2QhBm+xGA3SjyR7V/tyq2lL9MoiIiGigYFiaiIiIiIiIiIiIiIiIiIgGIYamiSLYt9hhaWgIhqL1VI+GUk0C8CjmiPByqL1ZbbscbnVujGqAjr7sUS2pfilEREREbRiWJiIiIiIiIiIiIiIiIiKiQYihaaIIfutwOJrqUz0MSgIJoFWxtgWbQwHmxoigc4PJ3qHxuTEchg4Fo/0KPyaJiIiIiIiIiIiIiIiIiIiIiIiI+jumAYmoz5EAmlVbVJg5GGi2R7U6d9YA3ajaEWDgmYiIiIiIiIiIiIiIiIiIiIiIiIjA0DQRJZkOgaZw4DmitdlocA61PDea7EYI2ghER9ymCyXVL4WIiIiIiIiIiIiIiIiIiIiIiIiIBgiGponIoEFEhZeN1mbjeqjhuV0DdJNqN25rUm2QDDwTERERERERERERERERERERERERUR/C0DTRAOEXakSrcyjcHBF0bggFnRs7aYBuUW2pfhlEREREREREREREREREREREREREREnH0DRRH+AVprYgc6jVudFoeG4LPTeGgs7RDdDB292qNdUvg4iIiIiIiIiIiIiIiIiIiIiIiIioT2JomugESAAexWwEncOh5w5hZ9VutD+HrzdEND57FUuqXwoRERERERERERERERERERERERER0YDF0DRRiKZLfGgbhUCes631WQ0FoMNhaNXedj3U+OxX+N+IiIiIiIiIiIiIiIiIiIiIiIiIiKgvY9qTKEQI4IbchZB5ItVDISIiIiIiIiIiIiIiIiIiIiIiIiKiJFJSPQCivkIRAmnSm+phEBERERERERERERERERERERERERFRkjE0TRSB/yGIiIiIiIiIiIiIiIiIiIiIiIiIiAYeZkSJIthsllQPgYiIiIiIiIiIiIiIiIiIiIiIiIiIkoyhaaIIox0y1UMgIiIiIiIiIiIiIiIiIiIiIiIiIqIkY2iaiIiIiIiIiIiIiIiIiIiIiIiIiIiIBjSGpomIiIiIiIiIiIiIiIiIiIiIiIiIiGhAY2iaiIiIiIiIiIiIiIiIiIiIiIiIiIiIBjSGpomIiIiIiIiIiIiIiIiIiIiIiIiIiGhAY2iaiIiIiIiIiIiIiIiIiIiIiIiIiIiIBjSGpomIiIiIiIiIiIiIiIiIiIiIiIiIiGhAM6V6AL3po48+wsqVK9HQ0IBhw4Zh4cKFGDduXKqHRURERERERERERERERERERERERERERD1o0DRNb9iwAW+88QauuOIK/OIXv8Do0aPx9NNPo6amJtVDIyIiIiIiIiIiIiIiIiIiIiIiIiIioh40aELTq1atwrRp03DBBRegoKAAN998MzIyMvDvf/871UMjIiIiIiIiIiIiIiIiIiIiIiIiIiKiHmRK9QB6QyAQwKFDhzB79uyo6RMnTsS+fftSNCrqi5ZfU5TqIRARERERERERERERERERERERERERUZINiqbp5uZm6LoOl8sVNd3lcqGhoSFFoyIiIiIiIiIiIiIiIiIiIiIiIiIiIqLeMChC00RERERERERERERERERERERERERERDR4DYrQdFpaGhRFQWNjY9T0xsZGZGRkpGhURERERERERERERERERERERERERERE1BsGRWjaZDJh5MiR2LFjR9T0HTt2YPTo0SkaFREREREREREREREREREREREREREREfWGQRGaBoBLL70Un376KdasWYOysjK8/vrraGhowIUXXpjqoQ1q1dXVuPvuu3Hw4MFUD6VbXnjhBTz99NOpHgYREQ1SvbX8fPTRR/H3v/+9R5+jv1i0aBFWrlyZ8Py7du3C3Xffjaamph4cFRFR7+ntdbfufu52Ze3atfje976XtMcLS/Y4e9Lx/A7ffvttPPDAAz03KCIi6jf60zKvO9q/roH6OomI6Pj192XD3XffjY0bN6Z6GCnHdWIiGgz6+zIL6Jnt0D2R7+lvWafj2T7eV/YTc3lMRD3BlOoB9JZzzjkHzc3NePfdd9HQ0IBhw4bhnnvuQU5OTqqHllIvvPACPvvsMwCAoijIzMzE6aefjmuuuQZOpzPFo4tv0aJFmDlzJubMmZPqocS1efNmvPfeeygrK4Ou68jKysKYMWNw2223JeXxH330URQWFuLmm29OyuMREVHi+vPys6amBt/85jcxderUqNt+97vf4cCBA7j++uuN5et3v/tdqKqaiqFGeeaZZ7Bt2zb88Ic/xMSJExO6T7K/K9x///2wWCxJeSwiouPR35c9AGCxWJCbm4tZs2bhggsu6PR+yf7cPeecc3D66acn7fG6KxAI4L777oPf78cjjzwCh8PR5X2qq6vxX//1X7j//vtx0kknnfAYsrOz8dhjjyEtLe2EH6u7XnjhBTQ3N+Oee+5J+D69uc779ttvY+PGjfjlL3/ZrfutXbsWr732Gp555pkeGhkRUfLE+1ztz59lmzZtwocffojS0lJomoa8vDxMmjQJs2bNgsvlSvXwAHAbLhFRXzCQloHh9UQhBB588MGo/dwtLS3GemfkeuRjjz2W0DpoTxrs68RERIkaiMusMIfDgcLCQsyfPx/jx4+Pe7+e+Ly+8cYbk/ZYx+PQoUP47W9/i5NPPhmLFi1K6D7J/p2nevv4/v37sXz5cuzZswc+nw+5ubk455xzcPnll8NsNhvz3X333fj2t7+Ns846K2VjJaLBYdCEpgFg5syZmDlzZqqH0edMmDABd955JzRNQ1lZGV588UW0trbiP/7jP477MQOBAEymQfXnFaWkpAR//vOfMXfuXNx+++1QFAXl5eXYvHlzqodGRERJ0l+Xn9nZ2Vi7dm1UaPro0aM4evRohw0QvRHC0zQNiqJACBHz9vr6epSUlODSSy/FmjVrugxNJ/s9DD9eenp60h6TiOh49ddlz1VXXYWLLroIXq8Xa9euxcsvvwy73Y5zzjkn7niS/blrsVhSevDL5s2bkZubC7vdjs8//xwXXXRRp/MHAoGkPn/4fc3IyEjq4/YHg337BBHRieqrn6NLlizBihUrMGvWLMyfPx/Z2dmorKxEcXExPv74Y8ybNy/VQ0yqvvp7ICIayPryZ29WVhbWrl0btbxbv3490tPTUVtbGzVvb6wHdvVecZ2YiKhn9eVl1g9+8AOMGDECTU1NWLJkCZ5++mn8z//8D/Ly8jrM21Of16k+eKi4uBgXXXQR1q1bh7KyMhQUFHQ6f08sB1O5fXzLli3405/+hClTpuDee+9FWloa9u7di8WLF2Pnzp249957U/L325f/3xBRz+P/for60pGdnY1zzjkHn376qXF7rEao9i1M4XnGjh2LDz/8EJqm4YknnsC6devwwQcfoLy8HBaLBePGjcPChQuRlZXVo6/p3//+N1auXIna2lpkZ2fjsssui2oya21txVtvvYUtW7agpaUFubm5mDdvntFI/ve//x179uwxbpszZw7OP//8hJ9/69atGDVqFK688kpjWn5+PiZNmgQA8Hq9+MlPfoLbb7896gipHTt24Omnn8YjjzwCl8uFZcuWobi4GI2NjXA4HJg4cSLuvPNOvPDCC9i9ezd2796Njz76CADw4IMPIjc3F8eOHcPixYuxe/duWCwWnHLKKVi4cKHxO478XX3wwQfw+/248MILcc011+Cdd97Bxx9/DCEELrnkElx++eUn9HsgIhrI+uvy89xzz8UHH3yAqqoqY4NEcXExzjrrLOzevTtq3vZHtC9atAjTp09HXV0dPv/8c9hsNlxyySVRbc41NTV4/fXXUVJSAgCYOHEibrzxRmRnZ0e9B7Nnz8a7776L6upqPP3007DZbDHH++mnn+K0007DxRdfjJ///Odobm6OCnfHeg8LCgpQU1ODxYsXY/HixQCA5557DgCwd+9eLFmyBAcPHoTD4cCkSZNw3XXXwW63G6+5oKAAVqsVn376KXJycvD//t//69Bc/f777+PTTz9FVVUVHA4HTjvtNCxYsCDlG16IaGDrr8sem81mjPuaa67BF198gc2bN+Occ85J+HP37rvvxi233IIdO3Zg+/btcLlcmD9/Ps477zzjeerr6/Hmm2/iq6++gt/vx5AhQ7Bw4UKccsopHZo5wu/LJZdcgnfeeQdNTU049dRTceuttxqB7QMHDmDp0qUoLS1FIBDA8OHDcf3112P06NHdfg+Ki4tx3nnnweFwYPXq1R12EC9atAjTpk1DbW0tNm3ahIkTJxqnT/7tb38LABg3bhzuu+8+AMGmkZUrV6KqqgrZ2dm46KKLMGvWLCiKYrxfN998M0pKSvDVV1/hoosuwsyZM6NaunRdx0svvYSdO3eisbERWVlZmDFjBmbPnm08Tk8J/x1OnDgRK1euhM/nw9e+9jXcfPPNsFqtSVvnjfwb37RpE95++21UVlbCbDZj+PDh+Na3voXt27dj2bJlxvsGALfffjvOP//8Tpf3u3btwosvvhh1v7lz52LevHkIBAJYunQp1q9fj9bWVhQUFODqq6/Gaaed1qPvKxFRMsT7HAWC2zSff/55bNmyBVarFbNnz45aH+xqPSm8PP7P//xPvP7666iursaoUaNw2223xdxhHs+BAwewfPlyLFiwALNnzzam5+TkYMKECWhtbTWmdbWduCutra1YvHgxtmzZAp/Ph5EjR2LBggVRjZf79u3DkiVLcODAAaiqiqKiItx1111466234i7Pdu/ejcWLF+Pw4cOw2+2YMmUKrrvuOmOHbbzvSERE1HP6wzIwbNq0afj0008xd+5co4yiuLgY06ZNwzvvvBM1b2RjY7j189vf/jb+/e9/Y9++fcjJycGNN94YVVaR7OUU14mJiJKrPy2z0tLSkJGRgYyMDHzjG9/AT3/6U+zYsQMXXnhhQp/Xu3btwmOPPYZ7770XS5YswdGjR1FQUIBbbrkFI0eONJ4n3npZZmZmh+3mjz76KIYOHQqz2Wyc3XH69Om47rrrjGVAsraX+3w+rF+/Hj/96U/h8/lQXFyMBQsWGLeHl8133XUX1qxZg/379+P666/Ha6+9BqD72x3D79c999yDt99+G4cPH8Z3vvMdNDc3R20fr6ysxD/+8Q8cOHAAHo8HQ4cOxbx584xMU7J4vV787W9/w+mnn4477rjDmJ6Tk4OhQ4fit7/9LVavXo05c+YYLdx/+tOfjHkeeugh4z6ff/45li5disbGRkyYMCFqWz5wfN8PIn8XRDS4MDRNUaqqqvDll19CVdVu33f37t2w2+344Q9/CCklgOCROfPnz8fQoUPR3NyMf/7zn3juuefw05/+NO7jLFq0COPGjcM3v/nN43oNmzZtwmuvvYYbbrgBEydOxFdffYW///3vyMjIwKRJkyClxNNPP43W1lbcfvvtyM/PR3l5Ofx+PwDA7/dj5MiRuOyyy2C321FSUoKXX34Z2dnZmDBhQkJjyMjIQHl5OQ4fPowRI0Z0uN1qteLcc881QmphxcXFOP300+FyubBx40a8//77uPvuu1FYWIimpibs378fQPD0IZWVlRg6dCiuueYaAEB6ejrq6+vx6KOP4vzzz8eCBQugaRqWLFmCP/zhD1i0aJHxZWDPnj3IysrCT37yE5SWluIvf/kLDh8+jKKiIvz0pz/Fzp078eqrr2LixIlRXzSJiCi2/rT8TEtLw6RJk7B27VpcffXVCAQCWLduHb7zne90CE3H8sEHH2DevHn4+c9/ju3bt+P111/HmDFjMHr0aOi6jj/84Q+wWCz4yU9+AgD4+9//jv/7v//D/fffb2zAr66uxueff45vfetbMJlMUaddiiSlxNq1a3HdddchJycHo0aNwrp163DJJZd0+h5mZWXhl7/8Jc4///yoDfBHjhzB//7v/2LevHm49dZb0dLSgjfeeAMvvvgivvOd7xjzrVu3DhdccEGn77eiKFi4cCHy8vJQU1OD1157Da+99hruvPPOLt9DIqJk6E/LnvbMZjM0TTOuJ/K5CwDLli3Dddddh2uvvRbFxcV48cUXMXbsWOTk5MDr9eLRRx9Feno6vvvd7yIzMxNHjhzp9PFqamqwbt06/Od//id8Ph9eeukl/O1vf8P3vvc9AIDH48F5551nnL7xo48+wtNPP43f/va33To9ZE1NDXbv3o277roLFosFr776asx1xVWrVuHKK6/E//t//w9SSsyZMwe/+93vjDaW8M7pTz75BG+//TZuuukmFBUV4dixY3jppZegqiouvvjiqPfrmmuuibvhV9d1ZGVl4Vvf+hbS09Nx4MABvPzyy3A6nZgxY0bCr+947d27F5mZmbj33ntRW1uLP//5z8jPz8cVV1xxwuu87f/GGxoa8Oyzz+Laa6/F5MmT4fV6jfXrc845B8eOHcO2bduM7y/hg6k6W96PHj0aCxcuxJIlS/C73/0OQHBdHwD++te/oqqqCnfffTeysrKwfft2PPPMM7j//vtjbiMgIuprYn1XAILLqssuuwxXXXUVdu3ahddeew15eXmYPHkygMTWkwKBAFasWIHbb78dZrMZL7zwAl555RX86Ec/AtC2wzh8AEss69evh9VqjVruRQrv7O9qO3FXpJT4/e9/D7vdju9///twOp349NNP8fjjj+PXv/41MjMzcfjwYTz++OM477zzcMMNN8BsNmP37t3QNC3u8qyurg5PPfUUzjvvPNxxxx2orKzESy+9BCEEbrjhBuP5E/2OREREydPXl4Fhp512GtasWYOdO3diwoQJKC0tRVVVFc4+++wOoelYli5diuuvvx5f//rX8e677+LZZ5/FQw89BJvNlvTlFNeJiYh6Rn9ZZkUKNx1Hbhvu6vM67K233sJ1112HjIwMvP7663j++efxq1/9CkKITtfL4lm/fj2mTZuGRYsW4ciRI3j55ZeRkZFhHJh7PNvLY9m4cSNycnIwfPhwTJ06FX/+859xzTXXdGg4XrJkCRYsWIDbbrsNiqJA1/UT2u74z3/+EwsWLMCQIUNgs9mwbdu2qOfzer047bTTcPXVV8NisWDDhg344x//iAceeKDLJuzu+Oqrr9Dc3BwV3A8bOXIkTjnlFKxfvx5z5szB/fffj3vvvRe33norzjjjjKiDmGpqarBhwwZ897vfhdfrxbPPPoulS5filltuAZC87wdENHgwNE346quv8L3vfQ+6rhvB4ciV3kSZzWbjS1PY9OnTjct5eXn4+te/jl/84hdGs0cseXl5yMzM7Pbzh73//vs477zzjAXf0KFDcejQIbz33nuYNGkSSkpKsH//fvzyl780FvaRR8RlZWVFLbDz8vKwc+dOfP755wmHpi+++GLs2bMHv/rVr5CVlYVRo0ZhwoQJOO+884wmzRkzZuDBBx9EXV0dsrKy0NLSgi1btuBb3/oWgOBCPyMjAxMnToTJZEJOTo7RYOJwOKCqKiwWS9SpSf79738b7Wdh3/zmN/HDH/4Qhw4dwqhRowAEdwB//etfh6IoKCgowKpVq9DQ0IBrr73WeM/ee+897Ny5k6FpIqI4+vPy8/zzz8crr7yCefPmYevWrXA4HBg3blxC9504caKxjJ01axY+/PBDlJSUYPTo0di5cyeOHDmC3/3ud8jNzQUQPGr3/vvvR0lJidFWomka7rzzTrhcrk6fa/fu3WhpacEZZ5wBAJg6dSo++OCDDqHpWO+hoihRzaYAsHLlSpx99tlRTWRf//rX8etf/xqNjY3GeHJzc7v8XUaOITc3F9dffz3+8Ic/4I477mATCRH1mP687AGCn//r1q3D0aNHow5qSeRzFwguB8LN0vPnz8fq1auxZ88e5OTkYP369WhoaMCiRYuMdokhQ4Z0+ng+nw/f/OY3kZOTAwC45ZZb8Mgjj6CiogL5+fkd1v9uuukmbNq0CV9++WVUw3VX1q5diwkTJhjjOvPMM7FmzRrjTA5h48aNw2WXXWZcr66uBtDWxhL27rvv4rrrrjMOwM3Ly8Pll1+Ojz/+OGoD8Nlnnx21ozf8eGEmkwnz5883rufm5qK0tBQbNmzolR3ENpsN3/jGN4z10rPPPhs7d+7EFVdcccLrvO3/xg8dOgRN03DWWWcZv+/CwkLjMaxWKxRF6XDqz86W9yaTCXa7HUKIqPtVVlZiw4YNePDBB43nuvjii1FSUoJPPvkEX//615P1FhIR9ZhY3xUARJ1Zb+jQoTh48CBWrVpl7HxPZD1J0zTcfPPNGDp0KABg9uzZ+Nvf/gYpJYQQUFUVQ4cONQ5giaWiogJ5eXldnka3q+3EXdm5cycOHz6MJ554wti5f/XVV2Pr1q1Yt24dLrvsMrz33nsYMWIEbr31VuN+kTuYYy3PPv74Y2RmZkZtn7322mvxyiuvYP78+cbO8ES/IxERUfL09WVgmKqqOO+881BcXIwJEyaguLgYZ599trEM6coll1xiLAuvueYafPbZZzh8+DDGjh2b9OUU14mJiHpGf1lmhXm9Xrz11ltQFCVqn2RXn9dh8+fPxymnnAIg2Lr88MMPo66uDtnZ2V2ul8WSkZGBm266CUIIFBQUoKKiAqtWrTL2IR7P9vJYwmdbAILLOovFgq1bt0aVKwLB7YeR0050u+PcuXNx6qmnxh3XiBEjokLWV155JbZu3YqNGzfiqquuSvj1daWiogJA/N/HsGHDsGbNGgAwvis4HI4O22k1TcMdd9xhHCR9wQUXYO3atcbtx/v9gIgGL4amCWPHjsWtt94Kn8+HNWvWoKqqCrNmzer24xQWFnb4Qnbo0CEsW7YMhw8fRktLizG9sy8SP/7xj7v93JHKyso6HM02duxYbN26FQBQWlqKjIyMuAtlXdexYsUKbNiwAfX19QgEAggEAhg/fnzCY7BarbjnnntQWVmJXbt2Yf/+/ViyZAlWrFiB+++/Hy6XCyeddBIKCwvx6aef4sorr8Tnn38Op9OJ008/HUBwYb169Wr813/9F0499VScdtppmDRpUtw2TiD4fu/Zs8doRotUWVlp7EAuKCiICnS5XK4OX2hdLheampoSfs1ERINNf15+nnrqqZBSYseOHSguLu7WUeDDhw+Pup6ZmWksL8rKypCZmWkEpoHgSmlGRgbKysqM0HRmZmaXgWkAWLNmDc4++2xjR/hZZ52F1157Dfv378fJJ59szBfrPYzl0KFDqKqqwhdffGFMCx99X1VVZYwpkQOGSkpKsGLFCpSVlcHtdkNKiUAggMbGxhM6+IuIqDP9ddmzdOlSLFu2DIFAAKqqYs6cObjggguM2xM9UDMy5KqqKtLS0tDY2AgAOHz4MIYPHx51Or6uZGVlGRuXgeAOBSEEysrKkJ+fj8bGRvzrX//Czp070dTUBF3X4fP5UFNTk/Bz6LqOtWvXRoV8w40iCxYsiPo9JPI+NDU1oba2Fq+88gpeffVVY3qs1pTwQbed+fjjj1FcXIyamhr4/X5omtatjf5PPfUU9uzZAwDIzs7Gr371q4Tv2369NCMjw2h/jifRdd72f+MjRozAhAkT8MADD+DUU0/FhAkTcNZZZ3X593I8y/vS0lJIKfHAAw9ETe/udgUiolSKt441evToqOsnn3wyNm3aZFxP5HPTZDIZO96B4PphIBBAa2srnE4nsrKy8Otf/zopr6Or7cRdKS0thc/nw7333hs13e/3o6qqCkDwO8iZZ57Z7XGdfPLJUcvBsWPHIhAIoKqqyljvZpkFEVHv60/LwOnTp+PXv/41GhoasH79etxzzz0J3zdyG294fJHbeJO1nBro68RERKnUX5ZZjz76KIQQ8Pl8yMjIwB133BG1HErk8xqIXnaFA7VNTU3Izs4+rvWyk08+2ThDLhB83/71r3/B7XbDbrcf1/by9iorK7F3717cfffdAAAhBKZMmdLhjPRAYsvB7mx37Op99Xq9WLZsGbZt24aGhgZomga/399hP3BnIrfRTpkyxWh97gk5OTlGYBqI3j+d7O8HRDQ4MDRNsFgsRgPXTTfdhMceewzvvPMO5s2bBwAx2xJjLVzCbRthXq8X//u//4sJEybgzjvvRHp6Opqbm/HII490ehqMnhL5haczK1euxPvvv48bb7wRhYWFsNlseOutt44rQDxkyBAMGTIEM2bMwBVXXIGf//zn+Pjjj433dsaMGfjggw9w5ZVXori4GFOnTjXe7+zsbPzmN79BSUkJSkpK8Oabb2LZsmX47//+77hHikspcfrpp8c8lURkOC3WKbxjTYs8jQsREUXrz8tPRVEwdepUrFixAvv378dtt92W8H2TsbxIpPGktbUVmzZtQiAQMI4wBoIb2ouLi6NC0+3fw3iklJg+fTouvfTSDrdFBp+6Gl9NTQ1+//vfY8aMGZg/fz6cTidKS0vx3HPPIRAIJDQWIqLj0V+XPZdeeimmT59utCy2XzdLtAmrfZukEKJH11n++te/orGxEQsXLkROTg7MZjMef/zxbr0nO3bsQG1tLZ5//nk8//zzxnRd17Fx48aoxupE3ofw6/3617+OMWPGdDpvV4+3YcMGvPHGG1iwYAFGjx4Nu92Ojz76CJs3b+5yHGG33nqr0Xoe6ztCZ9rPn8jvM9F13vZ/44qi4Ec/+hH279+Pr776CsXFxXjrrbdw3333dTgldNjxLu/DjTv3339/h9eYyEFeREQ9xW63w+12d5ge3iEcKdF1rEiJfm7G+vwHgsvGROXn52PPnj0IBAJdtk3Hkuh2Yl3X4XK5Yp5+OXw2wZ6U6HckIiLq3EBaBkYaOnQoioqK8NxzzyEjIwOjR4+O287ZXuRYwuPoiW28A32dmIgo2QbiMuuuu+5CYWEhHA4H0tLSOtye6HpPMpZdiUrW9vI1a9ZA13X87Gc/M6aFx9w+fJ3ocjDR7Y5d/X28+eab+PLLL7FgwQLk5+fDYrHghRde6NZ+zl/84hfG5XjryPn5+QCCB2XFWnYfO3asyzNGAp3vn07m9wMiGjwYmqYO5s6di6eeegoXXHABMjMzkZaWhsOHD0fN0/56LOXl5WhubsY111yDvLw8AIg6gq2nFBQUYN++fVGnVNizZ4/RLF1UVISGhgaUlZXFbJveu3cvJk2ahKlTpwIILmArKiqijlo6Hrm5ubBYLPB6vca0KVOmYPHixfjwww9RWlqK//iP/4i6j9lsxhlnnIEzzjgDl19+OX784x9j7969OPXUU2EymTp8KS0qKsIXX3yB7Ozs49phQEREx6+/LT+nT5+OFStW4LTTTktaM3JBQQHq6+tRXV1ttE1XVVWhoaEBw4YN69ZjrV+/Hunp6R0aUvbt24c333wTCxcu7HTFVlXVmMvJRFe+O3Pw4EEEAgEsXLjQCChu27bthB6TiOh49Jdlj9PpPOHP3q6MGDECn332GZqamhJum66rq4vaOH3w4EFIKY31xD179uCmm27CGWecAQBobGxEQ0NDt8ZVXFyMyZMnG8H2sNWrV0edmjGW8Dpd5PLM5XIhMzMTVVVVmDZtWrfG0t6ePXtw8sknR52eMNyamaisrKwTGkNnkr3OK4TA6NGjMXr0aMydOxcPPPAANmzYgBEjRsT83pDI8j7WGEeMGAEpJRoaGoxThhIR9QX5+fnYvn27sZM17NChQ8ZOzK60PyPAgQMHjOVmb64nnXvuuVi9ejU+/PBD49TJkVpbW+FwOLrcTtyVkSNHorGxEUII4ztSeyNGjMDOnTvjPkasZUVBQQG++OIL6LpuvFd79uyByWSK+zxERHT8BtIysL3p06fjxRdfjGpyPlHJXE4N9HViIqJkG4jLrKysrF7ZNtzZelksBw4ciHqf9+/fj8zMTKNl+kS3l2uahs8++wzXXnutsX057C9/+QvWrl2LuXPnxr1/T2933Lt3L6ZOnWo0Xvv9flRWVnbrd5XIvKeeeirS0tKwcuXKDoHmQ4cOYefOnbjuuuuMabG203Ylmd8PiGjw6FhDRYPe+PHjUVBQgHfffRcAcMopp6C0tBTFxcWorKzEe++9h71793b5OOGdmB999BGqqqqwbds2LF26tMv7Pf7443jrrbe6nK++vh6lpaVR/5qamjBnzhysW7cOH330ESoqKrB69Wp8/vnnuOyyy4zXM2rUKPzxj3/El19+iaqqKuzYscM4cjg/Px8lJSXYs2cPysrK8Pe//71bp18GgLfffhuLFy/Grl27UFVVhdLSUrz44ovweDyYNGmSMZ/D4cBZZ52FN998E+PGjYv6ort27VqsWbMGR44cQVVVFdauXQtVVY0vHjk5OThw4ACqq6uNU0XPnDkTbrcbzz77LPbv32+8tpdeegkej6dbr4GIiLqnvyw/w/Ly8vDkk0/i29/+dsL36cqECRMwfPhwPP/88zh48CAOHjyI559/HkVFRd1eeQ9vUC8sLIz6N23aNAghsGHDhk7vn5ubiz179qCurs44W8Tll1+OgwcP4uWXX0ZpaSkqKyuxdetWvPzyy90aW35+PqSU+OCDD1BVVYX169dj9erV3XoMIqJk6G/Lnp40ZcoUuFwu/OEPf8Du3btRVVWFLVu2dLqxPNyeUVpain379uGVV17B6aefbqyX5efnY926dTh27BgOHDiAZ599tltB3aamJmzduhVTp07tsDybPn06du/ejcrKyrj3T09Ph8ViwVdffYXGxka0trYCAObNm4eVK1di1apVKC8vx9GjR/Hpp59i+fLlCY8t/PoOHTqE7du3o6KiAu+88w52797drcfoSclc5923bx/eeecdHDhwADU1Ndi6dStqa2uNg7pyc3NRW1uLQ4cOoampCX6/P6HlfU5ODvx+P3bs2IGmpiZ4vV4MHToUU6ZMwV//+lds3LgRVVVVOHjwIFauXNkrB5ITEcVz0UUXobq6Gq+99hoOHz6M8vJyrFq1Chs2bMCcOXMSeoz9+/dj+fLlqKiowCeffILPPvvMOJNPstaT6urq8POf/7zTz8yTTz4Zc+bMweLFi/HGG29g7969qKmpwa5du/CXv/wFH3zwAQB0uZ24KxMmTMDo0aPxhz/8Adu3b0dVVRX27duHf/3rX8Yyc86cOSgtLcVLL71kvK9r1qwxtifHWp5ddNFFqK+vx6uvvoqysjJs27YNb731FmbOnMnWKyKiHjCQloHtTZ06FU888QQuueSSbj9fPMlaTnGdmIio+wbyMqsndbVeFkt9fT3eeOMNlJeXY+PGjVi5cqWxPD3e7eWRtm/fjubmZsyYMaPDcvCcc87B2rVrO23K7untjvn5+di8eTMOHTqEI0eO4Pnnn++Rs+larVbccsst2L59O1588UWUlpaipqYGn3/+OZ555hmMGTMGs2bNMubPyclBSUkJGhoa0NLSkvDzJOv7ARENHqyipZhmz56NF198EZdddhlOO+00zJ07F0uXLoXP58OUKVNw0UUXYevWrZ0+Rnp6Or75zW9iyZIl+OijjzB8+HDccMMNeOqppzq9X1VVVUJtVR988IGxATzspptuwsUXX4wbb7wR77//Pt544w1kZ2fj5ptvNsLKiqLgBz/4ARYvXoy//OUv8Hg8yMvLM47iuvLKK1FdXY2nnnoKFosF06ZNw5QpU3Ds2LEuxxQ2btw4fPzxx3jhhRfQ2NgIu92OYcOG4Xvf+x7GjRsXNe/06dPx2Wef4fzzz4+a7nA48N577+HNN9+EpmkoKCjAd77zHeNIttmzZ+Ovf/0rHnjgAfh8Pjz44IPIzc3Fz372M7z11lt46qmn4Pf7kZ2djYkTJ7J5moioF/SH5Wckp9PZrfm7IoTAf/7nf+L111/HY489BiC4k/mmm25K+PTHQPDI4tLSUtx0000dbjOZTJg0aRKKi4sxffr0uI8xb948vPLKK/jv//5vBAIBPPfccxg+fDjuu+8+LF26FI8++ih0XUdeXh7OPPPMbr3O4cOH48Ybb8R7772HpUuXYvTo0bj++uvx7LPPdutxiIiSob8te3qK1WrFT37yE7z55pt45plnoGka8vPzsXDhwrj3ycnJwbnnnotnnnkGzc3NmDhxIm677Tbj9ttvvx0vv/wyfvOb3yAzMxNz5841DsRJxGeffQaTyYRTTz21w22jRo1CVlYWiouLce2118a8v6qquPHGG/HOO+9g2bJlGDt2LO677z7MmDEDFosF77//Pt566y1YLBYMGzYMM2fOTHhsAHDhhRfi8OHDeP755yGlxOTJk3HppZdi7dq13XqcnpLMdV6Hw4F9+/bhww8/hNvtRlZWFq666iqj1Wzy5MnYtGkTnnjiCbS2tuL222/H+eef3+XyfsyYMbjwwgvx3HPPobm5GXPnzsW8efNw++23Y/ny5Vi8eDHq6urgdDoxatQoNk8TUUrl5eUZ60NPPvkkAoEAhg4dim9961s4/fTTE3qMSy+9FEeOHMHy5cthsVgwb948o5UqWetJmqahvLw85impI11//fU46aST8NFHH2Ht2rXQNA25ubn42te+ZiwTzzzzzE63E3dFCIEf/OAHWLp0KV566SU0NTXB5XJhzJgxxlkKi4qKcO+992LJkiV48MEHYTKZcNJJJxnvabzlWXj79K9+9SvY7XZMmTIF11xzTbfeKyIiSsxAWwZGUhQl4bMtJSorKyspyymuExMRdd9AXmb1pK7Wy2KZMmUKdF3H7373OwghMH36dCNcfrzbyyMVFxdj/PjxSEtL63Db2Wefjbfeegs7duyI2yDe09sdb7jhBvztb3/DI488AofDgUsuuQR+v79bj5GoyZMn47777sO7776Lxx9/HD6fDzk5OZgxYwYuv/zyqO26N9xwA/7xj3/gZz/7GTIzM/HQQw8l9BzJ+n5ARIOHqK+vj3/oChH1uA0bNuDll1/Go48+yiYRIiIiIiKiXvD2229j48aN+OUvf5nqoRARERERERERERFRL3n00UdRWFiIm2++OdVDISKiFGH1LFGKeL1eNDY2Yvny5ZgxYwYD00REREREREREREREREREREREREREPYShaaIUWblyJZYvX44xY8bgqquuSvVwiIiIiIiIiIiIiIiIiIiIiIiIiIgGLFFfXy9TPQgiIiIiIiIiIiIiIiIiIiIiIiIiIiKinqKkegBEREREREREREREREREREREREREREREPYmhaSIiIiIiIiIiIiIiIiIiIiIiIiIiIhrQGJomIiIiIiIiIiIiIiIiIiIiIiIiIiKiAY2haSIiIiIiIiIiIiLq4O6778bbb7+d6mEAANauXYu7774b1dXVSX3ct99+G3fffXdSH7O7Yr3PVVVVeOqpp3DPPffg7rvvxubNm3vsPUjECy+8gEWLFvX68xIRERERERERERElkynVAyAiIiIiIiIiIiIayNauXYsXX3wx5m0zZ87EzTff3LsDirB+/Xo0NTXhkksuSdkYeoLH48Hq1auxadMmVFZWIhAIICcnBxMnTsTFF1+MoUOHpnqInXr55Zdx5MgRzJ8/H06nEyNHjkRJSUmPPuexY8fwxRdfYNq0acjNze3R5yIiIiIiIiIiIiJKBYamiYiIiIiIiIiIiHrB3LlzMWTIkKhp+fn5KRpN0Pr163Hs2LGYoen/+7//g6L0v5MVVldX48knn0RVVRUmT56MadOmwWKxoLy8HBs2bMAnn3yCP/3pT6kepqH9+6zrOnbv3o2ZM2di1qxZxvSpU6fi3HPPhcnUM5v1y8rKsGzZMowfP75DaPrWW2+FlLJHnpeIiIiIiIiIiIiotzA0TURERERERERERNQLTj31VIwePTrVw0iY2WxO9RC6Tdd1/PGPf0R9fT1+/OMfY/z48VG3X3311ViyZEmKRhdb+/fZ7XZD0zTY7fao6YqipCzE3lNBbSIiIiIiIiIiIqLexC2dRERERERERERERCl29913Y+7cuZg3b17U9EWLFmHcuHH45je/CQBYu3YtXnzxRfzkJz/Bli1bsG7dOvh8PkycOBG33nor0tPTo+6/Y8cOLF++HIcOHYKUEvn5+bjoooswY8YMPProo9i9e7fx/GHPPfdc3DFVV1fjn//8J0pKSuDz+TBs2DBceeWVOPPMM415du3ahcceewx33XUXqqur8fHHH6O5uRljxozBLbfcEtW2vXv3bnz00UfYv38/Ghsb4XQ6ccYZZ+C6666D0+ns9vu4ceNGlJaWYv78+R0C00AwoHzDDTd0+hhbtmzBmjVrUFpaiubmZrhcLpx99tm4+uqrowLOjY2NWLJkCb766is0NTXBbrdj5MiRuP7661FYWAgAOHToEJYuXYqDBw/C4/HA5XJh3LhxuOWWW2CxWDq8z2+//TaWLVsGAFi2bBmWLVuGnJwcPPTQQ8bv/sEHH4xqgj548CCWLVuGvXv3IhAIIDc3F1OmTMEVV1wBADhy5AhWrVqFPXv2oK6uDjabDRMmTMB1112HnJwcAG1/VwDw2GOPGY99++234/zzz8cLL7yA3bt346GHHjJu03UdK1aswNq1a1FXVweXy4VzzjkH8+bNM14bEPwbHjp0KK688kr84x//wJEjR5CZmYm5c+di2rRpXf9SiYiIiIiIiIiIiJKEoWkiIiIiIiIiIiKiXuB2u9HU1BQ1rX3IOVFvvPEGnE4n5s6di5qaGnzwwQf4+9//jm9961vGPJ999hn++te/YujQoZgzZw7S0tJw5MgRbNu2DTNmzMCVV14Jt9uNuro6LFy4sMvnbGxsxEMPPQSPx4NZs2YhPT0d69atwx//+EfceeedmDJlStT8K1euhKIomDNnDlpbW7Fy5Uo8//zz+O///m9jno0bN8LtduOCCy5Aeno6jhw5guLiYhw9ehSLFi2CEKJb78vWrVsBAFOnTu3W/SKtXbsWJpMJs2bNgt1ux/79+/HBBx+grq4O//Ef/2HM98c//hFHjx7FzJkzkZubi+bmZuzevRsVFRUoLCxEU1MTnnzySaSlpeGyyy6Dw+FAbW0ttm7dCq/XGxUsDps8eTJcLhdeffVVnHnmmZg8eTKsVmvcsZaUlODpp5+Gy+XCxRdfjMzMTJSXl2Pr1q1GaHrHjh2oqKjAeeedh8zMTFRVVeHf//43Dhw4gP/5n/+B1WrFuHHjMGvWLKxevRpXXHEFCgoKAKDTZvSXX34ZxcXFmDx5Mi699FIcOnQIK1euxNGjR3HPPfdE/e6qq6vxpz/9CdOnT8e0adNQXFyMF198ESNHjjQC5kREREREREREREQ9jaFpIiIiIiIiIiIiol7w1FNPdZj2+9//HjabrduPlZaWhh/96EdGMFXXdXz44YdobW2Fw+GA2+3Ga6+9hqKiIvz0pz+NCuhKKQEAEydORGZmJlpbW3Heeed1+ZwrVqxAQ0MDfvKTnxgtzhdccAF+85vf4M0338RZZ50Fk6ltk7Pf78cDDzxgTHM6nXj99ddx9OhRIyh77bXXdggFjx49Gs8//zz27t2LsWPHdut9KSsrg91uNxqUj8ddd90VNaYLL7wQQ4YMwb/+9S9cf/31yM7ORmtrK/bu3Yvrr78ec+bMMea9/PLLjcv79u1DS0sLfvjDH+Kkk04yps+fPz/ucw8fPhwZGRl49dVXMXz48E5/L7qu4+WXX0Z6ejp+8YtfRDVzh3/H4fHPnj076r6TJk3Cww8/jM2bN+O8885DXl4exo4di9WrV2PixIkxW7ojhcPt06ZNwx133GFMz87OxrJly7Bt2zZMmjTJmF5RUYH77rsP48aNAwCcffbZ+OlPf4pPP/0UCxYs6PS5iIiIiIiIiIiIiJJFSfUAiIiIiIiIiIiIiAaDG2+8ET/60Y+i/sVqG07E+eefH9XkO3bsWOi6jtraWgDBdmG3243LL7+8w3N0t705bPv27SgqKooK1FosFlx00UVoaGhAaWlp1PxTp06NClGHA9BVVVXGtHA4WUppNHGH240PHTrU7TG63e7jCqFHCo9J13W0traiqakJY8eOhZTSeI1msxkmkwm7du1CS0tLzMex2+0AgG3btiEQCJzQmGIpLS1FVVUVZs2aFRWYBqJ/x5EBcI/Hg+bmZuTn58PhcBzXewwEXxMAXHrppVHTL7nkEiiKgu3bt0dNz8/PNwLTQLBhfejQoVF/C0REREREREREREQ9jU3TRERERERERERERL3gpJNOMgLBJ6p9k3I4NBsO8FZWVgIAhg0blpTnA4CamhpMnjy5w/SCggLj9pNPPtmYnp2dHTWfw+EAALS2thrTamtrsXjxYmzfvh0ejydqfrfb3e0x2u32Ew7iHj16FIsXL8bu3bvh8/mibguP3Ww247rrrsObb76JH//4xxg1ahROO+00TJ061Xjd48aNw1lnnYVly5Zh1apVGDduHCZNmoQpU6Z0aNc+HuHXGW7tjqelpQVvvfUWNm7c2CHgfTzvMRD8XQshkJ+fHzXd4XAgIyMDNTU1UdPb/y2E5438WyAiIiIiIiIiIiLqaQxNExEREREREREREfVRuq7HnH68bdG9SVFin+hQSgkg+NqefPJJNDc34/LLL0dBQQGsVit0XcdTTz1lzNcdQ4cORWlpKWpra2MGdbvS2tqKxx57DFarFVdffTWGDBkCs9mM+vp6/PWvf40a0yWXXIJJkyZh69at2LFjB959912sWLEC3//+9zF+/HgIIfDtb38bBw4cwLZt27Bjxw68/PLLWLFiBf7rv/4LLper2+M7Hn/+85+xb98+XHrppSgqKjKauJ977rm4f1/J1tXfAhEREREREREREVFvYGiaiIiIiIiIiIiIKMVite4GAgE0NDQc1+MNGTIEAHDs2DGjCTqW7oSvc3JyUF5e3mF6eFr79uuuHDlyBOXl5bjjjjswbdo0Y3pFRUW3HifS1772NXz++ef47LPPcOWVV3b7/rt27UJzczO+/e1vY/z48cb0HTt2xJw/Ly8Pl1xyCS655BLU1tbi17/+Nd59992o+44aNQqjRo3C/PnzsX37djz99NNYs2bNcY2v/XMDwWbs0047LeY8LS0tKCkpwdy5czFv3jxjut/v79A63R05OTmQUqKiogLDhw83prvdbjQ0NOCMM8447scmIiIiIiIiIiIi6imx6x2IiIiIiIiIiIiIqNfk5eVhz549UdM++eST424CnjhxIux2O1asWAGfzxd1W2S7r9VqRWtra0KNv2eccQZKS0uxe/duY5rf78fHH3+MjIwMjBw5sltjDLcPt3/u999/v1uPE2ny5MkYPnw4li9f3uH9DI/3zTff7HJMkXRdx6pVq6Kmeb3eDu9rdnY20tPTjfB7S0tLh9cWfo/aB+SPR1FREfLy8rB69eoOAejw88ZreF61alWHsVmtVmPcXQmHoj/44IOo6atXr4au6wxNExERERERERERUZ/EpmkiIiIiIiIiIiKiFJsxYwZeeeUV/PGPf8SECRNw5MgRfPXVV0hLSzuux7Pb7Vi4cCH+9re/4Te/+Q2mTJkCp9OJY8eOob6+Ht/97ncBBEO8GzZswBtvvIFRo0ZBCIFzzz035mNedtll+Pzzz/H73/8eF198MdLT07Fu3TqUlZXhzjvvhKqq3Rrj0KFDMWTIELz55puoq6uD0+nEl19+ibq6uuN6zQCgqiq++93v4sknn8Rjjz2GyZMnY9y4cTCZTKioqMCGDRvQ2NiIBQsWxLz/mDFjkJaWhhdeeAEXX3wxVFXFxo0b4fV6o+arqKjA448/jrPPPhvDhg2DyWTC9u3bUVZWZjz2Z599ho8++ghnnnkm8vLy4Pf7sXbtWiiKgrPOOuu4X2OYoij4xje+gaeffhq/+tWvcP755yMzMxOVlZXYu3cvFi1aBLvdjvHjx2PlypUIBALIycnB3r17sXv37g5/W0VFRVAUBStWrIDb7YbZbMaoUaOMRutIw4cPx/Tp01FcXAy3241TTjkFpaWlWLt2LU477TScfvrpJ/z6iIiIiIiIiIiIiJKNoWkiIiIiIiIiIiKiFJsxYwaqq6tRXFyML7/8EmPHjsWPfvQjPPHEE8f9mOeffz5cLheWL1+O5cuXQ1EU5OfnY+bMmcY8F110EY4ePYp169bhww8/hJQybmja5XLhZz/7Gf75z3/i448/hs/nQ2FhIb797W9j8uTJ3R6fyWTC97//fbz++ut4//33IYTAaaedhh/+8If48Y9/fNyvOy8vDz//+c+xevVqbNq0Cdu3b4emacjJycGZZ56JWbNmxb2v0+nE97//fbz55pt4++23YbVacdZZZ+HCCy/EL3/5S2O+7OxsTJkyBTt37sT69eshhEB+fj5uu+02TJ8+HQAwbtw4HDhwAF988QUaGhpgt9sxYsQI3HTTTTj55JOP+/VFmjhxIu677z4sW7YMq1atgq7ryMvLw5QpU4x57rrrLrz++uv45JNPoGkaxo4dix//+Mcd/rZcLhduvfVWLF++HC+99BJ0Xcftt98eMzQNALfccgtyc3Oxdu1abN26FS6XC7Nnz8a8efMghEjK6yMiIiIiIiIiIiJKJlFfX9/1eReJiIiIiIiIiIiIiIiIiIiIiIiIiIiI+ikl1QMgIiIiIiIiIiIiIiIiIiIiIiIiIiIi6kkMTRMREREREREREREREREREREREREREdGAxtA0ERERERERERERERERERERERERERERDWgMTRMREREREREREREREREREREREREREdGAxtA0ERERERERERERERERERERERERERERDWgMTRMREREREREREREREREREREREREREdGAxtA0ERERERERERERERERERERERERERERDWgMTRMREREREREREREREREREREREREREdGAxtA0ERERERERERERERERERERERERERERDWgMTRMREREREREREREREREREREREREREdGA9v8B7IQXF06em8AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 3240x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "legends=['1 - 4','4 - 8', '8 - 12', '12 - 16', '16 - 20', '20 - 24']\n",
    "direction_EDA = pd.pivot_table(data=traffic_df, index=['functional_classification_name'], values=timings)\n",
    "direction_EDA.plot(kind='area', figsize=(45,15), xlabel = 'Functional Classification', ylabel = 'Traffic count')\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.legend(title='Timings', labels=legends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:07.881346Z",
     "iopub.status.busy": "2021-09-20T23:09:07.880219Z",
     "iopub.status.idle": "2021-09-20T23:09:08.537540Z",
     "shell.execute_reply": "2021-09-20T23:09:08.536849Z",
     "shell.execute_reply.started": "2021-09-20T23:09:07.881298Z"
    },
    "id": "wBHslXGXJVWs",
    "outputId": "2587cc61-3c59-46c5-fc07-688548f54440"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5048"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df['station_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m8Ao6DmcouCa"
   },
   "source": [
    "In this we are trying to convert station_id into the count of each station_id\n",
    "since station_id is unique and we have **5048 unique station ID's** we had to perform this step so as to convert it into a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:08.539476Z",
     "iopub.status.busy": "2021-09-20T23:09:08.538527Z",
     "iopub.status.idle": "2021-09-20T23:09:14.930643Z",
     "shell.execute_reply": "2021-09-20T23:09:14.929476Z",
     "shell.execute_reply.started": "2021-09-20T23:09:08.539432Z"
    },
    "id": "tIx85vXlJVWs"
   },
   "outputs": [],
   "source": [
    "# def integers(x):\n",
    "#     ref = dict(traffic_df['station_id'].value_counts())\n",
    "#     if ref.get(x):\n",
    "#         return ref[x]\n",
    "\n",
    "# traffic_df['station_id'] = traffic_df['station_id'].apply(integers)\n",
    "\n",
    "\n",
    "\n",
    "station_id_element_count = dict(traffic_df['station_id'].value_counts())\n",
    "traffic_df['station_id'] = traffic_df['station_id'].map(station_id_element_count.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BqL3-g0ppmNZ"
   },
   "source": [
    "We can now see in the below density graph that the station Id has been converted into the feature and can contribute in the accuracy of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:14.932708Z",
     "iopub.status.busy": "2021-09-20T23:09:14.932081Z",
     "iopub.status.idle": "2021-09-20T23:09:47.704766Z",
     "shell.execute_reply": "2021-09-20T23:09:47.703612Z",
     "shell.execute_reply.started": "2021-09-20T23:09:14.932648Z"
    },
    "id": "w4gjM-ixJVWt",
    "outputId": "833981cd-2aa5-4f7f-aabc-b1bc3f67dcb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='station_id', ylabel='Density'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.figure(1)\n",
    "sns.distplot(traffic_df['station_id'],color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KrAxJ1jJqSt6"
   },
   "source": [
    "In the below Code cell we have performed **One Hot Encoding** on the columns **direction_of_travel_name and functional_classification** so that we can convert their unique values into the feature and they can contribute to our final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:47.706595Z",
     "iopub.status.busy": "2021-09-20T23:09:47.706363Z",
     "iopub.status.idle": "2021-09-20T23:09:56.032842Z",
     "shell.execute_reply": "2021-09-20T23:09:56.031854Z",
     "shell.execute_reply.started": "2021-09-20T23:09:47.706568Z"
    },
    "id": "D9oo2miAJVWt",
    "outputId": "b80e2b00-4cb1-4755-9251-cf71b00fbcad"
   },
   "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>date</th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>functional_classification</th>\n",
       "      <th>functional_classification_name</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>...</th>\n",
       "      <th>East</th>\n",
       "      <th>North</th>\n",
       "      <th>Northeast</th>\n",
       "      <th>Northeast-Southwest</th>\n",
       "      <th>Northwest</th>\n",
       "      <th>South</th>\n",
       "      <th>Southeast</th>\n",
       "      <th>Southeast-Northwest</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>56</td>\n",
       "      <td>3R</td>\n",
       "      <td>Rural: Principal Arterial - Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>21</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>55</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>4</td>\n",
       "      <td>4R</td>\n",
       "      <td>Rural: Minor Arterial</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-09-10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>19</td>\n",
       "      <td>3U</td>\n",
       "      <td>Urban: Principal Arterial - Other</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  day_of_data  day_of_week  direction_of_travel  \\\n",
       "0 2015-04-07            7            3                    1   \n",
       "1 2015-09-26           26            7                    7   \n",
       "3 2015-04-26           26            1                    5   \n",
       "4 2015-05-23           23            7                    3   \n",
       "6 2015-09-10           10            5                    3   \n",
       "\n",
       "  direction_of_travel_name  fips_state_code functional_classification  \\\n",
       "0                    North               56                        3R   \n",
       "1                     West               21                        1U   \n",
       "3                    South               55                        1U   \n",
       "4                     East                4                        4R   \n",
       "6                     East               19                        3U   \n",
       "\n",
       "           functional_classification_name  lane_of_travel  month_of_data  ...  \\\n",
       "0       Rural: Principal Arterial - Other               1              4  ...   \n",
       "1  Urban: Principal Arterial - Interstate               2              9  ...   \n",
       "3  Urban: Principal Arterial - Interstate               1              4  ...   \n",
       "4                   Rural: Minor Arterial               0              5  ...   \n",
       "6       Urban: Principal Arterial - Other               2              9  ...   \n",
       "\n",
       "   East  North  Northeast  Northeast-Southwest  Northwest  South  Southeast  \\\n",
       "0     0      1          0                    0          0      0          0   \n",
       "1     0      0          0                    0          0      0          0   \n",
       "3     0      0          0                    0          0      1          0   \n",
       "4     1      0          0                    0          0      0          0   \n",
       "6     1      0          0                    0          0      0          0   \n",
       "\n",
       "   Southeast-Northwest  Southwest  West  \n",
       "0                    0          0     0  \n",
       "1                    0          0     1  \n",
       "3                    0          0     0  \n",
       "4                    0          0     0  \n",
       "6                    0          0     0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is clear that '5U', '6R', '7U', '7R' functional_classification are very much less in number and therefore we can merge them all under one field.\n",
    "# In the functional_classification column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "traffic_df['functional_classification'] = traffic_df['functional_classification'].replace(['5U', '6R', '7U', '7R'], 'other_functional_classifications')\n",
    "traffic_df['functional_classification_name'] = traffic_df['functional_classification_name'].replace(['Urban: Collector', \n",
    "                                                                                                     'Rural: Minor Collector', \n",
    "                                                                                                     'Urban: Local System', \n",
    "                                                                                                     'Rural: Local System'], 'other_functional_classification_name')\n",
    "\n",
    "functional_classification_dummy = pd.get_dummies(traffic_df['functional_classification'])\n",
    "\n",
    "traffic_df = pd.concat([traffic_df, functional_classification_dummy], axis=1)\n",
    "\n",
    "\n",
    "# In the direction_of_travel_name column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "direction_of_travel_name_dummy = pd.get_dummies(traffic_df['direction_of_travel_name'])\n",
    "\n",
    "traffic_df = pd.concat([traffic_df, direction_of_travel_name_dummy], axis=1)\n",
    "\n",
    "traffic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D_md6EpHqyDJ"
   },
   "source": [
    "We can further Solidify our above mentioned proof that the **maximum amount of traffic occurs in the Direction of Northeast-Southwest** and in time range of 13 to 16 and 17 to 20.\n",
    "\n",
    "**More Proof for Pattern 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:56.034306Z",
     "iopub.status.busy": "2021-09-20T23:09:56.034080Z",
     "iopub.status.idle": "2021-09-20T23:09:57.667478Z",
     "shell.execute_reply": "2021-09-20T23:09:57.666384Z",
     "shell.execute_reply.started": "2021-09-20T23:09:56.034264Z"
    },
    "id": "i_IgO8WGJVWt",
    "outputId": "55104da6-8d83-4c8a-c5fd-0b6867180c38"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "direc = ['North', 'South', 'East', 'West', 'Northeast-Southwest', 'Southeast', 'Northwest', 'Northeast', 'Southeast-Northwest', 'Southwest']\n",
    "time_rural = pd.pivot_table(traffic_df, index=['Northeast-Southwest'], values =timings)\n",
    "plt.style.use('fivethirtyeight')\n",
    "time_rural.plot(kind='bar', figsize=(20, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t5gunyL1ttV5"
   },
   "source": [
    "With the below mentioned Area graph between **Station ID and Lane of Travel** We can conclude that:\n",
    "\n",
    "- The Reason for Maximum Traffic on the Lane number 7 and Lane number 8 was because **There are more number of stationed located on that path** and because of that people Travel more across those lanes.\n",
    "\n",
    "**More Proof for Pattern 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:57.670734Z",
     "iopub.status.busy": "2021-09-20T23:09:57.668804Z",
     "iopub.status.idle": "2021-09-20T23:09:58.214631Z",
     "shell.execute_reply": "2021-09-20T23:09:58.213527Z",
     "shell.execute_reply.started": "2021-09-20T23:09:57.670676Z"
    },
    "id": "pztDEcE_JVWt",
    "outputId": "2ccf3037-4cd1-4768-e6ba-cad4b4c4113b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c0f310ad0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timings=['1_to_4','5_to_8', '9_to_12', '13_to_16', '17_to_20', '21_to_24']\n",
    "lane_station = pd.pivot_table(traffic_df, index=['lane_of_travel'], values =['station_id'])\n",
    "plt.style.use('fivethirtyeight')\n",
    "lane_station.plot(kind='area', figsize=(20, 6));\n",
    "\n",
    "plt.legend(title='Station Id V/S Lane of Travel', labels=['Station ID Count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oJHhDjhOweNs"
   },
   "source": [
    "## **Pattern 5**\n",
    "\n",
    "From the below mentioned Bar Chart between **Functional Classification and Lane numbers** we can Conclude that:\n",
    "\n",
    "- Most number of people from **Rural Areas travel mostly on Outer Lanes** (i.e. 5, 6, 7, 8, 9) instead of main Lanes. Infact we can clearly see that There are almost negligible people from Urban Areas travelling in outer lanes except of lane 8 as there as most number of stationes situated in that lane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:58.216790Z",
     "iopub.status.busy": "2021-09-20T23:09:58.216431Z",
     "iopub.status.idle": "2021-09-20T23:09:59.741318Z",
     "shell.execute_reply": "2021-09-20T23:09:59.740391Z",
     "shell.execute_reply.started": "2021-09-20T23:09:58.216744Z"
    },
    "id": "bQyaiuhnJVWt",
    "outputId": "4d047295-f66c-4797-c900-215e6cc59cf2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c10d5f1d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "funtional_class = ['1U', '3R', '3U', '1R', '2U', '4R', '5R', '4U', 'other_functional_classifications']\n",
    "lane_functional = pd.pivot_table(traffic_df, index=['lane_of_travel'], values =funtional_class)\n",
    "plt.style.use('fivethirtyeight')\n",
    "lane_functional.plot(kind='bar', figsize=(20, 8));\n",
    "legends=['Urban: Principal Arterial - Interstate', 'Rural: Principal Arterial - Other', 'Urban: Principal Arterial - Other',\n",
    "         'Rural: Principal Arterial - Interstate', 'Urban: Principal Arterial - Other Freeways or Expressways', \n",
    "         'Rural: Minor Arterial', 'Rural: Major Collector', 'Urban: Minor Arterial', 'Other Classifications']\n",
    "plt.legend(title='Functional Classification', labels=legends)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECgkBkUTyfOP"
   },
   "source": [
    "We can further solidify our above mentioned argument that **Most amount traffic occurs in Urban: Principal Arterial - Interstate Area in hours 13 to 16 and 17 to 20 i.e. in Evening time mostly**.\n",
    "\n",
    "**More Proof for Pattern 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:09:59.743512Z",
     "iopub.status.busy": "2021-09-20T23:09:59.742576Z",
     "iopub.status.idle": "2021-09-20T23:10:00.475324Z",
     "shell.execute_reply": "2021-09-20T23:10:00.474383Z",
     "shell.execute_reply.started": "2021-09-20T23:09:59.743471Z"
    },
    "id": "V_Lh-SZjJVWu",
    "outputId": "2945ba7d-2595-4b9a-a7bd-118bdf260d74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_rural = pd.pivot_table(traffic_df, index=['1U'], values =timings)\n",
    "plt.style.use('fivethirtyeight')\n",
    "time_rural.plot(kind='barh', figsize=(20, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:10:00.476722Z",
     "iopub.status.busy": "2021-09-20T23:10:00.476482Z",
     "iopub.status.idle": "2021-09-20T23:10:00.501000Z",
     "shell.execute_reply": "2021-09-20T23:10:00.500168Z",
     "shell.execute_reply.started": "2021-09-20T23:10:00.476693Z"
    },
    "id": "o3GGkNPjJVWu",
    "outputId": "af728487-896f-44ab-c77e-2e7cb45b6372"
   },
   "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>date</th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>functional_classification</th>\n",
       "      <th>functional_classification_name</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>...</th>\n",
       "      <th>East</th>\n",
       "      <th>North</th>\n",
       "      <th>Northeast</th>\n",
       "      <th>Northeast-Southwest</th>\n",
       "      <th>Northwest</th>\n",
       "      <th>South</th>\n",
       "      <th>Southeast</th>\n",
       "      <th>Southeast-Northwest</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>56</td>\n",
       "      <td>3R</td>\n",
       "      <td>Rural: Principal Arterial - Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>21</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>55</td>\n",
       "      <td>1U</td>\n",
       "      <td>Urban: Principal Arterial - Interstate</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>4</td>\n",
       "      <td>4R</td>\n",
       "      <td>Rural: Minor Arterial</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-09-10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>East</td>\n",
       "      <td>19</td>\n",
       "      <td>3U</td>\n",
       "      <td>Urban: Principal Arterial - Other</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  day_of_data  day_of_week  direction_of_travel  \\\n",
       "0 2015-04-07            7            3                    1   \n",
       "1 2015-09-26           26            7                    7   \n",
       "3 2015-04-26           26            1                    5   \n",
       "4 2015-05-23           23            7                    3   \n",
       "6 2015-09-10           10            5                    3   \n",
       "\n",
       "  direction_of_travel_name  fips_state_code functional_classification  \\\n",
       "0                    North               56                        3R   \n",
       "1                     West               21                        1U   \n",
       "3                    South               55                        1U   \n",
       "4                     East                4                        4R   \n",
       "6                     East               19                        3U   \n",
       "\n",
       "           functional_classification_name  lane_of_travel  month_of_data  ...  \\\n",
       "0       Rural: Principal Arterial - Other               1              4  ...   \n",
       "1  Urban: Principal Arterial - Interstate               2              9  ...   \n",
       "3  Urban: Principal Arterial - Interstate               1              4  ...   \n",
       "4                   Rural: Minor Arterial               0              5  ...   \n",
       "6       Urban: Principal Arterial - Other               2              9  ...   \n",
       "\n",
       "   East  North  Northeast  Northeast-Southwest  Northwest  South  Southeast  \\\n",
       "0     0      1          0                    0          0      0          0   \n",
       "1     0      0          0                    0          0      0          0   \n",
       "3     0      0          0                    0          0      1          0   \n",
       "4     1      0          0                    0          0      0          0   \n",
       "6     1      0          0                    0          0      0          0   \n",
       "\n",
       "   Southeast-Northwest  Southwest  West  \n",
       "0                    0          0     0  \n",
       "1                    0          0     1  \n",
       "3                    0          0     0  \n",
       "4                    0          0     0  \n",
       "6                    0          0     0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-20T23:10:00.502391Z",
     "iopub.status.busy": "2021-09-20T23:10:00.502140Z",
     "iopub.status.idle": "2021-09-20T23:10:06.374209Z",
     "shell.execute_reply": "2021-09-20T23:10:06.373392Z",
     "shell.execute_reply.started": "2021-09-20T23:10:00.502354Z"
    },
    "id": "QRKdpzt-JVWu",
    "outputId": "3f637c82-641b-46e3-f6ce-196a5a6e7a30",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>fips_state_code</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>station_id</th>\n",
       "      <th>1_to_4</th>\n",
       "      <th>5_to_8</th>\n",
       "      <th>9_to_12</th>\n",
       "      <th>...</th>\n",
       "      <th>East</th>\n",
       "      <th>North</th>\n",
       "      <th>Northeast</th>\n",
       "      <th>Northeast-Southwest</th>\n",
       "      <th>Northwest</th>\n",
       "      <th>South</th>\n",
       "      <th>Southeast</th>\n",
       "      <th>Southeast-Northwest</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "      <td>6.901954e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.572624e+01</td>\n",
       "      <td>4.012449e+00</td>\n",
       "      <td>3.885063e+00</td>\n",
       "      <td>2.985262e+01</td>\n",
       "      <td>1.335427e+00</td>\n",
       "      <td>6.518998e+00</td>\n",
       "      <td>3.242214e+03</td>\n",
       "      <td>6.073193e+01</td>\n",
       "      <td>2.552298e+02</td>\n",
       "      <td>4.552350e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.048695e-01</td>\n",
       "      <td>2.817621e-01</td>\n",
       "      <td>3.734305e-03</td>\n",
       "      <td>7.690286e-03</td>\n",
       "      <td>5.575957e-03</td>\n",
       "      <td>2.799525e-01</td>\n",
       "      <td>5.827190e-03</td>\n",
       "      <td>3.727640e-03</td>\n",
       "      <td>3.690984e-03</td>\n",
       "      <td>2.031696e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.770013e+00</td>\n",
       "      <td>2.000232e+00</td>\n",
       "      <td>2.281844e+00</td>\n",
       "      <td>1.657622e+01</td>\n",
       "      <td>1.071629e+00</td>\n",
       "      <td>3.456660e+00</td>\n",
       "      <td>2.796729e+03</td>\n",
       "      <td>8.323963e+01</td>\n",
       "      <td>3.154451e+02</td>\n",
       "      <td>4.602179e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.036063e-01</td>\n",
       "      <td>4.498580e-01</td>\n",
       "      <td>6.099476e-02</td>\n",
       "      <td>8.735643e-02</td>\n",
       "      <td>7.446386e-02</td>\n",
       "      <td>4.489756e-01</td>\n",
       "      <td>7.611330e-02</td>\n",
       "      <td>6.094051e-02</td>\n",
       "      <td>6.064125e-02</td>\n",
       "      <td>4.023577e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.300000e+03</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>1.282500e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.367000e+03</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>1.355000e+02</td>\n",
       "      <td>3.105000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.355000e+03</td>\n",
       "      <td>7.975000e+01</td>\n",
       "      <td>3.357500e+02</td>\n",
       "      <td>6.277500e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.497900e+04</td>\n",
       "      <td>7.455000e+02</td>\n",
       "      <td>2.299750e+03</td>\n",
       "      <td>3.270500e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        day_of_data   day_of_week  direction_of_travel  fips_state_code  \\\n",
       "count  6.901954e+06  6.901954e+06         6.901954e+06     6.901954e+06   \n",
       "mean   1.572624e+01  4.012449e+00         3.885063e+00     2.985262e+01   \n",
       "std    8.770013e+00  2.000232e+00         2.281844e+00     1.657622e+01   \n",
       "min    1.000000e+00  1.000000e+00         0.000000e+00     1.000000e+00   \n",
       "25%    8.000000e+00  2.000000e+00         1.000000e+00     1.300000e+01   \n",
       "50%    1.600000e+01  4.000000e+00         5.000000e+00     3.000000e+01   \n",
       "75%    2.300000e+01  6.000000e+00         5.000000e+00     4.400000e+01   \n",
       "max    3.100000e+01  7.000000e+00         9.000000e+00     5.600000e+01   \n",
       "\n",
       "       lane_of_travel  month_of_data    station_id        1_to_4  \\\n",
       "count    6.901954e+06   6.901954e+06  6.901954e+06  6.901954e+06   \n",
       "mean     1.335427e+00   6.518998e+00  3.242214e+03  6.073193e+01   \n",
       "std      1.071629e+00   3.456660e+00  2.796729e+03  8.323963e+01   \n",
       "min      0.000000e+00   1.000000e+00  1.000000e+00 -1.000000e+00   \n",
       "25%      1.000000e+00   4.000000e+00  1.300000e+03  9.000000e+00   \n",
       "50%      1.000000e+00   7.000000e+00  2.367000e+03  2.700000e+01   \n",
       "75%      2.000000e+00   1.000000e+01  4.355000e+03  7.975000e+01   \n",
       "max      9.000000e+00   1.200000e+01  1.497900e+04  7.455000e+02   \n",
       "\n",
       "             5_to_8       9_to_12  ...          East         North  \\\n",
       "count  6.901954e+06  6.901954e+06  ...  6.901954e+06  6.901954e+06   \n",
       "mean   2.552298e+02  4.552350e+02  ...  2.048695e-01  2.817621e-01   \n",
       "std    3.154451e+02  4.602179e+02  ...  4.036063e-01  4.498580e-01   \n",
       "min   -1.000000e+00 -1.000000e+00  ...  0.000000e+00  0.000000e+00   \n",
       "25%    4.700000e+01  1.282500e+02  ...  0.000000e+00  0.000000e+00   \n",
       "50%    1.355000e+02  3.105000e+02  ...  0.000000e+00  0.000000e+00   \n",
       "75%    3.357500e+02  6.277500e+02  ...  0.000000e+00  1.000000e+00   \n",
       "max    2.299750e+03  3.270500e+03  ...  1.000000e+00  1.000000e+00   \n",
       "\n",
       "          Northeast  Northeast-Southwest     Northwest         South  \\\n",
       "count  6.901954e+06         6.901954e+06  6.901954e+06  6.901954e+06   \n",
       "mean   3.734305e-03         7.690286e-03  5.575957e-03  2.799525e-01   \n",
       "std    6.099476e-02         8.735643e-02  7.446386e-02  4.489756e-01   \n",
       "min    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    0.000000e+00         0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "max    1.000000e+00         1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "\n",
       "          Southeast  Southeast-Northwest     Southwest          West  \n",
       "count  6.901954e+06         6.901954e+06  6.901954e+06  6.901954e+06  \n",
       "mean   5.827190e-03         3.727640e-03  3.690984e-03  2.031696e-01  \n",
       "std    7.611330e-02         6.094051e-02  6.064125e-02  4.023577e-01  \n",
       "min    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "25%    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "50%    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "75%    0.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "max    1.000000e+00         1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **EDA - STATION DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>algorithm_of_vehicle_classification</th>\n",
       "      <th>algorithm_of_vehicle_classification_name</th>\n",
       "      <th>calibration_of_weighing_system</th>\n",
       "      <th>calibration_of_weighing_system_name</th>\n",
       "      <th>classification_system_for_vehicle_classification</th>\n",
       "      <th>concurrent_route_signing</th>\n",
       "      <th>concurrent_signed_route_number</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_county_code</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_type_for_vehicle_classification_name</th>\n",
       "      <th>second_type_of_sensor</th>\n",
       "      <th>shrp_site_identification</th>\n",
       "      <th>station_id</th>\n",
       "      <th>station_location</th>\n",
       "      <th>type_of_sensor</th>\n",
       "      <th>type_of_sensor_name</th>\n",
       "      <th>year_of_data</th>\n",
       "      <th>year_station_discontinued</th>\n",
       "      <th>year_station_established</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>091</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129130</td>\n",
       "      <td>LAKEVIEW AVENUE ORA91R10.091</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>099</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100190</td>\n",
       "      <td>LITTLE JOHN CREEK SJ9912.526</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>005</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>Station used for Heavy Vehicle Travel Informat...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>022940</td>\n",
       "      <td>EDGEWOOD SIS5R22.999</td>\n",
       "      <td>P</td>\n",
       "      <td>Piezoelectric</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Vehicle length classification</td>\n",
       "      <td>M</td>\n",
       "      <td>Moving average of the steering axle of 3S2s</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>000302</td>\n",
       "      <td>I 15  12900 South  M.P.  290.6</td>\n",
       "      <td>X</td>\n",
       "      <td>Radio wave</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>000000</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>Station not used for Heavy Vehicle Travel Info...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W01136</td>\n",
       "      <td>E. of Franklin Rd Underpass</td>\n",
       "      <td>L</td>\n",
       "      <td>Inductance loop</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  algorithm_of_vehicle_classification  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                   G   \n",
       "3                                   D   \n",
       "4                                   G   \n",
       "\n",
       "  algorithm_of_vehicle_classification_name calibration_of_weighing_system  \\\n",
       "0                                      NaN                            NaN   \n",
       "1                                      NaN                            NaN   \n",
       "2      Axle spacing with Scheme F modified                            NaN   \n",
       "3            Vehicle length classification                              M   \n",
       "4      Axle spacing with Scheme F modified                              0   \n",
       "\n",
       "           calibration_of_weighing_system_name  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3  Moving average of the steering axle of 3S2s   \n",
       "4                                          NaN   \n",
       "\n",
       "   classification_system_for_vehicle_classification  concurrent_route_signing  \\\n",
       "0                                                13                         3   \n",
       "1                                                13                         3   \n",
       "2                                                15                         1   \n",
       "3                                                13                         0   \n",
       "4                                                14                         1   \n",
       "\n",
       "  concurrent_signed_route_number  direction_of_travel  \\\n",
       "0                            091                    7   \n",
       "1                            099                    5   \n",
       "2                            005                    1   \n",
       "3                            NaN                    5   \n",
       "4                         000000                    7   \n",
       "\n",
       "  direction_of_travel_name  fips_county_code  ...  \\\n",
       "0                     West                59  ...   \n",
       "1                    South                77  ...   \n",
       "2                    North                93  ...   \n",
       "3                    South                35  ...   \n",
       "4                     West                27  ...   \n",
       "\n",
       "         sample_type_for_vehicle_classification_name second_type_of_sensor  \\\n",
       "0  Station not used for Heavy Vehicle Travel Info...                     N   \n",
       "1  Station not used for Heavy Vehicle Travel Info...                     N   \n",
       "2  Station used for Heavy Vehicle Travel Informat...                     N   \n",
       "3  Station not used for Heavy Vehicle Travel Info...                   NaN   \n",
       "4  Station not used for Heavy Vehicle Travel Info...                   NaN   \n",
       "\n",
       "  shrp_site_identification station_id                station_location  \\\n",
       "0                      NaN     129130    LAKEVIEW AVENUE ORA91R10.091   \n",
       "1                      NaN     100190    LITTLE JOHN CREEK SJ9912.526   \n",
       "2                      NaN     022940            EDGEWOOD SIS5R22.999   \n",
       "3                      NaN     000302  I 15  12900 South  M.P.  290.6   \n",
       "4                      NaN     W01136     E. of Franklin Rd Underpass   \n",
       "\n",
       "   type_of_sensor type_of_sensor_name  year_of_data  \\\n",
       "0               L     Inductance loop            15   \n",
       "1               L     Inductance loop            15   \n",
       "2               P       Piezoelectric            15   \n",
       "3               X          Radio wave            15   \n",
       "4               L     Inductance loop            15   \n",
       "\n",
       "   year_station_discontinued year_station_established  \n",
       "0                          0                       97  \n",
       "1                          0                       97  \n",
       "2                          0                       69  \n",
       "3                          0                       11  \n",
       "4                          0                       95  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_station_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CALIBRATION OF WEIGHING SYSTEM:\n",
      "{'T': 1744, 'C': 1683, '0': 1466, 'M': 1347, 'Z': 799, 'U': 546, 'A': 439, 'D': 108, 'P': 10, 'B': 9, '2': 8, 'S': 6}\n",
      "\n",
      "CALIBRATION OF WEIGHING SYSTEM NAME:\n",
      "{'Test trucks only': 1744, 'Combination of test trucks and trucks from the traffic stream (but not ASTM E1318)': 1683, 'Moving average of the steering axle of 3S2s': 1347, 'Other method': 799, 'Uncalibrated': 546, 'ASTM Standard E1318': 439, 'Other sample of trucks from the traffic stream': 108, 'Subset of ASTM Standard E1318': 9, 'Static calibration': 6}\n",
      "\n",
      "ALGORITHM_OF_VEHICLE_CLASSIFICATION:\n",
      "{'F': 7100, 'G': 5155, 'D': 2267, '0': 1231, 'L': 1082, 'K': 548, 'Z': 465, 'M': 420, 'N': 116, 'H': 98, 'C': 42, 'A': 36, '1': 10, 'E': 6}\n",
      "\n",
      "ALGORITHM_OF_VEHICLE_CLASSIFICATION_NAME:\n",
      "{'Axle spacing with Scheme F': 7100, 'Axle spacing with Scheme F modified': 5155, 'Vehicle length classification': 2267, 'Axle spacing and vehicle length algorithm': 1082, 'Axle spacing and weight algorithm': 548, 'Other means not specified above': 465, 'Axle spacing weight and vehicle length algorithm': 420, 'Axle spacing and other input(s) not specified above': 116, 'Other axle spacing algorithm': 98, 'Automated interpretation of vehicle image or signature (e.g. video microwave sonic)': 42, 'Human observation on site (manual)': 36, 'Axle spacing with ASTM Standard E1572': 6}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('calibration of weighing system:'.upper())\n",
    "print(dict(traffic_station_df['calibration_of_weighing_system'].value_counts()), end = '\\n\\n')\n",
    "print('calibration of weighing system name:'.upper())\n",
    "print(dict(traffic_station_df['calibration_of_weighing_system_name'].value_counts()), end = '\\n\\n')\n",
    "\n",
    "print('algorithm_of_vehicle_classification:'.upper())\n",
    "print(dict(traffic_station_df['algorithm_of_vehicle_classification'].value_counts()), end = '\\n\\n')\n",
    "print('algorithm_of_vehicle_classification_name:'.upper())\n",
    "print(dict(traffic_station_df['algorithm_of_vehicle_classification_name'].value_counts()), end = '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the below code cell we are performing **One Hot Encoding** to convert many useless columns into features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>algorithm_of_vehicle_classification</th>\n",
       "      <th>algorithm_of_vehicle_classification_name</th>\n",
       "      <th>calibration_of_weighing_system</th>\n",
       "      <th>calibration_of_weighing_system_name</th>\n",
       "      <th>classification_system_for_vehicle_classification</th>\n",
       "      <th>concurrent_route_signing</th>\n",
       "      <th>concurrent_signed_route_number</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>direction_of_travel_name</th>\n",
       "      <th>fips_county_code</th>\n",
       "      <th>...</th>\n",
       "      <th>4R</th>\n",
       "      <th>4U</th>\n",
       "      <th>5R</th>\n",
       "      <th>other_functional_classifications</th>\n",
       "      <th>Data_with_lanes_combined</th>\n",
       "      <th>Other_lanes</th>\n",
       "      <th>Outside_rightmost_lane</th>\n",
       "      <th>TVC_Automatic</th>\n",
       "      <th>TVC_Manual</th>\n",
       "      <th>TVC_Portable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>091</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>59</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>099</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>77</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>005</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>93</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Vehicle length classification</td>\n",
       "      <td>M</td>\n",
       "      <td>Moving average of the steering axle of 3S2s</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>South</td>\n",
       "      <td>35</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>Axle spacing with Scheme F modified</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>000000</td>\n",
       "      <td>7</td>\n",
       "      <td>West</td>\n",
       "      <td>27</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  algorithm_of_vehicle_classification  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                   G   \n",
       "3                                   D   \n",
       "4                                   G   \n",
       "\n",
       "  algorithm_of_vehicle_classification_name calibration_of_weighing_system  \\\n",
       "0                                      NaN                            NaN   \n",
       "1                                      NaN                            NaN   \n",
       "2      Axle spacing with Scheme F modified                            NaN   \n",
       "3            Vehicle length classification                              M   \n",
       "4      Axle spacing with Scheme F modified                              0   \n",
       "\n",
       "           calibration_of_weighing_system_name  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3  Moving average of the steering axle of 3S2s   \n",
       "4                                          NaN   \n",
       "\n",
       "   classification_system_for_vehicle_classification  concurrent_route_signing  \\\n",
       "0                                                13                         3   \n",
       "1                                                13                         3   \n",
       "2                                                15                         1   \n",
       "3                                                13                         0   \n",
       "4                                                14                         1   \n",
       "\n",
       "  concurrent_signed_route_number  direction_of_travel  \\\n",
       "0                            091                    7   \n",
       "1                            099                    5   \n",
       "2                            005                    1   \n",
       "3                            NaN                    5   \n",
       "4                         000000                    7   \n",
       "\n",
       "  direction_of_travel_name  fips_county_code  ...  4R 4U 5R  \\\n",
       "0                     West                59  ...   0  0  0   \n",
       "1                    South                77  ...   0  0  0   \n",
       "2                    North                93  ...   0  0  0   \n",
       "3                    South                35  ...   0  0  0   \n",
       "4                     West                27  ...   0  0  0   \n",
       "\n",
       "  other_functional_classifications Data_with_lanes_combined  Other_lanes  \\\n",
       "0                                0                        0            1   \n",
       "1                                0                        0            0   \n",
       "2                                0                        0            1   \n",
       "3                                0                        0            0   \n",
       "4                                0                        0            1   \n",
       "\n",
       "  Outside_rightmost_lane  TVC_Automatic  TVC_Manual TVC_Portable  \n",
       "0                      0              1           0            0  \n",
       "1                      1              1           0            0  \n",
       "2                      0              1           0            0  \n",
       "3                      1              1           0            0  \n",
       "4                      0              0           0            1  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is clear that 'D', 'P', 'B', '2', 'S' calibration_of_weighing_system are very much less in number and therefore we can merge them all under one field.\n",
    "# In the calibration_of_weighing_system column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "traffic_station_df['calibration_of_weighing_system'] = traffic_station_df['calibration_of_weighing_system'].replace(['D', 'P', 'B', '2', 'S'], 'other_calibration_of_weighing_system')\n",
    "\n",
    "calibration_of_weighing_system_dummy = pd.get_dummies(traffic_station_df['calibration_of_weighing_system'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, calibration_of_weighing_system_dummy], axis=1)\n",
    "\n",
    "\n",
    "# It is clear that 'Z', 'M', 'N', 'H', 'C', 'A', '1', 'E' algorithm_of_vehicle_classification are very much less in number and therefore we can merge them all under one field.\n",
    "# In the algorithm_of_vehicle_classification column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "traffic_station_df['algorithm_of_vehicle_classification'] = traffic_station_df['algorithm_of_vehicle_classification'].replace(['Z', 'M', 'N', 'H', 'C', 'A', '1', 'E'], 'other_algorithm_of_vehicle_classification')\n",
    "traffic_station_df['algorithm_of_vehicle_classification'] = traffic_station_df['algorithm_of_vehicle_classification'].replace(['0'], 'A_0')\n",
    "\n",
    "algorithm_of_vehicle_classification_dummy = pd.get_dummies(traffic_station_df['algorithm_of_vehicle_classification'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, algorithm_of_vehicle_classification_dummy], axis=1)\n",
    "\n",
    "# Renaming the very long column name.\n",
    "\n",
    "traffic_station_df[\"direction_of_travel_name\"].replace({\"North-South or Northeast-Southwest combined (ATR stations only)\": \"Northeast-Southwest\", \n",
    "                                                \"East-West or Southeast-Northwest combined (ATR stations only)\": \"Southeast-Northwest\"}, inplace=True)\n",
    "\n",
    "# In the direction_of_travel_name column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "direction_of_travel_name_dummy1 = pd.get_dummies(traffic_station_df['direction_of_travel_name'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, direction_of_travel_name_dummy1], axis=1)\n",
    "\n",
    "\n",
    "# It is clear that '5U', '6R', '7U', '7R' functional_classification are very much less in number and therefore we can merge them all under one field.\n",
    "# In the functional_classification column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "traffic_station_df['functional_classification'] = traffic_station_df['functional_classification'].replace(['5U', '6R', '7U', '7R'], 'other_functional_classifications')\n",
    "traffic_station_df['functional_classification_name'] = traffic_station_df['functional_classification_name'].replace(['Urban: Collector', \n",
    "                                                                                                     'Rural: Minor Collector', \n",
    "                                                                                                     'Urban: Local System', \n",
    "                                                                                                     'Rural: Local System'], 'other_functional_classification_name')\n",
    "\n",
    "functional_classification_dummy1 = pd.get_dummies(traffic_station_df['functional_classification'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, functional_classification_dummy1], axis=1)\n",
    "\n",
    "\n",
    "# In the lane_of_travel_name column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "traffic_station_df['lane_of_travel_name'] = traffic_station_df['lane_of_travel_name'].replace(['Outside (rightmost) lane'], 'Outside_rightmost_lane')\n",
    "traffic_station_df['lane_of_travel_name'] = traffic_station_df['lane_of_travel_name'].replace(['Data with lanes combined'], 'Data_with_lanes_combined')\n",
    "traffic_station_df['lane_of_travel_name'] = traffic_station_df['lane_of_travel_name'].replace(['Other lanes'], 'Other_lanes')\n",
    "\n",
    "lane_of_travel_name_dummy = pd.get_dummies(traffic_station_df['lane_of_travel_name'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, lane_of_travel_name_dummy], axis=1)\n",
    "\n",
    "\n",
    "# In the method_of_traffic_volume_counting_name column we can see they are categorical ones, so we make new feature out of them.\n",
    "\n",
    "\n",
    "traffic_station_df.replace({'method_of_traffic_volume_counting_name' : { 'Permanent automatic traffic recorder (ATR)' : 'TVC_Automatic', \n",
    "                                                               'Portable traffic recording device' : 'TVC_Portable',\n",
    "                                                                'Human observation (manual)': 'TVC_Manual'}}, inplace=True)\n",
    "\n",
    "method_of_traffic_volume_counting_name_dummy = pd.get_dummies(traffic_station_df['method_of_traffic_volume_counting_name'])\n",
    "\n",
    "traffic_station_df = pd.concat([traffic_station_df, method_of_traffic_volume_counting_name_dummy], axis=1)\n",
    "\n",
    "traffic_station_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the below code cell we are converting the columns which contains only 2 values into vinary values on 1 and 0 so that they can be machine readable and can be used as a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_station_df.replace({'sample_type_for_traffic_volume_name' : {'Station used for Traffic Volume Trends': 1, \n",
    "                                                                     'Station not used for Traffic Volume Trends': 0}}, inplace=True)\n",
    "\n",
    "traffic_station_df.replace({'sample_type_for_vehicle_classification_name' : {'Station used for Heavy Vehicle Travel Information System': 1, \n",
    "                                                                             'Station not used for Heavy Vehicle Travel Information System': 0}}, inplace=True)\n",
    "\n",
    "traffic_station_df.replace({'method_of_data_retrieval_name' : { 'Automated (telemetry)' : 1, 'Not automated (manual)' : 0}}, inplace=True)\n",
    "\n",
    "traffic_station_df.replace({'hpms_sample_type' : { 'N' : 1, 'Y' : 0}}, inplace=True)\n",
    "\n",
    "traffic_station_df.replace({'national_highway_system' : { 'N' : 0, 'Y' : 1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are converting all the columns which contains many unique values into the counts of each value so that they all can be used as the feature and contribute into our model accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_station_established_count = dict(traffic_station_df['year_station_established'].value_counts())\n",
    "traffic_station_df['year_station_established'] = traffic_station_df['year_station_established'].map(year_station_established_count.get)\n",
    "\n",
    "year_station_discontinued_count = dict(traffic_station_df['year_station_discontinued'].value_counts())\n",
    "traffic_station_df['year_station_discontinued'] = traffic_station_df['year_station_discontinued'].map(year_station_discontinued_count.get)\n",
    "\n",
    "station_id_element_count1 = dict(traffic_station_df['station_id'].value_counts())\n",
    "traffic_station_df['station_id'] = traffic_station_df['station_id'].map(station_id_element_count1.get)\n",
    "\n",
    "previous_station_id_count = dict(traffic_station_df['previous_station_id'].value_counts())\n",
    "traffic_station_df['previous_station_id'] = traffic_station_df['previous_station_id'].map(previous_station_id_count.get)\n",
    "\n",
    "posted_signed_route_number_count = dict(traffic_station_df['posted_signed_route_number'].value_counts())\n",
    "traffic_station_df['posted_signed_route_number'] = traffic_station_df['posted_signed_route_number'].map(posted_signed_route_number_count.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are dropping all the useless column and the columns we have already converted into the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_station_df.drop(['calibration_of_weighing_system', 'calibration_of_weighing_system_name', \n",
    "                         'algorithm_of_vehicle_classification', 'algorithm_of_vehicle_classification_name',\n",
    "                        'direction_of_travel_name', 'functional_classification','functional_classification_name', 'lane_of_travel_name',\n",
    "                        'method_of_traffic_volume_counting_name', 'year_of_data', 'station_location', 'method_of_truck_weighing_name',\n",
    "                        'record_type', 'sample_type_for_traffic_volume', 'sample_type_for_truck_weight_name',\n",
    "                        'sample_type_for_truck_weight', 'sample_type_for_vehicle_classification', 'type_of_sensor_name',\n",
    "                        'type_of_sensor', 'second_type_of_sensor', 'primary_purpose', 'primary_purpose_name', \n",
    "                        'method_of_vehicle_classification_name'], axis = 1, inplace=True)\n",
    "traffic_station_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Training and Testing of Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the unnecesary columns and the columns with non numerical values.\n",
    "traffic_df_m = traffic_df.drop(['date', 'direction_of_travel_name', 'functional_classification_name', 'functional_classification', 'fips_state_code'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_df_m = traffic_df_m.drop(['station_id'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_df_m = traffic_df_m.drop(['restrictions'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>lane_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>record_type</th>\n",
       "      <th>traffic_volume_counted_after_0000_to_0100</th>\n",
       "      <th>traffic_volume_counted_after_0100_to_0200</th>\n",
       "      <th>traffic_volume_counted_after_0200_to_0300</th>\n",
       "      <th>traffic_volume_counted_after_0300_to_0400</th>\n",
       "      <th>...</th>\n",
       "      <th>traffic_volume_counted_after_1500_to_1600</th>\n",
       "      <th>traffic_volume_counted_after_1600_to_1700</th>\n",
       "      <th>traffic_volume_counted_after_1700_to_1800</th>\n",
       "      <th>traffic_volume_counted_after_1800_to_1900</th>\n",
       "      <th>traffic_volume_counted_after_1900_to_2000</th>\n",
       "      <th>traffic_volume_counted_after_2000_to_2100</th>\n",
       "      <th>traffic_volume_counted_after_2100_to_2200</th>\n",
       "      <th>traffic_volume_counted_after_2200_to_2300</th>\n",
       "      <th>traffic_volume_counted_after_2300_to_2400</th>\n",
       "      <th>year_of_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>122</td>\n",
       "      <td>124</td>\n",
       "      <td>110</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>381</td>\n",
       "      <td>252</td>\n",
       "      <td>218</td>\n",
       "      <td>194</td>\n",
       "      <td>...</td>\n",
       "      <td>1617</td>\n",
       "      <td>1669</td>\n",
       "      <td>1308</td>\n",
       "      <td>1068</td>\n",
       "      <td>928</td>\n",
       "      <td>885</td>\n",
       "      <td>798</td>\n",
       "      <td>650</td>\n",
       "      <td>613</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>585</td>\n",
       "      <td>408</td>\n",
       "      <td>328</td>\n",
       "      <td>364</td>\n",
       "      <td>...</td>\n",
       "      <td>4244</td>\n",
       "      <td>4405</td>\n",
       "      <td>4609</td>\n",
       "      <td>4361</td>\n",
       "      <td>3272</td>\n",
       "      <td>2243</td>\n",
       "      <td>2050</td>\n",
       "      <td>1453</td>\n",
       "      <td>892</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>105</td>\n",
       "      <td>73</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>1011</td>\n",
       "      <td>959</td>\n",
       "      <td>851</td>\n",
       "      <td>708</td>\n",
       "      <td>559</td>\n",
       "      <td>457</td>\n",
       "      <td>297</td>\n",
       "      <td>207</td>\n",
       "      <td>110</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>61</td>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_of_data  day_of_week  direction_of_travel  lane_of_travel  \\\n",
       "0            7            3                    1               1   \n",
       "1           26            7                    7               2   \n",
       "2           16            3                    3               0   \n",
       "3           26            1                    5               1   \n",
       "4           23            7                    3               0   \n",
       "\n",
       "   month_of_data  record_type  traffic_volume_counted_after_0000_to_0100  \\\n",
       "0              4            3                                          4   \n",
       "1              9            3                                        381   \n",
       "2              6            3                                        585   \n",
       "3              4            3                                        105   \n",
       "4              5            3                                          6   \n",
       "\n",
       "   traffic_volume_counted_after_0100_to_0200  \\\n",
       "0                                          3   \n",
       "1                                        252   \n",
       "2                                        408   \n",
       "3                                         73   \n",
       "4                                          4   \n",
       "\n",
       "   traffic_volume_counted_after_0200_to_0300  \\\n",
       "0                                          2   \n",
       "1                                        218   \n",
       "2                                        328   \n",
       "3                                         68   \n",
       "4                                          2   \n",
       "\n",
       "   traffic_volume_counted_after_0300_to_0400  ...  \\\n",
       "0                                          4  ...   \n",
       "1                                        194  ...   \n",
       "2                                        364  ...   \n",
       "3                                         66  ...   \n",
       "4                                          3  ...   \n",
       "\n",
       "   traffic_volume_counted_after_1500_to_1600  \\\n",
       "0                                         89   \n",
       "1                                       1617   \n",
       "2                                       4244   \n",
       "3                                       1011   \n",
       "4                                         83   \n",
       "\n",
       "   traffic_volume_counted_after_1600_to_1700  \\\n",
       "0                                        122   \n",
       "1                                       1669   \n",
       "2                                       4405   \n",
       "3                                        959   \n",
       "4                                         61   \n",
       "\n",
       "   traffic_volume_counted_after_1700_to_1800  \\\n",
       "0                                        124   \n",
       "1                                       1308   \n",
       "2                                       4609   \n",
       "3                                        851   \n",
       "4                                         55   \n",
       "\n",
       "   traffic_volume_counted_after_1800_to_1900  \\\n",
       "0                                        110   \n",
       "1                                       1068   \n",
       "2                                       4361   \n",
       "3                                        708   \n",
       "4                                         35   \n",
       "\n",
       "   traffic_volume_counted_after_1900_to_2000  \\\n",
       "0                                         69   \n",
       "1                                        928   \n",
       "2                                       3272   \n",
       "3                                        559   \n",
       "4                                         29   \n",
       "\n",
       "   traffic_volume_counted_after_2000_to_2100  \\\n",
       "0                                         73   \n",
       "1                                        885   \n",
       "2                                       2243   \n",
       "3                                        457   \n",
       "4                                         21   \n",
       "\n",
       "   traffic_volume_counted_after_2100_to_2200  \\\n",
       "0                                         28   \n",
       "1                                        798   \n",
       "2                                       2050   \n",
       "3                                        297   \n",
       "4                                         23   \n",
       "\n",
       "   traffic_volume_counted_after_2200_to_2300  \\\n",
       "0                                         12   \n",
       "1                                        650   \n",
       "2                                       1453   \n",
       "3                                        207   \n",
       "4                                          9   \n",
       "\n",
       "   traffic_volume_counted_after_2300_to_2400  year_of_data  \n",
       "0                                          6            15  \n",
       "1                                        613            15  \n",
       "2                                        892            15  \n",
       "3                                        110            15  \n",
       "4                                          7            15  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df_m.reset_index(inplace=True, drop=True)\n",
    "traffic_df_m.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting our data into training and testing data.\n",
    "Our target value of the model will be the lane of travel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## traffic_df_mm = pd.get_dummies(traffic_df_m['station_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up our X and Y axis i.e. input(X axis) and output(Y axis) i.e. target value.\n",
    "# Splitting out training data into new Train data and Validation data.\n",
    "\n",
    "y = traffic_df_m['lane_of_travel']\n",
    "X_train = traffic_df_m.drop(['lane_of_travel'], axis = 1)\n",
    "Xtrain ,Xtest, ytrain, ytest = train_test_split(X_train, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Lists to Store in the Results and Names of Algorithms\n",
    "MSE_Score = []\n",
    "R2_Score = []\n",
    "Algorithm = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xtrain.drop(['station_id'], axis=)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xtrain[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>day_of_data</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>direction_of_travel</th>\n",
       "      <th>month_of_data</th>\n",
       "      <th>record_type</th>\n",
       "      <th>restrictions</th>\n",
       "      <th>traffic_volume_counted_after_0000_to_0100</th>\n",
       "      <th>traffic_volume_counted_after_0100_to_0200</th>\n",
       "      <th>traffic_volume_counted_after_0200_to_0300</th>\n",
       "      <th>traffic_volume_counted_after_0300_to_0400</th>\n",
       "      <th>...</th>\n",
       "      <th>traffic_volume_counted_after_1500_to_1600</th>\n",
       "      <th>traffic_volume_counted_after_1600_to_1700</th>\n",
       "      <th>traffic_volume_counted_after_1700_to_1800</th>\n",
       "      <th>traffic_volume_counted_after_1800_to_1900</th>\n",
       "      <th>traffic_volume_counted_after_1900_to_2000</th>\n",
       "      <th>traffic_volume_counted_after_2000_to_2100</th>\n",
       "      <th>traffic_volume_counted_after_2100_to_2200</th>\n",
       "      <th>traffic_volume_counted_after_2200_to_2300</th>\n",
       "      <th>traffic_volume_counted_after_2300_to_2400</th>\n",
       "      <th>year_of_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7052181</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>438</td>\n",
       "      <td>317</td>\n",
       "      <td>210</td>\n",
       "      <td>202</td>\n",
       "      <td>...</td>\n",
       "      <td>1790</td>\n",
       "      <td>1858</td>\n",
       "      <td>1724</td>\n",
       "      <td>1611</td>\n",
       "      <td>1416</td>\n",
       "      <td>1192</td>\n",
       "      <td>1147</td>\n",
       "      <td>960</td>\n",
       "      <td>826</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046231</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>738</td>\n",
       "      <td>702</td>\n",
       "      <td>659</td>\n",
       "      <td>601</td>\n",
       "      <td>548</td>\n",
       "      <td>440</td>\n",
       "      <td>356</td>\n",
       "      <td>324</td>\n",
       "      <td>233</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029291</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294</td>\n",
       "      <td>258</td>\n",
       "      <td>205</td>\n",
       "      <td>233</td>\n",
       "      <td>...</td>\n",
       "      <td>1112</td>\n",
       "      <td>1164</td>\n",
       "      <td>1189</td>\n",
       "      <td>1018</td>\n",
       "      <td>983</td>\n",
       "      <td>807</td>\n",
       "      <td>693</td>\n",
       "      <td>556</td>\n",
       "      <td>429</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964924</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>171</td>\n",
       "      <td>173</td>\n",
       "      <td>190</td>\n",
       "      <td>171</td>\n",
       "      <td>148</td>\n",
       "      <td>91</td>\n",
       "      <td>81</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023772</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>359</td>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "      <td>224</td>\n",
       "      <td>166</td>\n",
       "      <td>140</td>\n",
       "      <td>93</td>\n",
       "      <td>57</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         day_of_data  day_of_week  direction_of_travel  month_of_data  \\\n",
       "7052181           13            6                    5              2   \n",
       "1046231            7            7                    1              3   \n",
       "5029291            9            5                    1              7   \n",
       "4964924            8            7                    3              8   \n",
       "3023772            3            5                    7             12   \n",
       "\n",
       "         record_type  restrictions  traffic_volume_counted_after_0000_to_0100  \\\n",
       "7052181            3           NaN                                        438   \n",
       "1046231            3           NaN                                        172   \n",
       "5029291            3           NaN                                        294   \n",
       "4964924            3           NaN                                         43   \n",
       "3023772            3           NaN                                         28   \n",
       "\n",
       "         traffic_volume_counted_after_0100_to_0200  \\\n",
       "7052181                                        317   \n",
       "1046231                                         76   \n",
       "5029291                                        258   \n",
       "4964924                                         26   \n",
       "3023772                                         27   \n",
       "\n",
       "         traffic_volume_counted_after_0200_to_0300  \\\n",
       "7052181                                        210   \n",
       "1046231                                         85   \n",
       "5029291                                        205   \n",
       "4964924                                         24   \n",
       "3023772                                         23   \n",
       "\n",
       "         traffic_volume_counted_after_0300_to_0400  ...  \\\n",
       "7052181                                        202  ...   \n",
       "1046231                                         64  ...   \n",
       "5029291                                        233  ...   \n",
       "4964924                                         10  ...   \n",
       "3023772                                         26  ...   \n",
       "\n",
       "         traffic_volume_counted_after_1500_to_1600  \\\n",
       "7052181                                       1790   \n",
       "1046231                                        738   \n",
       "5029291                                       1112   \n",
       "4964924                                        173   \n",
       "3023772                                        359   \n",
       "\n",
       "         traffic_volume_counted_after_1600_to_1700  \\\n",
       "7052181                                       1858   \n",
       "1046231                                        702   \n",
       "5029291                                       1164   \n",
       "4964924                                        171   \n",
       "3023772                                        338   \n",
       "\n",
       "         traffic_volume_counted_after_1700_to_1800  \\\n",
       "7052181                                       1724   \n",
       "1046231                                        659   \n",
       "5029291                                       1189   \n",
       "4964924                                        173   \n",
       "3023772                                        338   \n",
       "\n",
       "         traffic_volume_counted_after_1800_to_1900  \\\n",
       "7052181                                       1611   \n",
       "1046231                                        601   \n",
       "5029291                                       1018   \n",
       "4964924                                        190   \n",
       "3023772                                        224   \n",
       "\n",
       "         traffic_volume_counted_after_1900_to_2000  \\\n",
       "7052181                                       1416   \n",
       "1046231                                        548   \n",
       "5029291                                        983   \n",
       "4964924                                        171   \n",
       "3023772                                        166   \n",
       "\n",
       "         traffic_volume_counted_after_2000_to_2100  \\\n",
       "7052181                                       1192   \n",
       "1046231                                        440   \n",
       "5029291                                        807   \n",
       "4964924                                        148   \n",
       "3023772                                        140   \n",
       "\n",
       "         traffic_volume_counted_after_2100_to_2200  \\\n",
       "7052181                                       1147   \n",
       "1046231                                        356   \n",
       "5029291                                        693   \n",
       "4964924                                         91   \n",
       "3023772                                         93   \n",
       "\n",
       "         traffic_volume_counted_after_2200_to_2300  \\\n",
       "7052181                                        960   \n",
       "1046231                                        324   \n",
       "5029291                                        556   \n",
       "4964924                                         81   \n",
       "3023772                                         57   \n",
       "\n",
       "         traffic_volume_counted_after_2300_to_2400  year_of_data  \n",
       "7052181                                        826            15  \n",
       "1046231                                        233            15  \n",
       "5029291                                        429            15  \n",
       "4964924                                         61            15  \n",
       "3023772                                         53            15  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Xtrain.info()\n",
    "# Xtrain.loc[Xtrain.station_id =='LC0200', ]\n",
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the output using our trained model\n",
    "Output = clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy Score :  69.72 %\n"
     ]
    }
   ],
   "source": [
    "Output\n",
    "accuracy_train = round(clf.score(Xtest, ytest)*100,2)\n",
    "\n",
    "print(\"Decision Tree Accuracy Score : \", round(accuracy_train,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_squared_error(ytest, Output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
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      "Collecting tensorflow-io-gcs-filesystem>=0.21.0\n",
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      "Collecting astunparse>=1.6.0\n",
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      "Collecting h5py>=2.9.0\n",
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      "Collecting keras<2.8,>=2.7.0rc0\n",
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      "Collecting flatbuffers<3.0,>=1.12\n",
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      "Requirement already satisfied: numpy>=1.14.5 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from tensorflow) (1.21.1)\n",
      "Collecting opt-einsum>=2.3.2\n",
      "  Downloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
      "Collecting google-pasta>=0.1.1\n",
      "  Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
      "Collecting libclang>=9.0.1\n",
      "  Downloading libclang-12.0.0-py2.py3-none-win_amd64.whl (13.1 MB)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from tensorboard~=2.6->tensorflow) (2.25.1)\n",
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      "  Downloading tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)\n",
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      "Collecting google-auth-oauthlib<0.5,>=0.4.1\n",
      "  Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)\n",
      "Collecting google-auth<3,>=1.6.3\n",
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      "  Downloading tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)\n",
      "Collecting markdown>=2.6.8\n",
      "  Downloading Markdown-3.3.4-py3-none-any.whl (97 kB)\n",
      "Collecting werkzeug>=0.11.15\n",
      "  Downloading Werkzeug-2.0.2-py3-none-any.whl (288 kB)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.2.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (0.2.8)\n",
      "Collecting requests-oauthlib>=0.7.0\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (1.26.6)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (4.0.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2021.5.30)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\solai\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2.10)\n",
      "Collecting oauthlib>=3.0.0\n",
      "  Downloading oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)\n",
      "Building wheels for collected packages: termcolor\n",
      "  Building wheel for termcolor (setup.py): started\n",
      "  Building wheel for termcolor (setup.py): finished with status 'done'\n",
      "  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4829 sha256=7775be57d3fc93ecb4a39edb216587628c27994890e0fbe7496288625b51fcd4\n",
      "  Stored in directory: c:\\users\\solai\\appdata\\local\\pip\\cache\\wheels\\a0\\16\\9c\\5473df82468f958445479c59e784896fa24f4a5fc024b0f501\n",
      "Successfully built termcolor\n",
      "Installing collected packages: oauthlib, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, markdown, grpcio, google-auth-oauthlib, wrapt, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard, opt-einsum, libclang, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, astunparse, tensorflow\n",
      "  Attempting uninstall: google-auth\n",
      "    Found existing installation: google-auth 1.4.2\n",
      "    Uninstalling google-auth-1.4.2:\n",
      "      Successfully uninstalled google-auth-1.4.2\n",
      "Successfully installed astunparse-1.6.3 flatbuffers-2.0 gast-0.4.0 google-auth-2.3.3 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.41.1 h5py-3.5.0 keras-2.7.0 keras-preprocessing-1.1.2 libclang-12.0.0 markdown-3.3.4 oauthlib-3.1.1 opt-einsum-3.3.0 requests-oauthlib-1.3.0 tensorboard-2.7.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.0 tensorflow-2.7.0 tensorflow-estimator-2.7.0 tensorflow-io-gcs-filesystem-0.21.0 termcolor-1.1.0 typing-extensions-3.10.0.2 werkzeug-2.0.2 wrapt-1.13.3\n"
     ]
    }
   ],
   "source": [
    "# !pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose a number of time steps\n",
    "n_steps = 30\n",
    "n_features = 1\n",
    "# define model\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "# model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_5 (Conv1D)           (None, 29, 64)            192       \n",
      "                                                                 \n",
      " max_pooling1d_5 (MaxPooling  (None, 14, 64)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " flatten_6 (Flatten)         (None, 896)               0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 50)                44850     \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 45,093\n",
      "Trainable params: 45,093\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Xtrain[0:10000].values.reshape(Xtrain[0:10000].values.shape[0], Xtrain[0:10000].values.shape[1], n_features)\n",
    "# X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "313/313 [==============================] - 4s 4ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 2/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 3/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 4/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 5/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 6/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 7/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 8/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 9/10\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 1.5532e-07 - accuracy: 0.2370\n",
      "Epoch 10/10\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 1.5532e-07 - accuracy: 0.2370: 0s - loss: 1.5443e-07 - \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16e1ca68910>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model\n",
    "model.fit(X, ytrain_t[0:10000], epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# demonstrate prediction\n",
    "# x_input = Xtest[0:1000].values.reshape((1, n_steps, n_features))\n",
    "X_input = Xtest[0:1000].values.reshape(Xtest[0:1000].values.shape[0], Xtest[0:1000].values.shape[1], n_features)\n",
    "\n",
    "yhat = model.predict(X_input, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat.shape\n",
    "# yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, accuracy_score, confusion_matrix\n",
    "# mean_squared_error(ytest[0:1000], yhat)\n",
    "# accuracy_score(ytest[0:1000], yhat)\n",
    "cnn_score = accuracy_score(ytest[0:1000], np.round(abs(yhat)), normalize=False)*10\n",
    "# confusion_matrix(ytest[0:1000], yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Accuracy Score:  80 %\n"
     ]
    }
   ],
   "source": [
    "print(\"CNN Accuracy Score: \", cnn_score, \"%\")"
   ]
  }
 ],
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